Wikipedia Handbook of Biomedical Informatics
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Contents Articles General Overview
1
Health information technology
1
Health informatics
6
Clinical Informatics
18
Cybermedicine
29
EHealth
34
Health 2.0
39
Public health informatics
44
Applications in Healthcare Management
47
Health Administration Informatics
47
Medical integration environment
49
Health information exchange
49
Hospital information system
52
Healthcare workflow
55
Computer physician order entry
55
ICU quality and management tools
59
Laboratory information management system
61
Laboratory information system
67
MHealth
73
Practice management software
83
Clinical Quality Management System
83
Health Electronic Records
84
Electronic health record
84
Electronic medical record
107
Personal health record
131
COmputer STored Ambulatory Record
145
ProRec
146
Health record trust
146
ClearHealth
148
Laika
149
OpenEHR
151
OpenEMR
154
OpenMRS
157
Kind Messages for Electronic Healthcare Record
159
Summarized Electronic Health Record
160
VistA
162
VistA imaging
174
VistA Web
176
WorldVistA
178
ZEPRS
181
Decision Support Applications
185
Clinical decision support system
185
Computer-aided diagnosis
194
Medical algorithm
198
Medical logic module
200
Arden syntax
200
Concept Processing
205
Guideline execution engine
207
CADUCEUS
208
DXplain
209
Internist-I
210
Mycin
212
Physicians' Information and Education Resource
214
RetroGuide
215
STD Wizard
218
Medical Imaging Applications
229
Digital radiography
229
Imaging informatics
232
Patient registration
234
Radiology information system
237
Picture archiving and communication system
238
Analysis of Functional NeuroImages
245
3DSlicer
246
Analyze
250
CARET
251
CAVEman
252
FreeSurfer
253
ImageJ
255
InVesalius
258
ITK-SNAP
260
Mango
262
OsiriX
263
Medical and biological signal applications
265
Medical monitor
265
Holter monitor
271
Automated ECG interpretation
274
MECIF Protocol
276
SCP-ECG
277
European Data Format
277
OpenXDF
279
Databases, Digital Libraries and Literature Retrieval
281
Biological database
281
Medical literature retrieval
283
MEDLINE
284
Entrez
287
ETBLAST
289
PMID
291
PubMed
295
GoPubMed
299
Pubget
300
PubMed Central
302
UK PubMed Central
305
Trip
307
Twease
309
SciELO
310
Telehealth and Telemedicine
311
Connected Health
311
Telehealth
315
Telemedicine
320
Telecare
331
Telephone triage
333
Remote guidance
335
Tele-epidemiology
336
Telenursing
338
Teledermatology
340
Telemental Health
343
Telepsychiatry
344
Teleradiology
346
Telerehabilitation
348
Virtual reality in telerehabilitation
353
Wireless Medical Telemetry Service
354
Computer-Aided Surgery, Medical Robotics and Virtual Reality
356
Computer assisted surgery
356
Remote surgery
360
Robotic surgery
363
Surgical Segment Navigator
372
Bone segment navigation
373
Robotic prostatectomy
375
Robot-assisted heart surgery
383
Cyberknife
391
Da Vinci Surgical System
397
NeuroArm
400
Laboratory Unit for Computer Assisted Surgery
401
Legislation and Regulation
403
Health Insurance Portability and Accountability Act
403
Certification Commission for Healthcare Information Technology
413
Software Systems
416
Medical software
416
Dental software
418
List of freeware health software
420
List of open source healthcare software
421
List of neuroimaging software
424
Mirth
427
Mpro
428
Open Dental
429
Personal Health Application
433
Texas Medication Algorithm Project
434
Languages and Development Platforms MUMPS
Internet Projects
436 436 446
Bing Health
446
Dossia
448
E-Patient
450
Google Health
452
IMedicor
455
Microsoft Amalga
456
Microsoft HealthVault
458
Patient portal
460
Virtual patient
462
Clinical Research Informatics
465
Translational research informatics
465
Clinical data management system
466
Case report form
468
Clinical coder
469
Clinical data acquisition
471
Data clarification form
473
Patient-reported outcome
473
Standards, Coding and Nomenclature
477
Diagnosis codes
477
Procedure codes
480
Bar Code Medication Administration
481
Bidirectional Health Information Exchange
482
Classification Commune des Actes Médicaux
484
Classification of Pharmaco-Therapeutic Referrals
487
Clinical Context Object Workgroup
492
Clinical Data Interchange Standards Consortium
493
Clinical Document Architecture
495
Continuity of Care Document
496
Clinical Document Architecture
498
Continuity of Care Record
499
COSTART
501
Current Procedural Terminology
502
Diagnosis-related group
506
Digital Imaging and Communications in Medicine
510
DOCLE
519
Electronic Common Technical Document
520
EUDRANET
523
General Data Format for Biomedical Signals
523
Health Level 7
524
Healthcare Common Procedure Coding System
534
Healthcare Information Technology Standards Panel
535
Health Informatics Service Architecture
536
Healthcare Services Specification Project
538
International Statistical Classification of Diseases and Related Health Problems
541
ICD-10
547
ICD-10 Procedure Coding System
551
International Classification of Diseases for Oncology
557
International Classification of Functioning, Disability and Health
568
International Classification of Health Interventions
572
International Classification of Primary Care
573
International Healthcare Terminology Standards Development Organisation
575
ICPC-2 PLUS
578
ISO 27799
579
ISO/IEEE 11073
580
ISO/TC 215
587
LOINC
590
MEDCIN
592
MedDRA
595
Medical Subject Headings
597
Minimum Data Set
599
Multiscale Electrophysiology Format
600
NANDA
601
Nursing Interventions Classification
603
Nursing Minimum Data Set
603
Nursing Outcomes Classification
604
OpenGALEN
604
SNOMED
606
SNOMED CT
609
Standard for Exchange of Non-clinical Data
616
TC 251
617
WHOART
Healthcare Ontologies
618 619
Nosology
619
Archetype
620
OBO Foundry
621
Ontology for Biomedical Investigations
624
Open Biomedical Ontologies
625
TIME-ITEM
628
Associations, Committees and Conferences
630
American Association for Medical Systems and Informatics
630
American College of Medical Informatics
631
American Health Information Management Association
632
American Telemedicine Association
634
Belgian Health Telematics Commission
635
Brazilian Society of Health Informatics
636
Brazilian Congress on Health Informatics
639
Center for Telehealth and E-Health Law
642
European Federation for Medical Informatics
643
European Health Telematics Association
644
European Health Telematics Observatory
645
European Institute for Health Records
646
Health On the Net Foundation
649
HISA
652
Indian Association for Medical Informatics
654
International Medical Informatics Association
656
Medinfo
660
National Resource Center for Health Information Technology
662
Open Source Health Care Alliance
663
The Continua Health Alliance
664
UNESCO Chair in Telemedicine
667
World Health Imaging, Telemedicine, and Informatics Alliance
669
Publications
671
List of medical and health informatics journals
671
Journal of Information Professionals in Health
672
Journal of Medical Internet Research
673
National Projects
674
BeHealth
674
Canada Health Infoway
674
Distance Learning and Telemedicine Grant and Loan Program
676
EHealth Ontario
677
HealthConnect
678
Health Information Systems Programme
679
District Health Information System
680
FLOW
681
Health informatics in China
682
NHS Direct
692
NHS National Programme for IT
695
Ontario Telemedicine Network
703
Public Health Information Network
704
SAPPHIRE
708
SmartCare
709
International Projects
711
Building Europe-Africa Collaborative Network for Applying IST in Health Care Sector
711
Global Infectious Disease Epidemiology Network
712
Integrating the Healthcare Enterprise
714
Miscellanea
716
GIS and Public Health
716
Neuroinformatics
719
Healthcare Effectiveness Data and Information Set
728
E-epidemiology
732
Epi Info
734
OpenEpi
739
Living Human Project
741
Stereolithography
743
Virtual Physiological Human
744
Visible Human Project
751
People
754
Edward H. Shortliffe
754
Don E. Detmer
758
Homer R. Warner
761
Robert Ledley
765
Vimla L. Patel
778
References Article Sources and Contributors
781
Image Sources, Licenses and Contributors
794
Article Licenses License
797
1
General Overview Health information technology Health information technology (HIT) provides the umbrella framework to describe the comprehensive management of health information across computerized systems and its secure exchange between consumers, providers, government and quality entities, and insurers. Health information technology (HIT) is in general increasingly viewed as the most promising tool for improving the overall quality, safety and efficiency of the health delivery system (Chaudhry et al., 2006). Broad and consistent utilization of HIT will: • • • • •
Improve health care quality or effectiveness; Increase health care productivity or efficiency; Prevent medical errors and increase health care accuracy and procedural correctness; Reduce health care costs; Increase administrative efficiencies and healthcare work processes;
• Decrease paperwork and unproductive or idle work time; • Extend real-time communications of health informatics among health care professionals; and • Expand access to affordable care. Risk-based regulatory framework for health IT September 4, 2013 the Health IT Policy Committee (HITPC) accepted and approved recommendations from the Food and Drug Administration Safety and Innovation Act (FDASIA) working group for a risk-based regulatory framework for health information technology[1]. The Food and Drug Administration (FDA), the Office of the National Coordinator for Health IT (ONC), and Federal Communications Commission (FCC) kicked off the FDASIA workgroup of the HITPC to provide stakeholder input into a report on a risk-based regulatory framework that promotes safety and innovation and reduces regulatory duplication, consistent with section 618 of FDASIA. This provision permitted the Secretary of Health and Human Services (HHS) to form a workgroup in order to obtain broad stakeholder input from across the health care, IT, patients and innovation spectrum. The FDA, ONC, and FCC actively participated in these discussions with stakeholders from across the health care, IT, patients and innovation spectrum. HIMSS Good Informatics Practices-GIP is aligned with FDA risk-based regulatory framework for health information technology. [2] GIP development began in 2004 developing risk-based IT technical guidance. [3] Today the GIP peer-review and published modules are an excellent tool for educating Health IT professionals[4] Interoperable HIT will improve individual patient care, but it will also bring many public health benefits including: • Early detection of infectious disease outbreaks around the country; • Improved tracking of chronic disease management; and • Evaluation of health care based on value enabled by the collection of de-identified price and quality information that can be compared.
Concepts and Definitions Health information technology (HIT) is “the application of information processing involving both computer hardware and software that deals with the storage, retrieval, sharing, and use of health care information, data, and knowledge for communication and decision making” (Brailer, & Thompson, 2004). Technology is a broad concept that deals with a species' usage and knowledge of tools and crafts, and how it affects a species' ability to control and adapt to its environment. However, a strict definition is elusive; "technology" can refer to material objects of use to humanity, such as machines, hardware or utensils, but can also encompass broader themes, including systems,
Health information technology methods of organization, and techniques. For HIT, technology represents computers and communications attributes that can be networked to build systems for moving health information. Informatics is yet another integral aspect of HIT. Informatics refers to the science of information, the practice of information processing, and the engineering of information systems. Informatics underlies the academic investigation and practitioner application of computing and communications technology to healthcare, health education, and biomedical research. Health informatics refers to the intersection of information science, computer science, and health care. Health informatics describes the use and sharing of information within the healthcare industry with contributions from computer science, mathematics, and psychology. It deals with the resources, devices, and methods required for optimizing the acquisition, storage, retrieval, and use of information in health and biomedicine. Health informatics tools include not only computers but also clinical guidelines, formal medical terminologies, and information and communication systems. Medical informatics, nursing informatics, public health informatics, and pharmacy informatics are subdisciplines that inform health informatics from different disciplinary perspectives. The processes and people of concern or study are the main variables.
Implementation of HIT The Institute of Medicine’s (2001) call for the use of electronic prescribing systems in all healthcare organizations by 2010 heightened the urgency to accelerate United States hospitals’ adoption of CPOE systems. In 2004, President Bush signed an Executive Order titled the President’s Health Information Technology Plan, which established a ten-year plan to develop and implement electronic medical record systems across the US to improve the efficiency and safety of care. According to a study by RAND Health, the US healthcare system could save more than $81 billion annually, reduce adverse healthcare events and improve the quality of care if it were to widely adopt health information technology.[5] The American Recovery and Reinvestment Act, signed into law in 2009 under the Obama Administration, has provided approximately $19 billion in incentives for hospitals to shift from paper to electronic medical records. The American Recovery and Reinvestment Act has set aside $2 billion which will go towards programs developed by the National Coordinator and Secretary to help healthcare providers implement HIT and provide technical assistance through various regional centers. The other $17 billion in incentives comes from Medicare and Medicaid funding for those who adopt HIT before 2015. Healthcare providers who implement electronic records can receive up to $44,000 over four years in Medicare funding and $63,750 over six years in Medicaid funding. The sooner that healthcare providers adopt the system, the more funding they receive. Those who do not adopt electronic health record systems before 2015 do not receive any federal funding. While electronic health records have potentially many advantages in terms of providing efficient and safe care, recent reports have brought to light some challenges with implementing electronic health records. The most immediate barriers for widespread adoption of this technology have been the high initial cost of implementing the new technology and the time required for doctors to train and adapt to the new system. There have also been suspected cases of fraudulent billing, where hospitals inflate their billings to Medicare. Given that healthcare providers have not reached the deadline (2015) for adopting electronic health records, it is unclear what effects this policy will have long term.
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Health information technology
Types of technology In a recent study about the adoption of technology in the United States, Furukawa, and colleagues (2008) classified applications for prescribing to include electronic medical records (EMR), clinical decision support (CDS), and computerized physician order entry (CPOE). They further defined applications for dispensing to include bar-coding at medication dispensing (BarD), robot for medication dispensing (ROBOT), and automated dispensing machines (ADM). And, they defined applications for administration to include electronic medication administration records (EMAR) and bar-coding at medication administration (BarA).
Electronic Health Record (EHR) Although frequently cited in the literature the Electronic health record (EHR), previously known as the Electronic medical record (EMR), there is no consensus about the definition (Jha et al., 2008). However, there is consensus that EMRs can reduce several types of errors, including those related to prescription drugs, to preventive care, and to tests and procedures.[6] Recurring alerts remind clinicians of intervals for preventive care and track referrals and test results. Clinical guidelines for disease management have a demonstrated benefit when accessible within the electronic record during the process of treating the patient. Advances in health informatics and widespread adoption of interoperable electronic health records US medical groups' adoption of EHR (2005) promise access to a patient's records at any health care site. A 2005 report noted that medical practices in the United States are encountering barriers to adopting an EHR system, such as training, costs and complexity, but the adoption rate continues to rise (see chart to right). Since 2002, the National Health Service of the United Kingdom has placed emphasis on introducing computers into healthcare. As of 2005, one of the largest projects for a national EHR is by the National Health Service (NHS) in the United Kingdom. The goal of the NHS is to have 60,000,000 patients with a centralized electronic health record by 2010. The plan involves a gradual roll-out commencing May 2006, providing general practices in England access to the National Programme for IT (NPfIT), the NHS component of which is known as the "Connecting for Health Programme".[7] However, recent surveys have shown physicians' deficiencies in understanding the patient safety features of the NPfIT-approved software.
Clinical point of care technology Computerized Provider (Physician) Order Entry (CPOE) Prescribing errors are the largest identified source of preventable errors in hospitals. A 2006 report by the Institute of Medicine estimated that a hospitalized patient is exposed to a medication error each day of his or her stay. Computerized provider order entry (CPOE), formerly called Computer physician order entry, can reduce total medication error rates by 80%, and adverse (serious with harm to patient) errors by 55%. A 2004 survey by Leapfrog [8] found that 16% of US clinics, hospitals and medical practices are expected to be utilizing CPOE within 2 years. In addition to electronic prescribing, a standardized bar code system for dispensing drugs could prevent a quarter of drug errors. Consumer information about the risks of the drugs and improved drug packaging (clear labels, avoiding similar drug names and dosage reminders) are other error-proofing measures. Despite ample evidence of the potential to reduce medication errors, competing systems of barcoding and electronic prescribing have slowed
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Health information technology adoption of this technology by doctors and hospitals in the United States, due to concern with interoperability and compliance with future national standards. Such concerns are not inconsequential; standards for electronic prescribing for Medicare Part D conflict with regulations in many US states. And, aside from regulatory concerns, for the small-practice physician, utilizing CPOE requires a major change in practice work flow and an additional investment of time. Many physicians are not full-time hospital staff; entering orders for their hospitalized patients means taking time away from scheduled patients.[9]
Technological Innovations, Opportunities, and Challenges Handwritten reports or notes, manual order entry, non-standard abbreviations and poor legibility lead to substantial errors and injuries, according to the Institute of Medicine (2000) report. The follow-up IOM (2004) report, Crossing the quality chasm: A new health system for the 21st century, advised rapid adoption of electronic patient records, electronic medication ordering, with computer- and internet-based information systems to support clinical decisions. However, many system implementations have experienced costly failures (Ammenwerth et al., 2006). Furthermore, there is evidence that CPOE may actually contribute to some types of adverse events and other medical errors.(Campbell et al., 2007) For example, the period immediately following CPOE implementation resulted in significant increases in reported adverse drug events in at least one study (Bradley, Steltenkamp, & Hite, 2006) and evidence of other errors have been reported.(Bates, 2005a; Bates, Leape, Cullen, & Laird, 1998; Bates; 2005b) Collectively, these reported adverse events describe phenomena related to the disruption of the complex adaptive system resulting from poorly implemented or inadequately planned technological innovation.
Technological Iatrogenesis Technology may introduce new sources of error Technologically induced errors are significant and increasingly more evident in care delivery systems. Terms to describe this new area of error production include the label technological iatrogenesis for the process and e-iatrogenic for the individual error. The sources for these errors include: • Prescriber and staff inexperience may lead to a false sense of security; that when technology suggests a course of action, errors are avoided. • Shortcut or default selections can override non-standard medication regimens for elderly or underweight patients, resulting in toxic doses. • CPOE and automated drug dispensing was identified as a cause of error by 84% of over 500 health care facilities participating in a surveillance system by the United States Pharmacopoeia. • Irrelevant or frequent warnings can interrupt work flow. Healthcare information technology can also result in iatrogenesis if design and engineering are substandard, as illustrated in a 14-part detailed analysis done at the University of Sydney.[10]
References [1] [2] [3] [4] [5]
http:/ / www. healthit. gov/ buzz-blog/ hit-policy-committee/ path-riskbased-regulatory-framework-health/ http:/ / www. himss. org/ files/ HIMSSorg/ content/ files/ LSITOverviewCh1forwebsite270111. pdf http:/ / www. scivee. tv/ node/ 12332 http:/ / ebooks. himss. org/ catalog/ show/ good+ informatics+ practices/ 9 RAND Healthcare: Health Information Technology: Can HIT Lower Costs and Improve Quality? (http:/ / www. rand. org/ pubs/ research_briefs/ RB9136/ index1. html) Retrieved on July 8, 2006 [6] American College of Physicians Observer: How EMR software can help prevent medical mistakes (http:/ / www. acponline. org/ journals/ news/ sep04/ emr. htm) by Jerome H. Carter (September 2004) [7] NHS Connecting for Health: Delivering the National Programme for IT (http:/ / www. connectingforhealth. nhs. uk/ delivery/ ) Retrieved August 4, 2006 [8] http:/ / leapfroggroup. org/
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Health information technology [9] "Computerized Physician Order Entry: Coming to a Hospital Near You" (http:/ / www. physicianspractice. com/ blog/ content/ article/ 1462168/ 2052940) J. Scott Litton, Physicians Practice, March 2012. [10] A Study of An Enterprise Health Information System (http:/ / sydney. edu. au/ engineering/ it/ ~hitru/ index. php?option=com_content& task=view& id=91& Itemid=146)
Further reading • Ammenwerth, E., Talmon, J., Ash, J. S., Bates, D. W., Beuscart-Zephir, M. C., Duhamel, A., Elkin, P. L., Gardner, R. M., & Geissbuhler, A. (2006). Impact of CPOE on mortality rates – contradictory findings, important messages.” Methods Inf Med, 45(6): 586-593. • Ash, J. S., Sittig, D. F., Poon, E. G., Guappone, K., Campbell, E., & Dykstra, R. H. (2007). The extent and importance of unintended consequences related to computerized provider order entry.” Journal of the American Medical Informatics Association, 14(4): 415-423. • Bates, D. (2005a). Computerized Physician Order entry and medication errors: finding a balance. Journal of Biomedical Informatics, 38(4): 250-261. • Bates, D.W. (2005b). Physicians and ambulatory electronic health records. Health Affairs, 24(5): 1180-1189. • Bates, D. W., Leape, L. L., Cullen, D. J., & Laird, N. (1998). Effect of computerized physician order entry and a team intervention on prevention of serious medical errors. Journal of the American Medical Association, 280: 1311-1316. • Bradley, V. M., Steltenkamp, C. L., & Hite, K. B. (2006). Evaluation of reported medication errors before and after implementation of computerized practitioner order entry. Journal Healthc Inf Manag, 20(4): 46-53. • Brailer, D., & Thompson, T. (2004). Health IT strategic framework. Washington, DC: Department of Health and Human Services. • Chaudhry, B. Wang, J., & Wu, S. et al., (2006). Systematic review: Impact of health information technology on quality, efficiency, and costs of medical care, Annals of Internal Medicine, 144(10), 742–752. • Campbell, E. M., Sittig, D. F., Ash, J. S., Guappone, K. P., & Dykstra, R. H. (2007). In reply to: “e-Iatrogenesis: The most critical consequence of CPOE and other HIT. Journal of the American Medical Informatics Association. • Edmunds M, Peddicord D, Detmer DE, Shortliffe E. Health IT Policy and Politics: A Primer on Moving Policy Into Action. Featured Session, American Medical Informatics Association Annual Symposium (2009). Available as a webinar at https://www.amia.org/amia-policy-101. • Furukawa, M. F., Raghu, T. S., Spaulding, T. J., & Vinze, A. (2008). Health Affairs, 27, (3), 865-875. • Institute of Medicine (2001). Crossing the quality chasm: A new health system for the 21st century. Washington, D.C: National Academies Press. • Jha, A. K., Doolan, D., Grandt, D., Scott, T. & Bates, D. W. (2008). The use of health information technology in seven nations. International Journal of Medical Informatics, corrected proof in-press. • Kawamoto, K. H., Caitlin, A., Balas, E. A., & Lobach, D. F. (2005). “Improving clinical practice using clinical decision support systems: A systematic review of trials to identify features critical to success.” British Journal of Medicine, 330(7494): 765-774. • Sidrov, J. (2006). It ain’t necessarily so: The electronic health record and the unlikely prospect of reducing healthcare costs. Health Affairs, 25(4): 1079-1085.
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Health information technology
External links • Health Resources and Services Administration (HRSA) (http://www.hrsa.gov/healthit/) • Health Information Technology (http://www.hhs.gov/healthit/) at US Department of Health & Human Services • Healthcare Information Technology (http://www.ansi.org/standards_activities/standards_boards_panels/hisb/ hitsp.aspx?menuid=3) from American National Standards Institute (ANSI) • Certification Commission for Healthcare Information Technology (CCHIT) (http://www.cchit.org/) • Health Information Technology Videos (http://www.ehrtv.com/) • Health IT Discussion Forum (http://www.healthinformaticsforum.com/) • Hospital Management Information System (http://www.cdacnoida.in/HIS/index.asp) from Center for Development of Advanced Computing (C-DAC) (http://www.cdacnoida.in/) • American Society of Health Informatics Managers (http://www.ashim.org/) • Patient Safety Initiatives in India Using Health IT (http://thepatient.in/safety/) • Health Information Technology Certification Programs (http://www.chcp.edu/online-programs/ health-information-technology) • Health Information Technology Careers (http://www.healthinformationtechnologycareers.com/)
Health informatics Health informatics (also called Health Information Systems, health care informatics, healthcare informatics, medical informatics, nursing informatics, clinical informatics, or biomedical informatics) is a discipline at the intersection of information science, computer science, and health care. It deals with the resources, devices, and methods required to optimize the acquisition, storage, retrieval, and use of information in health and biomedicine. Health informatics Electronic patient chart from a health information tools include computers, clinical guidelines, formal medical system terminologies, and information and communication systems. It is applied to the areas of nursing, clinical care, dentistry, pharmacy, public health, occupational therapy, physical therapy and (bio)medical research. • The international standards on the subject are covered by ICS 35.240.80 in which ISO 27799:2008 is one of the core components. • Molecular bioinformatics and clinical informatics have converged into the field of translational bioinformatics.
Clinical Informatics Clinical Informatics is concerned with the use of information in health care by clinicians. Clinical informaticians transform health care by analyzing, designing, implementing, and evaluating information and communication systems that enhance individual and population health outcomes, improve [patient] care, and strengthen the clinician-patient relationship. Clinical informaticians use their knowledge of patient care combined with their understanding of informatics concepts, methods, and health informatics tools to: • assess information and knowledge needs of health care professionals and patients, • characterize, evaluate, and refine clinical processes, • develop, implement, and refine clinical decision support systems, and
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Health informatics • lead or participate in the procurement, customization, development, implementation, management, evaluation, and continuous improvement of clinical information systems. Clinicians collaborate with other health care and information technology professionals to develop health informatics tools which promote patient care that is safe, efficient, effective, timely, patient-centered, and equitable. In October 2011 American Board of Medical Specialties (ABMS), the organization overseeing the certification of physician specialists in the United States, announces the creation of physician certification in Clinical Informatics. The first examination for board certification in the subspecialty of Clinical Informatics will be offered in October 2013 by American Board of Preventive Medicine.
Integrated data repository Development of the field of clinical informatics lead to creation of large data sets with electronic health record data integrated with other data (such as genomic data). Large data warehouses are often described as clinical data warehouses (also known as clinical data repositories). In research, deidentified CDWs can be used by researchers with less complex ethical oversight. CDWs with data of deceased patients were also suggested as a research resource that does not require IRB approval.
Translational bioinformatics With the completion of the human genome and the recent advent of high throughput sequencing and genome-wide association studies of single nucleotide polymorphisms, the fields of molecular bioinformatics, biostatistics, statistical genetics and clinical informatics are converging into the emerging field of translational bioinformatics. The relationship between bioinformatics and health informatics, while conceptually related under the umbrella of biomedical informatics, has not always been very clear. The TBI community is specifically motivated with the development of approaches to identify linkages between fundamental biological and clinical information. Along with complementary areas of emphasis, such as those focused on developing systems and approaches within clinical research contexts, insights from TBI may enable a new paradigm for the study and treatment of disease.
Clinical Research Informatics Clinical Research Informatics (or, CRI) takes the core foundations, principles, and technologies related to Health Informatics, and applies these to clinical research contexts. As such, CRI is a sub-discipline of sorts of Health Informatics, and interest and activities in CRI have increased greatly in recent years given the overwhelming problems associated with the explosive growth of clinical research data and information.[1] There are a number of activities within clinical research that CRI supports, including: • • • • • •
more efficient and effective data collection and acquisition optimal protocol design and efficient management patient recruitment and management adverse event reporting regulatory compliance data storage, transfer, processing and analysis
Computational Health Informatics Computational health informatics is a branch of Computer Science that deals specifically with computational techniques that are relevant in healthcare. Computational health informatics is also a branch of Health Informatics, but is orthogonal to much of the work going on in health informatics because computer scientist's interest is mainly in understanding fundamental properties of computation. Health informatics, on the other hand, is primarily concerned with understanding fundamental properties of medicine that allow for the intervention of computers. The
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Health informatics health domain provides an extremely wide variety of problems that can be tackled using computational techniques, and computer scientists are attempting to make a difference in medicine by studying the underlying principles of computer science that will allow for meaningful (to medicine) algorithms and systems to be developed. Thus, computer scientists working in computational health informatics and health scientists working in medical health informatics combine to develop the next generation of healthcare technologies. Using computers to analyze health data has been around since the 1950s, but it wasn't until the 1990s that the first sturdy models appeared. The development of the internet has helped develop computational health informatics over the past decade. Computer models are used to examine various topics such as how exercise affects obesity, healthcare costs, and many more. Examples of projects in computational health informatics include the COACH project.
Medical informatics in the United States Even though the idea of using computers in medicine emerged as technology advanced in the early 20th century, it was not until the 1950s that informatics began to have an effect in the United States. The earliest use of electronic digital computers for medicine was for dental projects in the 1950s at the United States National Bureau of Standards by Robert Ledley. During the mid-1950s, the United States Air Force (USAF) carried out several medical projects on its computers while also encouraging civilian agencies such as the National Academy of Sciences - National Research Council (NAS-NRC) and the National Institutes of Health (NIH) to sponsor such work. In 1959, Ledley and Lee B. Lusted published “Reasoning Foundations of Medical Diagnosis,” a widely-read article in Science, which introduced computing (especially operations research) techniques to medical workers. Ledley and Lusted’s article has remained influential for decades, especially within the field of medical decision making. Guided by Ledley's late 1950s survey of computer use in biology and medicine (carried out for the NAS-NRC), and by his and Lusted's articles, the NIH undertook the first major effort to introduce computers to biology and medicine. This effort, carried out initially by the NIH's Advisory Committee on Computers in Research (ACCR), chaired by Lusted, spent over $40 million between 1960 and 1964 in order to establish dozens of large and small biomedical research centers in the US. One early (1960, non-ACCR) use of computers was to help quantify normal human movement, as a precursor to scientifically measuring deviations from normal, and design of prostheses. The use of computers (IBM 650, 1620, and 7040) allowed analysis of a large sample size, and of more measurements and subgroups than had been previously practical with mechanical calculators, thus allowing an objective understanding of how human locomotion varies by age and body characteristics. A study co-author was Dean of the Marquette University College of Engineering; this work led to discrete Biomedical Engineering departments there and elsewhere. The next steps, in the mid-1960s, were the development (sponsored largely by the NIH) of expert systems such as MYCIN and Internist-I. In 1965, the National Library of Medicine started to use MEDLINE and MEDLARS. Around this time, Neil Pappalardo, Curtis Marble, and Robert Greenes developed MUMPS (Massachusetts General Hospital Utility Multi-Programming System) in Octo Barnett's Laboratory of Computer Science [2] at Massachusetts General Hospital in Boston, another center of biomedical computing that received significant support from the NIH. In the 1970s and 1980s it was the most commonly used programming language for clinical applications. The MUMPS operating system was used to support MUMPS language specifications. As of 2004[3], a descendent of this system is being used in the United States Veterans Affairs hospital system. The VA has the largest enterprise-wide health information system that includes an electronic medical record, known as the Veterans Health Information Systems and Technology Architecture (VistA). A graphical user interface known as the Computerized Patient Record System (CPRS) allows health care providers to review and update a patient’s electronic medical record at any of the VA's over 1,000 health care facilities.
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Health informatics During the 1960s, Morris Collen, a physician working for Kaiser Permanente's Division of Research, developed computerized systems to automate many aspects of multiphasic health checkups. These system became the basis the larger medical databases Kaiser Permanente developed during the 1970s and 1980s. The American College of Medical Informatics (ACMI) has since 1993 annually bestowed the Morris F. Collen, MD Medal for Outstanding Contributions to the Field of Medical Informatics. In the 1970s a growing number of commercial vendors began to market practice management and electronic medical records systems. Although many products exist, only a small number of health practitioners use fully featured electronic health care records systems. Homer R. Warner, one of the fathers of medical informatics, founded the Department of Medical Informatics at the University of Utah in 1968. The American Medical Informatics Association (AMIA) has an award named after him on application of informatics to medicine.
Informatics Certifications Like other IT training specialties, there are Informatics certifications available to help informatics professionals stand out and be recognized. The American Nurses Credentialing Center (ANCC) offers a board certification in Nursing Informatics, the Radiology Informatics, the CIIP (Certified Imaging Informatics Professional) certification was created by ABII (The American Board of Imaging Informatics) which is sponsored by SIIM (the Society for Imaging Informatics in Medicine) in 2005. The CIIP certification requires documented experience working in Imaging Informatics, formal testing and is a limited time credential requiring renewal every five years. The exam tests for a combination of IT technical knowledge, clinical understanding, and project management experience thought to represent the typical workload of a PACS administrator or other radiology IT clinical support role. Certifications from PARCA (PACS Administrators Registry and Certifications Association) are also recognized. The five PARCA certifications are tiered from entry level to architect level.
Medical informatics in the UK The broad history of health informatics has been captured in the book UK Health Computing : Recollections and reflections, Hayes G, Barnett D (Eds.), BCS (May 2008) by those active in the field, predominantly members of BCS Health and its constituent groups. The book describes the path taken as ‘early development of health informatics was unorganized and idiosyncratic’. In the early -1950s it was prompted by those involved in NHS finance and only in the early 1960s did solutions including those in pathology (1960), radiotherapy (1962), immunization (1963), and primary care (1968) emerge. Many of these solutions, even in the early 1970s were developed in-house by pioneers in the field to meet their own requirements. In part this was due to some areas of health services (for example the immunization and vaccination of children) still being provided by Local Authorities. Interesting, this is a situation which the coalition government propose broadly to return to in the 2010 strategy Equity and Excellence: Liberating the NHS (July 2010); stating: "We will put patients at the heart of the NHS, through an information revolution and greater choice and control’ with shared decision-making becoming the norm: ‘no decision about me without me’ and patients having access to the information they want, to make choices about their care. They will have increased control over their own care records." These types of statements present a significant opportunity for health informaticians to come out of the back-office and take up a front-line role supporting clinical practice, and the business of care delivery. The UK health informatics community has long played a key role in international activity, joining TC4 of the International Federation of Information Processing (1969) which became IMIA (1979). Under the aegis of BCS Health, Cambridge was the host for the first EFMI Medical Informatics Europe (1974) conference and London was the location for IMIA’s tenth global congress (MEDINFO2001).
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Health informatics
Current state of health informatics and policy initiatives Americas Argentina Since 1997, the Buenos Aires Biomedical Informatics Group, a nonprofit group, represents the interests of a broad range of clinical and non-clinical professionals working within the Health Informatics sphere. Its purposes are: • Promote the implementation of the computer tool in the healthcare activity, scientific research, health administration and in all areas related to health sciences and biomedical research. • Support, promote and disseminate content related activities with the management of health information and tools they used to do under the name of Biomedical informatics. • Promote cooperation and exchange of actions generated in the field of biomedical informatics, both in the public and private, national and international level. • Interact with all scientists, recognized academic stimulating the creation of new instances that have the same goal and be inspired by the same purpose. • To promote, organize, sponsor and participate in events and activities for training in computer and information and disseminating developments in this area that might be useful for team members and health related activities. The Argentinian health system is heterogeneous in its function, and because of that the informatics developments show a heterogeneous stage. Many private Health Care center have developed systems, such as the German Hospital of Buenos Aires, or the Hospital Italiano de Buenos Aires that also has a residence program for health informatics. Brazil The first applications of computers to medicine and healthcare in Brazil started around 1968, with the installation of the first mainframes in public university hospitals, and the use of programmable calculators in scientific research applications. Minicomputers, such as the IBM 1130 were installed in several universities, and the first applications were developed for them, such as the hospital census in the School of Medicine of Ribeirão Preto and patient master files, in the Hospital das Clínicas da Universidade de São Paulo, respectively at the cities of Ribeirão Preto and São Paulo campi of the University of São Paulo. In the 1970s, several Digital Corporation and Hewlett Packard minicomputers were acquired for public and Armed Forces hospitals, and more intensively used for intensive-care unit, cardiology diagnostics, patient monitoring and other applications. In the early 1980s, with the arrival of cheaper microcomputers, a great upsurge of computer applications in health ensued, and in 1986 the Brazilian Society of Health Informatics was founded, the first Brazilian Congress of Health Informatics was held, and the first Brazilian Journal of Health Informatics was published. In Brazil, two universities are pioneers in teaching and research in Medical Informatics, both the University of Sao Paulo and the Federal University of Sao Paulo offer undergraduate programs highly qualified in the area as well as extensive graduate programs (MSc and PhD) Canada Health Informatics projects in Canada are implemented provincially, with different provinces creating different systems. A national, federally-funded, not-for-profit organization called Canada Health Infoway was created in 2001 to foster the development and adoption of electronic health records across Canada. As of December 31, 2008 there were 276 EHR projects under way in Canadian hospitals, other health-care facilities, pharmacies and laboratories, with an investment value of $1.5-billion from Canada Health Infoway. Provincial and territorial programmes include the following: • eHealth Ontario was created as an Ontario provincial government agency in September 2008. It has been plagued by delays and its CEO was fired over a multimillion-dollar contracts scandal in 2009. • Alberta Netcare was created in 2003 by the Government of Alberta. Today the netCARE portal is used daily by thousands of clinicians. It provides access to demographic data, prescribed/dispensed drugs, known
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Health informatics allergies/intolerances, immunizations, laboratory test results, diagnostic imaging reports, the diabetes registry and other medical reports. netCARE interface capabilities are being included in electronic medical record products which are being funded by the provincial government. United States In 2004, President George W. Bush signed Executive Order 13335 [4], creating the Office of the National Coordinator for Health Information Technology (ONCHIT) as a division of the U.S. Department of Health and Human Services (HHS). The mission of this office is widespread adoption of interoperable electronic health records (EHRs) in the US within 10 years. See quality improvement organizations for more information on federal initiatives in this area. The Certification Commission for Healthcare Information Technology (CCHIT), a private nonprofit group, was funded in 2005 by the U.S. Department of Health and Human Services to develop a set of standards for electronic health records (EHR) and supporting networks, and certify vendors who meet them. In July 2006, CCHIT released its first list of 22 certified ambulatory EHR products, in two different announcements.[5]
Europe The European Union's Member States are committed to sharing their best practices and experiences to create a European eHealth Area, thereby improving access to and quality health care at the same time as stimulating growth in a promising new industrial sector. The European eHealth Action Plan plays a fundamental role in the European Union's strategy. Work on this initiative involves a collaborative approach among several parts of the Commission services.[6][7] The European Institute for Health Records is involved in the promotion of high quality electronic health record systems in the European Union. UK There are different models of health informatics delivery in each of the home countries (England, Scotland, Northern Ireland and Wales) but some bodies like UKCHIP [8] (see below ) operate for those 'in and for' all the home countries and beyond. England NHS informatics in England was contracted out to several vendors for national health informatics solutions under the National Programme for Information Technology (NPfIT) label in the early-mid 2000's, under the auspices of NHS Connecting for Health (part of the Health and Social Care Information Centre as of 1 April 2013). NPfIT originally divided the country into five regions, with strategic 'systems integration' contracts awarded to one of several Local Service Providers (LSP). The various specific technical solutions were required to connect securely with the NHS 'Spine', a system designed to broker data between different systems and care settings).[16] NPfIT fell significant;ly was behind schedule and its scope and design were being revised in real time, exacerbated by media and political lambasting of the Programme's spend (past and projected) against proposed budget. In 2010 a consultation was launched as part of the new Conservative/Liberal Democrat Coalition Government's White Paper 'Liberating the NHS'. This initiative provided little in the way of innovative thinking, primarily re-stating existing strategies within the proposed new context of the Coalition's vision for the NHS. The degree of computerisation in NHS secondary care was quite high before NPfIT, and the programme stagnated further development of the install base - the original NPfIT regional approach provided neither a single, nationwide solution nor local health community agility or autonomy to purchase systems, but instead tried to deal with a hinterland in the middle. Almost all general practices in England and Wales are computerised under the 'GP Systems of Choice' (GPSoC) [9] programme, and patients have relatively extensive computerised primary care clinical records. System choice is the responsibility of individual GP practices and while there is no single, standardised GP system, GPSoC sets relatively rigid minimum standards of performance and functionality for vendors to adhere to. Interoperation between primary and secondary care systems
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Health informatics is rather primitive. A focus on interworking (for interfacing and integration) standards is hoped will stimulate synergy between primary and secondary care in sharing necessary information to support the care of individuals. Notably successes to date are in electronic requesting and viewing test results, and in some areas GPs having access to digital X-Ray images from secondary care systems. Scotland has an approach to central connection under way which is more advanced than the English one in some ways. Scotland has the GPASS system whose source code is owned by the State, and controlled and developed by NHS Scotland. GPASS was accepted in 1984. It has been provided free to all GPs in Scotland but has developed poorly.[citation needed] Discussion of open sourcing it as a remedy is occurring. Wales Wales has a dedicated Health Informatics function that supports NHS Wales in leading on the new integrated digital information services and promoting Health Informatics as a career. More information at www.wales.nhs.uk/nwis Emerging Directions (European R&D) The European Commission's preference, as exemplified in the 5th Framework[10] as well as currently pursued pilot projects,[11] is for Free/Libre and Open Source Software (FLOSS) for healthcare. Another stream of research currently focuses on aspects of "big data" in health information systems. For background information on data-related aspects in health informatics see, e.g., the book "Biomedical Informatics" [12] by Andreas Holzinger.
Asia and Oceania In Asia and Australia-New Zealand, the regional group called the Asia Pacific Association for Medical Informatics (APAMI) was established in 1994 and now consists of more than 15 member regions in the Asia Pacific Region. Australia The Australasian College of Health Informatics (ACHI) is the professional association for health informatics in the Asia-Pacific region. It represents the interests of a broad range of clinical and non-clinical professionals working within the health informatics sphere through a commitment to quality, standards and ethical practice.[13] ACHI is an academic institutional member of the International Medical Informatics Association (IMIA) and a full member of the Australian Council of Professions.[14] ACHI is a sponsor of the "e-Journal for Health Informatics",[15] an indexed and peer-reviewed professional journal. ACHI has also supported the "Australian Health Informatics Education Council" (AHIEC) since its founding in 2009.[16] Although there are a number of health informatics organisations in Australia, the Health Informatics Society of Australia (HISA) is regarded as the major umbrella group and is a member of the International Medical Informatics Association (IMIA). Nursing informaticians were the driving force behind the formation of HISA, which is now a company limited by guarantee of the members. The membership comes from across the informatics spectrum that is from students to corporate affiliates. HISA has a number of branches (Queensland, New South Wales, Victoria and Western Australia) as well as special interest groups such as nursing (NIA), pathology, aged and community care, industry and medical imaging (Conrick, 2006). China At last 20 years, China performed a successful transition from its planned economy to a socialist market economy. Along this great and earth-shaking change, China’s healthcare system also experienced a significant reform to follow and adapt to this historical revolution. In 2003, the data (released from Ministry of Health of the People's Republic of China (MoH)), indicated that the national healthcare-involved expenditure was up to RMB 662.33 billion totally, which accounted for about 5.56% of nation-wide gross domestic products. Before 1980s, the entire healthcare costs were covered in central government annual budget. Since that, the construct of healthcare-expended supporters started to change gradually. Most of the expenditure was contributed by health insurance schemes and private
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Health informatics spending, which corresponded to 40% and 45% of total expenditure, respectively. Meanwhile the financially governmental contribution was decreased to 10% only. On the other hand, by 2004, up to 296,492 healthcare facilities were recorded in statistic summary of MoH, and an average of 2.4 clinical beds per 1000 people were mentioned as well. Health Informatics in China Along with the development of information technology since 1990s, healthcare providers realised that the information could generate significant benefits to improve their services by computerised cases and data, for instance of gaining the information for directing patient care and assessing the best patient care for specific clinical conditions. Therefore substantial resources were collected to build China's own health informatics system. Most of these resources were arranged to construct Hospital Information System (HIS), which was aimed to Proportion of Nation-wide Hospitals with HIS in minimise unnecessary waste and repetition, subsequently to promote China by 2004 the efficiency and quality-control of healthcare. By 2004, China had successfully spread HIS through approximately 35-40% of nation-wide hospitals. However, the dispersion of hospital-owned HIS varies critically. In the east part of China , over 80% of hospitals constructed HIS, in northwest of China the equivalent was no more than 20%. Moreover, all of the Centers for Disease Control and Prevention (CDC) above rural level, approximately 80% of healthcare organisations above the rural level and 27% of hospitals over town level have the ability to perform the transmission of reports about real-time epidemic situation through public health information system and to analysis infectious diseases by dynamic statistics. Health Informatics Standards in China Collected information at different times, by different participants or systems could frequently lead to issues of misunderstanding, dis-comparing or dis-exchanging. To design an issues-minor system, healthcare providers realised that certain standards were the basis for sharing information and interoperability, however a system lacking standards would be a large impediment to interfere the improvement of corresponding information systems. Given that the standardisation for health informatics depends on the authorities, standardisation events must be involved with government and the subsequently relevant funding and supports were critical. In 2003, the Ministry of Health released the Development Lay-out of National Health Informatics (2003-2010) indicating the identification of standardisation for health informatics which is ‘combining adoption of international standards and development of national standards’. In China , the establishment of standardisation was initially facilitated with the development of vocabulary, classification and coding, which is conducive to reserve and transmit information for premium management at national level. By 2006, 55 international/ domestic standards of vocabulary, classification and coding have served in hospital information system. In 2003, the 10th revision of the International Statistical Classification of Diseases and Related Health Problems (ICD-10) and the ICD-10 Clinical Modification (ICD-10-CM) were adopted as standards for diagnostic classification and acute care procedure classification. Simultaneously, the International Classification of Primary Care (ICPC) were translated and tested in China ’s local applied environment. Another coding standard, named Logical Observation Identifiers Names and Codes (LOINC), was applied to serve as general identifiers for clinical observation in hospitals. Personal identifier codes were widely employed in different information systems, involving name, sex, nationality, family relationship, educational level and job occupation. However, these codes within different systems are inconsistent, when sharing between different regions. Considering this large quantity of vocabulary, classification and coding standards between different jurisdictions, the healthcare provider realised that using multiple systems could generate issues of resource wasting and a non-conflicting national level standard was
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beneficial and necessary. Therefore, in late 2003, the health informatics group in Ministry of Health released three projects to deal with issues of lacking national health information standards, which were the Chinese National Health Information Framework and Standardisation, the Basic Data Set Standards of Hospital Information System and the Basic Data Set Standards of Public Health Information System. Objectives of Chinese National Health Information Framework and Standardisation 1. Establish national health information framework and identify in what areas standards and guidelines are required 2. Identify the classes, relationships and attributes of national health information framework. Produce a conceptual health data model to cover the scope of the health information framework 3. Create logical data model for specific domains, depicting the logical data entities, the data attributes, and the relationships between the entities according to the conceptual health data model 4. Establish uniform represent standard for data elements according to the data entities and their attributes in conceptual data model and logical data model 5. Circulate the completed health information framework and health data model to the partnership members for review and acceptance 6. Develop a process to maintain and refine the China model and to align with and influence international health data models
Comparison between China's EHR Standard and Segments of the ASTM E 1384 Standard Recently, researchers from local universities evaluated the performance of China’s Electronic Health Record(EHR) Standard compared with the American Society for Testing and Materials Standard Practice for Content and Structure of Electronic Health Records in the United States (ASTM E 1384 Standard). China’s EHR Standard ● H.01 Document identifier, H.02 Service object identifier, H.03Demographics, H.04 Contact person, H.05 Address, H.06 Contacts
ASTM E 1384 Standard ● Seg1 Demographic/Administrative, Seg14A Administrative/Diagnostic Summary
● H.07 Medical insurance ● H.08 Healthcare institution, H.09 Healthcare practitioner
● Seg4 Provider/Practitioners
● H.10 Event summary
● Seg5 Problem List, Seg14A Administrative/Diagnostic Summary
● S.01 Chief complaints
● Seg14B Chief Complaint Present Illness/Trauma Care
● S.02 Physical exam
● Seg9 Assessments/Exams
● S.03 Present illness history
● Seg14B Chief Complaint Present Illness/Trauma Care
● S.04 Past medical history
● Seg5 Problem List, Seg6 Immunizations, Seg7 Exposure to Hazardous Substances, Seg8 Family/Prenatal/Cumulative Health/Medical/Dental Nursing History
● S.05 Specific Exam, S.06 Lab data
● Seg11 Diagnostic Tests
● S.07 Diagnoses
● Seg5 Problem List, Seg14A Administrative/Diagnostic Summary
● S.08 Procedures
● Seg14E Procedures
● S.09 Medications
● Seg12 Medications
● S.10 Care/treatment plans
● Seg2 Legal Agreements, Seg10 Care/Treatment Plans and Orders, Seg13 Scheduled Appointments/Events
● S.11 Assessments
● Seg9 Assessments/Exams
● S.12 Encounters/episodes notes
● Seg14C Progress Notes/Clinical Course, Seg14D Therapies, Seg14F Disposition
● S.13 Financial information
● Seg3 Financial
Health informatics
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● S.14 Nursing service
● Seg8 Family/Prenatal/Cumulative Health/Medical/Dental Nursing History, Seg14D Therapies
● S.15 Health guidance
● Seg10 Care/Treatment Plans and Orders
● S.16 Four diagnostic methods in Traditional Chinese medicine
● Seg11 Diagnostic Tests
The table above demonstrates details of this comparison which indicates certain domains of improvement for future revisions of EHR Standard in China. Detailedly, these deficiencies are listed in the following. 1. The lack of supporting on privacy and security. The ISO/TS 18308 specifies” The EHR must support the ethical and legal use of personal information, in accordance with established privacy principles and frameworks, which may be culturally or jurisdictionally specific” (ISO 18308: Health Informatics-Requirements for an Electronic Health Record Architecture, 2004). However this China’s EHR Standard did not achieve any of the fifteen requirements in the subclass of privacy and security. 2. The shortage of supporting on different types of data and reference. Considering only ICD-9 is referenced as China’s external international coding systems, other similar systems, such as SNOMED CT in clinical terminology presentation, cannot be considered as familiar for Chinese specialists, which could lead to internationally information-sharing deficiency. 3. The lack of more generic and extensible lower level data structures. China’s large and complex EHR Standard was constructed for all medical domains. However, the specific and time-frequent attributes of clinical data elements, value sets and templates identified that this once-for-all purpose cannot lead to practical consequence. Hong Kong In Hong Kong a computerized patient record system called the Clinical Management System (CMS) has been developed by the Hospital Authority since 1994. This system has been deployed at all the sites of the Authority (40 hospitals and 120 clinics), and is used by all 30,000 clinical staff on a daily basis, with a daily transaction of up to 2 millions. The comprehensive records of 7 million patients are available on-line in the Electronic Patient Record (ePR), with data integrated from all sites. Since 2004 radiology image viewing has been added to the ePR, with radiography images from any HA site being available as part of the ePR. The Hong Kong Hospital Authority placed particular attention to the governance of clinical systems development, with input from hundreds of clinicians being incorporated through a structured process. The Health Informatics Section in Hong Kong Hospital Authority[17] has close relationship with Information Technology Department and clinicians to develop healthcare systems for the organization to support the service to all public hospitals and clinics in the region. The Hong Kong Society of Medical Informatics (HKSMI) was established in 1987 to promote the use of information technology in healthcare. The eHealth Consortium has been formed to bring together clinicians from both the private and public sectors, medical informatics professionals and the IT industry to further promote IT in healthcare in Hong Kong.[18]
Health informatics New Zealand Health Informatics is taught at five New Zealand universities. The most mature and established is the Otago programme which has been offered for over a decade. Health Informatics New Zealand (HINZ), is the national organisation that advocates for Health Informatics. HINZ organises a conference every year and also publishes an online journal- Healthcare Informatics Review Online. Saudi Arabia The Saudi Association for Health Information (SAHI) was established in 2006 to work under direct supervision of King Saud bin Abdulaziz University for Health Sciences to practice public activities, develop theoretical and applicable knowledge, and provide scientific and applicable studies.
Health Informatics Law Health informatics law deals with evolving and sometimes complex legal principles as they apply to information technology in health-related fields. It addresses the privacy, ethical and operational issues that invariably arise when electronic tools, information and media are used in health care delivery. Health Informatics Law also applies to all matters that involve information technology, health care and the interaction of information. It deals with the circumstances under which data and records are shared with other fields or areas that support and enhance patient care. As many healthcare systems are making an effort to have patient records more readily available to them via the internet, it is important that providers be sure that there are a few security standards in place in order to make sure that the patients information is safe. They have to be able to assure confidentiality and the security of the people, process, and technology. Since there is also the possibility of payments being made through this system, it is vital that this aspect of their private information will also be protected through cryptography.
History World wide use of computer technology in medicine began in the early 1950s with the rise of the computers. In 1949, Gustav Wagner established the first professional organization for informatics in Germany. The prehistory, history, and future of medical information and health information technology are discussed in reference. Specialized university departments and Informatics training programs began during the 1960s in France, Germany, Belgium and The Netherlands. Medical informatics research units began to appear during the 1970s in Poland and in the U.S. Since then the development of high-quality health informatics research, education and infrastructure has been a goal of the U.S. and the European Union. Early names for health informatics included medical computing, biomedical computing, medical computer science, computer medicine, medical electronic data processing, medical automatic data processing, medical information processing, medical information science, medical software engineering, and medical computer technology.[citation needed]
The health informatics community is still growing, it is by no means a mature profession, but work in the UK by the voluntary registration body, the UK Council of Health Informatics Professions has suggested eight key constituencies within the domain - information management, knowledge management, portfolio/programme/project management, ICT, education and research, clinical informatics, health records(service and business-related), health informatics service management. These constituencies accommodate professionals in and for the NHS, in academia and commercial service and solution providers. Since the 1970s the most prominent international coordinating body has been the International Medical Informatics Association (IMIA).
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References [1] [2] [3] [4] [5]
Richesson, Rachel L., and James E. Andrews. 2012.Clinical research informatics. London: Springer. MGH - Laboratory of Computer Science (http:/ / www. lcs. mgh. harvard. edu/ ) http:/ / en. wikipedia. org/ w/ index. php?title=Health_informatics& action=edit http:/ / www. gpo. gov/ fdsys/ pkg/ FR-2004-04-30/ pdf/ 04-10024. pdf Certification Commission for Healthcare Information Technology (July 18, 2006): CCHIT Announces First Certified Electronic Health Record Products (http:/ / www. cchit. org/ media/ press+ releases/ CCHIT+ Announces+ First+ Certified+ Electronic+ Health+ Record+ Products. htm). Retrieved July 26, 2006. [6] European eHealth Action Plan (http:/ / ec. europa. eu/ information_society/ activities/ health/ policy_action_plan/ index_en. htm) [7] European eHealth Action Plan i2010 (http:/ / ec. europa. eu/ information_society/ eeurope/ i2010/ index_en. htm) [8] http:/ / www. ukchip. org [9] http:/ / systems. hscic. gov. uk/ gpsoc [10] Cordis FP5web (http:/ / cordis. europa. eu/ fp5/ ) [11] European Patient Smart Open Services (http:/ / www. epsos. eu) [12] Holzinger, A. (2012). Biomedical Informatics, Lecture Notes to LV 444.152. Books on Demand, ISBN 978-3-8482-2219-3. [13] Australasian College of Health Informatics [14] ACHI Memberships (http:/ / www. ACHI. org. au) ACHI memberships: Professions Australia [15] eJHI - electronic Journal of Health Informatics (http:/ / www. ejhi. net/ ojs/ index. php/ ejhi/ about/ journalSponsorship) (open access journal) [16] Australian Health Informatics Education Council (AHIEC) (http:/ / www. AHIEC. org. au) AHIEC Auspicing Organisations [17] Health Informatics Section in Hong Kong Hospital Authority (http:/ / www. ha. org. hk/ hi/ Welcome. html) [18] eHealth Consortium (http:/ / www. iproa. org/ ProjectDetail. action?id=270)
External links • • • •
Health informatics (http://www.dmoz.org/Health/Medicine/Informatics/) at the Open Directory Project e-Journal for Health Informatics (http://www.eJHI.net) Article about informatics (http://www.biomedcentral.com/1472-6947/9/24) Willison, Brian. Advancing Meaningful Use: Simplifying Complex Clinical Metrics Through Visual Representation. (http://piim.newschool.edu/_media/pdfs/PIIM-RESEARCH_AdvancingMeaningfulUse.pdf) • Clinfowiki (http://www.clinfowiki.org/) • Global Health Informatics Partnership (http://www.ghip.net/)
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Clinical Informatics
Clinical Informatics Health informatics (also called Health Information Systems, health care informatics, healthcare informatics, medical informatics, nursing informatics, clinical informatics, or biomedical informatics) is a discipline at the intersection of information science, computer science, and health care. It deals with the resources, devices, and methods required to optimize the acquisition, storage, retrieval, and use of information in health and biomedicine. Health informatics Electronic patient chart from a health information tools include computers, clinical guidelines, formal medical system terminologies, and information and communication systems. It is applied to the areas of nursing, clinical care, dentistry, pharmacy, public health, occupational therapy, physical therapy and (bio)medical research. • The international standards on the subject are covered by ICS 35.240.80 in which ISO 27799:2008 is one of the core components. • Molecular bioinformatics and clinical informatics have converged into the field of translational bioinformatics.
Clinical Informatics Clinical Informatics is concerned with the use of information in health care by clinicians. Clinical informaticians transform health care by analyzing, designing, implementing, and evaluating information and communication systems that enhance individual and population health outcomes, improve [patient] care, and strengthen the clinician-patient relationship. Clinical informaticians use their knowledge of patient care combined with their understanding of informatics concepts, methods, and health informatics tools to: • • • •
assess information and knowledge needs of health care professionals and patients, characterize, evaluate, and refine clinical processes, develop, implement, and refine clinical decision support systems, and lead or participate in the procurement, customization, development, implementation, management, evaluation, and continuous improvement of clinical information systems.
Clinicians collaborate with other health care and information technology professionals to develop health informatics tools which promote patient care that is safe, efficient, effective, timely, patient-centered, and equitable. In October 2011 American Board of Medical Specialties (ABMS), the organization overseeing the certification of physician specialists in the United States, announces the creation of physician certification in Clinical Informatics. The first examination for board certification in the subspecialty of Clinical Informatics will be offered in October 2013 by American Board of Preventive Medicine.
Integrated data repository Development of the field of clinical informatics lead to creation of large data sets with electronic health record data integrated with other data (such as genomic data). Large data warehouses are often described as clinical data warehouses (also known as clinical data repositories). In research, deidentified CDWs can be used by researchers with less complex ethical oversight. CDWs with data of deceased patients were also suggested as a research resource that does not require IRB approval.
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Clinical Informatics
Translational bioinformatics With the completion of the human genome and the recent advent of high throughput sequencing and genome-wide association studies of single nucleotide polymorphisms, the fields of molecular bioinformatics, biostatistics, statistical genetics and clinical informatics are converging into the emerging field of translational bioinformatics. The relationship between bioinformatics and health informatics, while conceptually related under the umbrella of biomedical informatics, has not always been very clear. The TBI community is specifically motivated with the development of approaches to identify linkages between fundamental biological and clinical information. Along with complementary areas of emphasis, such as those focused on developing systems and approaches within clinical research contexts, insights from TBI may enable a new paradigm for the study and treatment of disease.
Clinical Research Informatics Clinical Research Informatics (or, CRI) takes the core foundations, principles, and technologies related to Health Informatics, and applies these to clinical research contexts. As such, CRI is a sub-discipline of sorts of Health Informatics, and interest and activities in CRI have increased greatly in recent years given the overwhelming problems associated with the explosive growth of clinical research data and information.[1] There are a number of activities within clinical research that CRI supports, including: • • • • • •
more efficient and effective data collection and acquisition optimal protocol design and efficient management patient recruitment and management adverse event reporting regulatory compliance data storage, transfer, processing and analysis
Computational Health Informatics Computational health informatics is a branch of Computer Science that deals specifically with computational techniques that are relevant in healthcare. Computational health informatics is also a branch of Health Informatics, but is orthogonal to much of the work going on in health informatics because computer scientist's interest is mainly in understanding fundamental properties of computation. Health informatics, on the other hand, is primarily concerned with understanding fundamental properties of medicine that allow for the intervention of computers. The health domain provides an extremely wide variety of problems that can be tackled using computational techniques, and computer scientists are attempting to make a difference in medicine by studying the underlying principles of computer science that will allow for meaningful (to medicine) algorithms and systems to be developed. Thus, computer scientists working in computational health informatics and health scientists working in medical health informatics combine to develop the next generation of healthcare technologies. Using computers to analyze health data has been around since the 1950s, but it wasn't until the 1990s that the first sturdy models appeared. The development of the internet has helped develop computational health informatics over the past decade. Computer models are used to examine various topics such as how exercise affects obesity, healthcare costs, and many more. Examples of projects in computational health informatics include the COACH project.
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Clinical Informatics
Medical informatics in the United States Even though the idea of using computers in medicine emerged as technology advanced in the early 20th century, it was not until the 1950s that informatics began to have an effect in the United States. The earliest use of electronic digital computers for medicine was for dental projects in the 1950s at the United States National Bureau of Standards by Robert Ledley. During the mid-1950s, the United States Air Force (USAF) carried out several medical projects on its computers while also encouraging civilian agencies such as the National Academy of Sciences - National Research Council (NAS-NRC) and the National Institutes of Health (NIH) to sponsor such work. In 1959, Ledley and Lee B. Lusted published “Reasoning Foundations of Medical Diagnosis,” a widely-read article in Science, which introduced computing (especially operations research) techniques to medical workers. Ledley and Lusted’s article has remained influential for decades, especially within the field of medical decision making. Guided by Ledley's late 1950s survey of computer use in biology and medicine (carried out for the NAS-NRC), and by his and Lusted's articles, the NIH undertook the first major effort to introduce computers to biology and medicine. This effort, carried out initially by the NIH's Advisory Committee on Computers in Research (ACCR), chaired by Lusted, spent over $40 million between 1960 and 1964 in order to establish dozens of large and small biomedical research centers in the US. One early (1960, non-ACCR) use of computers was to help quantify normal human movement, as a precursor to scientifically measuring deviations from normal, and design of prostheses. The use of computers (IBM 650, 1620, and 7040) allowed analysis of a large sample size, and of more measurements and subgroups than had been previously practical with mechanical calculators, thus allowing an objective understanding of how human locomotion varies by age and body characteristics. A study co-author was Dean of the Marquette University College of Engineering; this work led to discrete Biomedical Engineering departments there and elsewhere. The next steps, in the mid-1960s, were the development (sponsored largely by the NIH) of expert systems such as MYCIN and Internist-I. In 1965, the National Library of Medicine started to use MEDLINE and MEDLARS. Around this time, Neil Pappalardo, Curtis Marble, and Robert Greenes developed MUMPS (Massachusetts General Hospital Utility Multi-Programming System) in Octo Barnett's Laboratory of Computer Science [2] at Massachusetts General Hospital in Boston, another center of biomedical computing that received significant support from the NIH. In the 1970s and 1980s it was the most commonly used programming language for clinical applications. The MUMPS operating system was used to support MUMPS language specifications. As of 2004[3], a descendent of this system is being used in the United States Veterans Affairs hospital system. The VA has the largest enterprise-wide health information system that includes an electronic medical record, known as the Veterans Health Information Systems and Technology Architecture (VistA). A graphical user interface known as the Computerized Patient Record System (CPRS) allows health care providers to review and update a patient’s electronic medical record at any of the VA's over 1,000 health care facilities. During the 1960s, Morris Collen, a physician working for Kaiser Permanente's Division of Research, developed computerized systems to automate many aspects of multiphasic health checkups. These system became the basis the larger medical databases Kaiser Permanente developed during the 1970s and 1980s. The American College of Medical Informatics (ACMI) has since 1993 annually bestowed the Morris F. Collen, MD Medal for Outstanding Contributions to the Field of Medical Informatics. In the 1970s a growing number of commercial vendors began to market practice management and electronic medical records systems. Although many products exist, only a small number of health practitioners use fully featured electronic health care records systems. Homer R. Warner, one of the fathers of medical informatics, founded the Department of Medical Informatics at the University of Utah in 1968. The American Medical Informatics Association (AMIA) has an award named after him on application of informatics to medicine.
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Clinical Informatics
Informatics Certifications Like other IT training specialties, there are Informatics certifications available to help informatics professionals stand out and be recognized. The American Nurses Credentialing Center (ANCC) offers a board certification in Nursing Informatics, the Radiology Informatics, the CIIP (Certified Imaging Informatics Professional) certification was created by ABII (The American Board of Imaging Informatics) which is sponsored by SIIM (the Society for Imaging Informatics in Medicine) in 2005. The CIIP certification requires documented experience working in Imaging Informatics, formal testing and is a limited time credential requiring renewal every five years. The exam tests for a combination of IT technical knowledge, clinical understanding, and project management experience thought to represent the typical workload of a PACS administrator or other radiology IT clinical support role. Certifications from PARCA (PACS Administrators Registry and Certifications Association) are also recognized. The five PARCA certifications are tiered from entry level to architect level.
Medical informatics in the UK The broad history of health informatics has been captured in the book UK Health Computing : Recollections and reflections, Hayes G, Barnett D (Eds.), BCS (May 2008) by those active in the field, predominantly members of BCS Health and its constituent groups. The book describes the path taken as ‘early development of health informatics was unorganized and idiosyncratic’. In the early -1950s it was prompted by those involved in NHS finance and only in the early 1960s did solutions including those in pathology (1960), radiotherapy (1962), immunization (1963), and primary care (1968) emerge. Many of these solutions, even in the early 1970s were developed in-house by pioneers in the field to meet their own requirements. In part this was due to some areas of health services (for example the immunization and vaccination of children) still being provided by Local Authorities. Interesting, this is a situation which the coalition government propose broadly to return to in the 2010 strategy Equity and Excellence: Liberating the NHS (July 2010); stating: "We will put patients at the heart of the NHS, through an information revolution and greater choice and control’ with shared decision-making becoming the norm: ‘no decision about me without me’ and patients having access to the information they want, to make choices about their care. They will have increased control over their own care records." These types of statements present a significant opportunity for health informaticians to come out of the back-office and take up a front-line role supporting clinical practice, and the business of care delivery. The UK health informatics community has long played a key role in international activity, joining TC4 of the International Federation of Information Processing (1969) which became IMIA (1979). Under the aegis of BCS Health, Cambridge was the host for the first EFMI Medical Informatics Europe (1974) conference and London was the location for IMIA’s tenth global congress (MEDINFO2001).
Current state of health informatics and policy initiatives Americas Argentina Since 1997, the Buenos Aires Biomedical Informatics Group, a nonprofit group, represents the interests of a broad range of clinical and non-clinical professionals working within the Health Informatics sphere. Its purposes are: • Promote the implementation of the computer tool in the healthcare activity, scientific research, health administration and in all areas related to health sciences and biomedical research. • Support, promote and disseminate content related activities with the management of health information and tools they used to do under the name of Biomedical informatics.
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Clinical Informatics • Promote cooperation and exchange of actions generated in the field of biomedical informatics, both in the public and private, national and international level. • Interact with all scientists, recognized academic stimulating the creation of new instances that have the same goal and be inspired by the same purpose. • To promote, organize, sponsor and participate in events and activities for training in computer and information and disseminating developments in this area that might be useful for team members and health related activities. The Argentinian health system is heterogeneous in its function, and because of that the informatics developments show a heterogeneous stage. Many private Health Care center have developed systems, such as the German Hospital of Buenos Aires, or the Hospital Italiano de Buenos Aires that also has a residence program for health informatics. Brazil The first applications of computers to medicine and healthcare in Brazil started around 1968, with the installation of the first mainframes in public university hospitals, and the use of programmable calculators in scientific research applications. Minicomputers, such as the IBM 1130 were installed in several universities, and the first applications were developed for them, such as the hospital census in the School of Medicine of Ribeirão Preto and patient master files, in the Hospital das Clínicas da Universidade de São Paulo, respectively at the cities of Ribeirão Preto and São Paulo campi of the University of São Paulo. In the 1970s, several Digital Corporation and Hewlett Packard minicomputers were acquired for public and Armed Forces hospitals, and more intensively used for intensive-care unit, cardiology diagnostics, patient monitoring and other applications. In the early 1980s, with the arrival of cheaper microcomputers, a great upsurge of computer applications in health ensued, and in 1986 the Brazilian Society of Health Informatics was founded, the first Brazilian Congress of Health Informatics was held, and the first Brazilian Journal of Health Informatics was published. In Brazil, two universities are pioneers in teaching and research in Medical Informatics, both the University of Sao Paulo and the Federal University of Sao Paulo offer undergraduate programs highly qualified in the area as well as extensive graduate programs (MSc and PhD) Canada Health Informatics projects in Canada are implemented provincially, with different provinces creating different systems. A national, federally-funded, not-for-profit organization called Canada Health Infoway was created in 2001 to foster the development and adoption of electronic health records across Canada. As of December 31, 2008 there were 276 EHR projects under way in Canadian hospitals, other health-care facilities, pharmacies and laboratories, with an investment value of $1.5-billion from Canada Health Infoway. Provincial and territorial programmes include the following: • eHealth Ontario was created as an Ontario provincial government agency in September 2008. It has been plagued by delays and its CEO was fired over a multimillion-dollar contracts scandal in 2009. • Alberta Netcare was created in 2003 by the Government of Alberta. Today the netCARE portal is used daily by thousands of clinicians. It provides access to demographic data, prescribed/dispensed drugs, known allergies/intolerances, immunizations, laboratory test results, diagnostic imaging reports, the diabetes registry and other medical reports. netCARE interface capabilities are being included in electronic medical record products which are being funded by the provincial government.
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Clinical Informatics United States In 2004, President George W. Bush signed Executive Order 13335 [4], creating the Office of the National Coordinator for Health Information Technology (ONCHIT) as a division of the U.S. Department of Health and Human Services (HHS). The mission of this office is widespread adoption of interoperable electronic health records (EHRs) in the US within 10 years. See quality improvement organizations for more information on federal initiatives in this area. The Certification Commission for Healthcare Information Technology (CCHIT), a private nonprofit group, was funded in 2005 by the U.S. Department of Health and Human Services to develop a set of standards for electronic health records (EHR) and supporting networks, and certify vendors who meet them. In July 2006, CCHIT released its first list of 22 certified ambulatory EHR products, in two different announcements.[3]
Europe The European Union's Member States are committed to sharing their best practices and experiences to create a European eHealth Area, thereby improving access to and quality health care at the same time as stimulating growth in a promising new industrial sector. The European eHealth Action Plan plays a fundamental role in the European Union's strategy. Work on this initiative involves a collaborative approach among several parts of the Commission services.[4][5] The European Institute for Health Records is involved in the promotion of high quality electronic health record systems in the European Union. UK There are different models of health informatics delivery in each of the home countries (England, Scotland, Northern Ireland and Wales) but some bodies like UKCHIP [8] (see below ) operate for those 'in and for' all the home countries and beyond. England NHS informatics in England was contracted out to several vendors for national health informatics solutions under the National Programme for Information Technology (NPfIT) label in the early-mid 2000's, under the auspices of NHS Connecting for Health (part of the Health and Social Care Information Centre as of 1 April 2013). NPfIT originally divided the country into five regions, with strategic 'systems integration' contracts awarded to one of several Local Service Providers (LSP). The various specific technical solutions were required to connect securely with the NHS 'Spine', a system designed to broker data between different systems and care settings).[16] NPfIT fell significant;ly was behind schedule and its scope and design were being revised in real time, exacerbated by media and political lambasting of the Programme's spend (past and projected) against proposed budget. In 2010 a consultation was launched as part of the new Conservative/Liberal Democrat Coalition Government's White Paper 'Liberating the NHS'. This initiative provided little in the way of innovative thinking, primarily re-stating existing strategies within the proposed new context of the Coalition's vision for the NHS. The degree of computerisation in NHS secondary care was quite high before NPfIT, and the programme stagnated further development of the install base - the original NPfIT regional approach provided neither a single, nationwide solution nor local health community agility or autonomy to purchase systems, but instead tried to deal with a hinterland in the middle. Almost all general practices in England and Wales are computerised under the 'GP Systems of Choice' (GPSoC) [9] programme, and patients have relatively extensive computerised primary care clinical records. System choice is the responsibility of individual GP practices and while there is no single, standardised GP system, GPSoC sets relatively rigid minimum standards of performance and functionality for vendors to adhere to. Interoperation between primary and secondary care systems is rather primitive. A focus on interworking (for interfacing and integration) standards is hoped will stimulate synergy between primary and secondary care in sharing necessary information to support the care of individuals. Notably successes to date are in electronic requesting and viewing test results, and in some areas GPs having access to digital X-Ray images from secondary care systems. Scotland has an approach to central connection under way
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Clinical Informatics which is more advanced than the English one in some ways. Scotland has the GPASS system whose source code is owned by the State, and controlled and developed by NHS Scotland. GPASS was accepted in 1984. It has been provided free to all GPs in Scotland but has developed poorly.[citation needed] Discussion of open sourcing it as a remedy is occurring. Wales Wales has a dedicated Health Informatics function that supports NHS Wales in leading on the new integrated digital information services and promoting Health Informatics as a career. More information at www.wales.nhs.uk/nwis Emerging Directions (European R&D) The European Commission's preference, as exemplified in the 5th Framework[6] as well as currently pursued pilot projects,[7] is for Free/Libre and Open Source Software (FLOSS) for healthcare. Another stream of research currently focuses on aspects of "big data" in health information systems. For background information on data-related aspects in health informatics see, e.g., the book "Biomedical Informatics" [8] by Andreas Holzinger.
Asia and Oceania In Asia and Australia-New Zealand, the regional group called the Asia Pacific Association for Medical Informatics (APAMI) was established in 1994 and now consists of more than 15 member regions in the Asia Pacific Region. Australia The Australasian College of Health Informatics (ACHI) is the professional association for health informatics in the Asia-Pacific region. It represents the interests of a broad range of clinical and non-clinical professionals working within the health informatics sphere through a commitment to quality, standards and ethical practice.[9] ACHI is an academic institutional member of the International Medical Informatics Association (IMIA) and a full member of the Australian Council of Professions.[10] ACHI is a sponsor of the "e-Journal for Health Informatics",[11] an indexed and peer-reviewed professional journal. ACHI has also supported the "Australian Health Informatics Education Council" (AHIEC) since its founding in 2009.[12] Although there are a number of health informatics organisations in Australia, the Health Informatics Society of Australia (HISA) is regarded as the major umbrella group and is a member of the International Medical Informatics Association (IMIA). Nursing informaticians were the driving force behind the formation of HISA, which is now a company limited by guarantee of the members. The membership comes from across the informatics spectrum that is from students to corporate affiliates. HISA has a number of branches (Queensland, New South Wales, Victoria and Western Australia) as well as special interest groups such as nursing (NIA), pathology, aged and community care, industry and medical imaging (Conrick, 2006). China At last 20 years, China performed a successful transition from its planned economy to a socialist market economy. Along this great and earth-shaking change, China’s healthcare system also experienced a significant reform to follow and adapt to this historical revolution. In 2003, the data (released from Ministry of Health of the People's Republic of China (MoH)), indicated that the national healthcare-involved expenditure was up to RMB 662.33 billion totally, which accounted for about 5.56% of nation-wide gross domestic products. Before 1980s, the entire healthcare costs were covered in central government annual budget. Since that, the construct of healthcare-expended supporters started to change gradually. Most of the expenditure was contributed by health insurance schemes and private spending, which corresponded to 40% and 45% of total expenditure, respectively. Meanwhile the financially governmental contribution was decreased to 10% only. On the other hand, by 2004, up to 296,492 healthcare facilities were recorded in statistic summary of MoH, and an average of 2.4 clinical beds per 1000 people were mentioned as well.
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Clinical Informatics Health Informatics in China Along with the development of information technology since 1990s, healthcare providers realised that the information could generate significant benefits to improve their services by computerised cases and data, for instance of gaining the information for directing patient care and assessing the best patient care for specific clinical conditions. Therefore substantial resources were collected to build China's own health informatics system. Most of these resources were arranged to construct Hospital Information System (HIS), which was aimed to Proportion of Nation-wide Hospitals with HIS in minimise unnecessary waste and repetition, subsequently to promote China by 2004 the efficiency and quality-control of healthcare. By 2004, China had successfully spread HIS through approximately 35-40% of nation-wide hospitals. However, the dispersion of hospital-owned HIS varies critically. In the east part of China , over 80% of hospitals constructed HIS, in northwest of China the equivalent was no more than 20%. Moreover, all of the Centers for Disease Control and Prevention (CDC) above rural level, approximately 80% of healthcare organisations above the rural level and 27% of hospitals over town level have the ability to perform the transmission of reports about real-time epidemic situation through public health information system and to analysis infectious diseases by dynamic statistics. Health Informatics Standards in China Collected information at different times, by different participants or systems could frequently lead to issues of misunderstanding, dis-comparing or dis-exchanging. To design an issues-minor system, healthcare providers realised that certain standards were the basis for sharing information and interoperability, however a system lacking standards would be a large impediment to interfere the improvement of corresponding information systems. Given that the standardisation for health informatics depends on the authorities, standardisation events must be involved with government and the subsequently relevant funding and supports were critical. In 2003, the Ministry of Health released the Development Lay-out of National Health Informatics (2003-2010) indicating the identification of standardisation for health informatics which is ‘combining adoption of international standards and development of national standards’. In China , the establishment of standardisation was initially facilitated with the development of vocabulary, classification and coding, which is conducive to reserve and transmit information for premium management at national level. By 2006, 55 international/ domestic standards of vocabulary, classification and coding have served in hospital information system. In 2003, the 10th revision of the International Statistical Classification of Diseases and Related Health Problems (ICD-10) and the ICD-10 Clinical Modification (ICD-10-CM) were adopted as standards for diagnostic classification and acute care procedure classification. Simultaneously, the International Classification of Primary Care (ICPC) were translated and tested in China ’s local applied environment. Another coding standard, named Logical Observation Identifiers Names and Codes (LOINC), was applied to serve as general identifiers for clinical observation in hospitals. Personal identifier codes were widely employed in different information systems, involving name, sex, nationality, family relationship, educational level and job occupation. However, these codes within different systems are inconsistent, when sharing between different regions. Considering this large quantity of vocabulary, classification and coding standards between different jurisdictions, the healthcare provider realised that using multiple systems could generate issues of resource wasting and a non-conflicting national level standard was beneficial and necessary. Therefore, in late 2003, the health informatics group in Ministry of Health released three projects to deal with issues of lacking national health information standards, which were the Chinese National Health Information Framework and Standardisation, the Basic Data Set Standards of Hospital Information System and the Basic Data Set Standards of Public Health Information System.
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Clinical Informatics
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Objectives of Chinese National Health Information Framework and Standardisation 1. Establish national health information framework and identify in what areas standards and guidelines are required 2. Identify the classes, relationships and attributes of national health information framework. Produce a conceptual health data model to cover the scope of the health information framework 3. Create logical data model for specific domains, depicting the logical data entities, the data attributes, and the relationships between the entities according to the conceptual health data model 4. Establish uniform represent standard for data elements according to the data entities and their attributes in conceptual data model and logical data model 5. Circulate the completed health information framework and health data model to the partnership members for review and acceptance 6. Develop a process to maintain and refine the China model and to align with and influence international health data models
Comparison between China's EHR Standard and Segments of the ASTM E 1384 Standard Recently, researchers from local universities evaluated the performance of China’s Electronic Health Record(EHR) Standard compared with the American Society for Testing and Materials Standard Practice for Content and Structure of Electronic Health Records in the United States (ASTM E 1384 Standard). China’s EHR Standard ● H.01 Document identifier, H.02 Service object identifier, H.03Demographics, H.04 Contact person, H.05 Address, H.06 Contacts
ASTM E 1384 Standard ● Seg1 Demographic/Administrative, Seg14A Administrative/Diagnostic Summary
● H.07 Medical insurance ● H.08 Healthcare institution, H.09 Healthcare practitioner
● Seg4 Provider/Practitioners
● H.10 Event summary
● Seg5 Problem List, Seg14A Administrative/Diagnostic Summary
● S.01 Chief complaints
● Seg14B Chief Complaint Present Illness/Trauma Care
● S.02 Physical exam
● Seg9 Assessments/Exams
● S.03 Present illness history
● Seg14B Chief Complaint Present Illness/Trauma Care
● S.04 Past medical history
● Seg5 Problem List, Seg6 Immunizations, Seg7 Exposure to Hazardous Substances, Seg8 Family/Prenatal/Cumulative Health/Medical/Dental Nursing History
● S.05 Specific Exam, S.06 Lab data
● Seg11 Diagnostic Tests
● S.07 Diagnoses
● Seg5 Problem List, Seg14A Administrative/Diagnostic Summary
● S.08 Procedures
● Seg14E Procedures
● S.09 Medications
● Seg12 Medications
● S.10 Care/treatment plans
● Seg2 Legal Agreements, Seg10 Care/Treatment Plans and Orders, Seg13 Scheduled Appointments/Events
● S.11 Assessments
● Seg9 Assessments/Exams
● S.12 Encounters/episodes notes
● Seg14C Progress Notes/Clinical Course, Seg14D Therapies, Seg14F Disposition
● S.13 Financial information
● Seg3 Financial
● S.14 Nursing service
● Seg8 Family/Prenatal/Cumulative Health/Medical/Dental Nursing History, Seg14D Therapies
● S.15 Health guidance
● Seg10 Care/Treatment Plans and Orders
● S.16 Four diagnostic methods in Traditional Chinese medicine
● Seg11 Diagnostic Tests
Clinical Informatics
The table above demonstrates details of this comparison which indicates certain domains of improvement for future revisions of EHR Standard in China. Detailedly, these deficiencies are listed in the following. 1. The lack of supporting on privacy and security. The ISO/TS 18308 specifies” The EHR must support the ethical and legal use of personal information, in accordance with established privacy principles and frameworks, which may be culturally or jurisdictionally specific” (ISO 18308: Health Informatics-Requirements for an Electronic Health Record Architecture, 2004). However this China’s EHR Standard did not achieve any of the fifteen requirements in the subclass of privacy and security. 2. The shortage of supporting on different types of data and reference. Considering only ICD-9 is referenced as China’s external international coding systems, other similar systems, such as SNOMED CT in clinical terminology presentation, cannot be considered as familiar for Chinese specialists, which could lead to internationally information-sharing deficiency. 3. The lack of more generic and extensible lower level data structures. China’s large and complex EHR Standard was constructed for all medical domains. However, the specific and time-frequent attributes of clinical data elements, value sets and templates identified that this once-for-all purpose cannot lead to practical consequence. Hong Kong In Hong Kong a computerized patient record system called the Clinical Management System (CMS) has been developed by the Hospital Authority since 1994. This system has been deployed at all the sites of the Authority (40 hospitals and 120 clinics), and is used by all 30,000 clinical staff on a daily basis, with a daily transaction of up to 2 millions. The comprehensive records of 7 million patients are available on-line in the Electronic Patient Record (ePR), with data integrated from all sites. Since 2004 radiology image viewing has been added to the ePR, with radiography images from any HA site being available as part of the ePR. The Hong Kong Hospital Authority placed particular attention to the governance of clinical systems development, with input from hundreds of clinicians being incorporated through a structured process. The Health Informatics Section in Hong Kong Hospital Authority[13] has close relationship with Information Technology Department and clinicians to develop healthcare systems for the organization to support the service to all public hospitals and clinics in the region. The Hong Kong Society of Medical Informatics (HKSMI) was established in 1987 to promote the use of information technology in healthcare. The eHealth Consortium has been formed to bring together clinicians from both the private and public sectors, medical informatics professionals and the IT industry to further promote IT in healthcare in Hong Kong.[14] New Zealand Health Informatics is taught at five New Zealand universities. The most mature and established is the Otago programme which has been offered for over a decade. Health Informatics New Zealand (HINZ), is the national organisation that advocates for Health Informatics. HINZ organises a conference every year and also publishes an online journal- Healthcare Informatics Review Online. Saudi Arabia The Saudi Association for Health Information (SAHI) was established in 2006 to work under direct supervision of King Saud bin Abdulaziz University for Health Sciences to practice public activities, develop theoretical and applicable knowledge, and provide scientific and applicable studies.
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Health Informatics Law Health informatics law deals with evolving and sometimes complex legal principles as they apply to information technology in health-related fields. It addresses the privacy, ethical and operational issues that invariably arise when electronic tools, information and media are used in health care delivery. Health Informatics Law also applies to all matters that involve information technology, health care and the interaction of information. It deals with the circumstances under which data and records are shared with other fields or areas that support and enhance patient care. As many healthcare systems are making an effort to have patient records more readily available to them via the internet, it is important that providers be sure that there are a few security standards in place in order to make sure that the patients information is safe. They have to be able to assure confidentiality and the security of the people, process, and technology. Since there is also the possibility of payments being made through this system, it is vital that this aspect of their private information will also be protected through cryptography.
History World wide use of computer technology in medicine began in the early 1950s with the rise of the computers. In 1949, Gustav Wagner established the first professional organization for informatics in Germany. The prehistory, history, and future of medical information and health information technology are discussed in reference. Specialized university departments and Informatics training programs began during the 1960s in France, Germany, Belgium and The Netherlands. Medical informatics research units began to appear during the 1970s in Poland and in the U.S. Since then the development of high-quality health informatics research, education and infrastructure has been a goal of the U.S. and the European Union. Early names for health informatics included medical computing, biomedical computing, medical computer science, computer medicine, medical electronic data processing, medical automatic data processing, medical information processing, medical information science, medical software engineering, and medical computer technology.[citation needed]
The health informatics community is still growing, it is by no means a mature profession, but work in the UK by the voluntary registration body, the UK Council of Health Informatics Professions has suggested eight key constituencies within the domain - information management, knowledge management, portfolio/programme/project management, ICT, education and research, clinical informatics, health records(service and business-related), health informatics service management. These constituencies accommodate professionals in and for the NHS, in academia and commercial service and solution providers. Since the 1970s the most prominent international coordinating body has been the International Medical Informatics Association (IMIA).
References [1] Richesson, Rachel L., and James E. Andrews. 2012.Clinical research informatics. London: Springer. [2] MGH - Laboratory of Computer Science (http:/ / www. lcs. mgh. harvard. edu/ ) [3] Certification Commission for Healthcare Information Technology (July 18, 2006): CCHIT Announces First Certified Electronic Health Record Products (http:/ / www. cchit. org/ media/ press+ releases/ CCHIT+ Announces+ First+ Certified+ Electronic+ Health+ Record+ Products. htm). Retrieved July 26, 2006. [4] European eHealth Action Plan (http:/ / ec. europa. eu/ information_society/ activities/ health/ policy_action_plan/ index_en. htm) [5] European eHealth Action Plan i2010 (http:/ / ec. europa. eu/ information_society/ eeurope/ i2010/ index_en. htm) [6] Cordis FP5web (http:/ / cordis. europa. eu/ fp5/ ) [7] European Patient Smart Open Services (http:/ / www. epsos. eu) [8] Holzinger, A. (2012). Biomedical Informatics, Lecture Notes to LV 444.152. Books on Demand, ISBN 978-3-8482-2219-3. [9] Australasian College of Health Informatics [10] ACHI Memberships (http:/ / www. ACHI. org. au) ACHI memberships: Professions Australia
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Clinical Informatics [11] eJHI - electronic Journal of Health Informatics (http:/ / www. ejhi. net/ ojs/ index. php/ ejhi/ about/ journalSponsorship) (open access journal) [12] Australian Health Informatics Education Council (AHIEC) (http:/ / www. AHIEC. org. au) AHIEC Auspicing Organisations [13] Health Informatics Section in Hong Kong Hospital Authority (http:/ / www. ha. org. hk/ hi/ Welcome. html) [14] eHealth Consortium (http:/ / www. iproa. org/ ProjectDetail. action?id=270)
External links • • • •
Health informatics (http://www.dmoz.org/Health/Medicine/Informatics/) at the Open Directory Project e-Journal for Health Informatics (http://www.eJHI.net) Article about informatics (http://www.biomedcentral.com/1472-6947/9/24) Willison, Brian. Advancing Meaningful Use: Simplifying Complex Clinical Metrics Through Visual Representation. (http://piim.newschool.edu/_media/pdfs/PIIM-RESEARCH_AdvancingMeaningfulUse.pdf) • Clinfowiki (http://www.clinfowiki.org/) • Global Health Informatics Partnership (http://www.ghip.net/)
Cybermedicine eHealth (also written e-health) is a relatively recent term for healthcare practice supported by electronic processes and communication, dating back to at least 1999. Usage of the term varies: some would argue it is interchangeable with health informatics with a broad definition covering electronic/digital processes in health while others use it in the narrower sense of healthcare practice using the Internet.[1][2][3] It can also include health applications and links on mobile phones, referred to as m-health or mHealth. Since about 2011, the increasing recognition of the need for better cyber-security and regulation may result in the need for these specialized resources to develop safer eHealth solutions that can withstand these growing threats.
Forms of e-health The term can encompass a range of services or systems that are at the edge of medicine/healthcare and information technology, including: • Electronic health records: enabling the communication of patient data between different healthcare professionals (GPs, specialists etc.); • ePrescribing: access to prescribing options,printing prescriptions to patients and sometimes electronic transmission of prescriptions from doctors to pharmacists • Telemedicine: physical and psychological treatments at a distance, including telemonitoring of patients functions; • Consumer health informatics: use of electronic resources on medical topics by healthy individuals or patients; • Health knowledge management: e.g. in an overview of latest medical journals, best practice guidelines or epidemiological tracking (examples include physician resources such as Medscape and MDLinx); • Virtual healthcare teams: consisting of healthcare professionals who collaborate and share information on patients through digital equipment (for transmural care); • mHealth or m-Health: includes the use of mobile devices in collecting aggregate and patient level health data, providing healthcare information to practitioners, researchers, and patients, real-time monitoring of patient vitals, and direct provision of care (via mobile telemedicine); • Medical research using Grids: powerful computing and data management capabilities to handle large amounts of heterogeneous data.[4] • Healthcare Information Systems: also often refer to software solutions for appointment scheduling, patient data management, work schedule management and other administrative tasks surrounding health
29
Cybermedicine
Contested definition Several authors have noted the variable usage in the term, from being specific to the use of the Internet in healthcare to being generally around any use of computers in healthcare. Various authors have considered the evolution of the term and its usage and how this maps to changes in health informatics and healthcare generally. Oh et al., in a 2005 systematic review of the term's usage, offered the definition of eHealth as a set of technological themes in health today, more specifically based on commerce, activities, stakeholders, outcomes, locations, or perspectives. One thing that all sources seem to agree on is that e-Health initiatives do not originate with the patient, though the patient may be a member of a patient organization that seeks to do this (see e-Patient).
E-Health data exchange One of the factors blocking the use of e-Health tools from widespread acceptance is the concern about privacy issues regarding patient records, most specifically the EPR (Electronic patient record). This main concern has to do with the confidentiality of the data. There is also concern about non-confidential data however. Each medical practise has its own jargon and diagnostic tools. To standardize the exchange of information, various coding schemes may be used in combination with international medical standards. Of the forms of e-Health already mentioned, there are roughly two types; front-end data exchange and back-end exchange. Front-end exchange typically involves the patient, while back-end exchange does not. A common example of a rather simple front-end exchange is a patient sending a photo taken by mobile phone of a healing wound and sending it by email to the family doctor for control. Such an actions may avoid the cost of an expensive visit to the hospital. A common example of a back-end exchange is when a patient on vacation visits a doctor who then may request access to the patient's health records, such as medicine prescriptions, x-ray photographs, or blood test results. Such an action may reveal allergies or other prior conditions that are relevant to the visit.
Thesaurus Successful e-Health initiatives such as e-Diabetes have shown that for data exchange to be facilitated either at the front-end or the back-end, a common thesaurus is needed for terms of reference. Various medical practises in chronic patient care (such as for diabetic patients) already have a well defined set of terms and actions, which makes standard communication exchange easier, whether the exchange is initiated by the patient or the caregiver. In general, explanatory diagnostic information (such as the standard ICD-10) may be exchanged insecurely, and private information (such as personal information from the patient) must be secured. E-health manages both flows of information, while ensuring the quality of the data exchange.
Early adopters Chronic patients over time often acquire a high level of knowledge about the processes involved in their own care, and often develop a routine in coping with their condition. For these types of routine patients, front-end e-Health solutions tend to be relatively easy to implement.
E-Mental Health E-mental health is frequently used to refer to internet based interventions and support for mental health conditions.[5] However, it can also refer to the use of information and communication technologies that also includes the use of social media, landline and mobile phones.[6] E-mental health services can include information; peer support services, computer and internet based programs, virtual applications and games as well as real time interaction with trained clinicians.[7] Programs can also be delivered using telephones and interactive voice response (IVR) [8]
30
Cybermedicine Mental disorders includes a range of conditions such as alcohol and drug use disorders, mood disorders such as depression, dementia and Alzheimer’s disease, delusional disorders such as schizophrenia and anxiety disorders.[9] The majority of e-mental health interventions have focused on the treatment of depression and anxiety. There are, however, programs also for problems as diverse as smoking cessation [10] gambling [11] and post-disaster mental health.[12]
Advantages and Disadvantages E-mental health has a number of advantages such as being low cost, easily accessible and providing anonymity to users.[13] However, there are also a number of disadvantages such as concerns regarding user privacy and confidentiality. Online security involves the implementation of appropriate safeguards to protect user privacy and confidentiality. This includes appropriate collection and handling of user data, the protection of data from unauthorized access and modification and the safe storage of data.[14] E-mental health has been gaining momentum in the academic research as well as practical arenas in a wide variety of disciplines such as psychology, clinical social work, family and marriage therapy, and mental health counseling. Testifying to this momentum, the E-Mental Health movement has its own international organization, The International Society for Mental Health Online.[15] It also has its own academic peer review journals, such as the Journal of Medical Internet Research.[16]
Programs There are at least four programs currently available to treat anxiety and depression. Two programs have been identified by the UK National Institute for Clinical Excellence [17] as cost effective for use in primary care. The first is Fearfighter[18] which is a text based CBT program to treat people with phobias and the second is Beating the Blues,[19] an interactive text, cartoon and video CBT program for anxiety and depression. Two programs have been supported for use in primary care by the Australian Government. The first is Anxiety Online,[20] a text based program for the anxiety, depressive and eating disorders, and the second is This Way Up,[21] a set of interactive text, cartoon and video programs for the anxiety and depressive disorders. There are a number of online programs relating to smoking cessation. QuitCoach[22] is a personalised quit plan based on the users response to questions regarding giving up smoking and tailored individually each time the user logs in to the site. Freedom From Smoking[23] takes users through lessons that are grouped into modules that provide information and assignments to complete. The modules guide participants through steps such as preparing to quit smoking, stopping smoking and preventing relapse. Other internet programs have been developed specifically as part of research into treatment for specific disorders. For example, an online self-directed therapy for problem gambling was developed to specifically test this as a method of treatment.[24] All participants were given access to a website. The treatment group was provided with behavioural and cognitive strategies to reduce or quit gambling. This was presented in the form of a workbook which encouraged participants to self-monitor their gambling by maintaining an online log of gambling and gambling urges. Participants could also use a smartphone application to collect self-monitoring information. Finally participants could also choose to receive motivational email or text reminders of their progress and goals. An internet based intervention was also developed for use after Hurricane Ike in 2009.[25] During this study, 1,249 disaster-affected adults were randomly recruited to take part in the intervention. Participants were given a structured interview then invited to access the web intervention using a unique password. Access to the website was provided for a four month period. As participants accessed the site they were randomly assigned to either the intervention. those assigned to the intervention were provided with modules consisting of information regarding effective coping strategies to manage mental health and health risk behaviour.
31
Cybermedicine
Cybermedicine Cybermedicine is the use of the Internet to deliver medical services, such as medical consultations and drug prescriptions. It is the successor to telemedicine, wherein doctors would consult and treat patients remotely via telephone or fax. Cybermedicine is already being used in small projects where images are transmitted from a primary care setting to a medical specialist, who comments on the case and suggests which intervention might benefit the patient. A field that lends itself to this approach is dermatology, where images of an eruption are communicated to a hospital specialist who determines if referral is necessary. A Cyber Doctor,[26] known in the UK as a Cyber Physician,[27] is a medical professional who does consultation via the internet, treating virtual patients, who may never meet face to face. This is a new area of medicine which has been utilized by the armed forces and teaching hospitals offering online consultation to patients before making their decision to travel for unique medical treatment only offered at a particular medical facility.[28]
Notes [1] "HIMSS SIG develops proposed e-health definition", HIMSS News, 13(7): 12 [2] Eysenbach G, Diepgen TL. The role of e-health and consumer health informatics for evidence-based patient choice in the 21st century. Clin Dermatol. 2001 Jan-Feb;19(1):11-7 [3] Ball MJ, Lillis J. E-health: transforming the physician/patient relationship. Int J Med Inform. 2001 Apr;61(1):1-10 [4] Jochen Fingberg, Marit Hansen et al.: Integrating Data Custodians in eHealth Grids – Security and Privacy Aspects (http:/ / www. ccrl-nece. de/ publications/ paper/ public/ LR-06-262. pdf), NEC Lab Report, 2006 [5] Bennett, K., Reynolds, J., Christensen, H., & Griffiths, K.M. (2010) e-hub: an online self-help mental health service in the community. "Medical Journal of Australia", 192(11) S48-S52. [6] The NHS Confederation (2013) "E-Mental Health: what’s all the fuss about?" London, UK. [7] Australian Government (2012) "E-Mental Health Strategy for Australia." Canberra, Australia. [8] National Institute for Health & Clinical Excellence (2008) "Computerised cognitive behaviour therapy for depression and anxiety." London, UK. [9] American Psychiatric Association (2000) "Diagnostic and Statistical Manual of Mental Disorders." Eigal Meirovich, Baltimore, US. [10] Civljak, M., Sheikh, A., Stead, L.F. & Car, J. (2012) Internet-based interventions for smoking cessation. "Cochrane Database of Systematic Reviews" 2010, (9) Art. No:CD007078. Retrieved 21st April, 2013, from The Cochrane Library Database. [11] Hodgins, D.C. , Fick, G.H., Murray, R. & Cunningham, J.A.(2013) Internet-based interventions for disordered gamblers: study protocol for a randomized controlled trial of online self-directed cognitive-behavioural motivational therapy "BMC Public Health" (13) 10. [12] Ruggiero, K.J., Resnick, H. s., Paul, L.A., Gros, K., McCauley, J.L., Acierno, R., Morgan, M. & Galea, S. (2012) Randomized Controlled Trial of an Internet-Based Intervention Using Random-Digit-Dial Recruitment: "The Disaster Recovery" Web Project "Contemporary Clinical Trials" 33 (1) 237-246. [13] Andrews G. & Titov, N. (2010) Treating people you never see: internet-based treatment of the internalising mental disorders, "Australian Health Review," 34,2 pg 144-147. [14] Bennett K., Bennett A.J. & Griffiths K.M. (2010) Security Considerations for E-Mental Health Interventions "Journal of Medical Internet Research" 12(5):e61 [15] http:/ / www. ismho. org/ home. asp [16] http:/ / www. jmir. org/ [17] http:/ / www. nice. org. uk/ [18] http:/ / www. fearfighter. com/ [19] http:/ / www. beatingtheblues. co. uk/ [20] http:/ / www. anxietyonline. org. au/ [21] http:/ / thiswayup. org. au [22] http:/ / www. quitcoach. org. au/ [23] http:/ / www. ffsonline. org/ [24] Hodgins, D.C. , Fick, G.H., Murray, R. & Cunningham, J.A.(2013) Internet-based interventions for disordered gamblers: study protocol for a randomized controlled trial of online self-directed cognitive-behavioural motivational therapy "BMC Public Health" (13) 10. [25] Ruggiero, K.J., Resnick, H. s., Paul, L.A., Gros, K., McCauley, J.L., Acierno, R., Morgan, M. & Galea, S. (2012) Randomized Controlled Trial of an Internet-Based Intervention Using Random-Digit-Dial Recruitment: "The Disaster Recovery" Web Project "Contemporary Clinical Trials" 33 (1) 237-246. [26] US term,http:/ / www. sfgate. com/ cgi-bin/ article. cgi?f=/ c/ a/ 2007/ 05/ 27/ BAGOKQ2IEQ1. DTL [27] UK term,http:/ / society. guardian. co. uk/ health/ story/ 0,,408225,00. html
32
Cybermedicine [28] Online visits a boon for far-off patients, Sfgate.com,http:/ / www. sfgate. com/ cgi-bin/ article. cgi?f=/ c/ a/ 2007/ 05/ 27/ BAGOKQ2IEQ1. DTL
Further reading (M = Medline, W = Wilson Business Abstracts, G = Google) • 1999 Mitchell (G) A new term is needed to refer to the combined use of electronic communication and information technology in the health sector. The use in the health sector of digital data – transmitted, stored and retrieved electronically – for clinical, educational and administrative purposes, both at the local site and at a distance. • 2000 JHITA (G) Internet-related healthcare activities • 2000 McLendon (M) Ehealth refers to all forms of electronic healthcare delivered over the Internet, ranging from informational, educational and commercial "products" to direct services offered by professionals, non-professionals, businesses or consumers themselves. Ehealth includes a wide variety of the clinical activities that have traditionally characterized telehealth, but delivered through the Internet. Simply stated, Ehealth is making healthcare more efficient, while allowing patients and professionals to do the previously impossible. • 2000 DeLuca, Enmark - Frontiers of Medicine (W) (M) E-health is the embryonic convergence of wide-reaching technologies like the Internet, computer telephony/interactive voice response, wireless communications, and direct access to healthcare providers, care management, education, and wellness. • 2000 Pretlow (G) E-health is the process of providing health care via electronic means, in particular over the Internet. It can include teaching, monitoring ( e.g. physiologic data), and interaction with health care providers, as well as interaction with other patients afflicted with the same conditions. • 2001 Eysenbach (M) e-health is an emerging field in the intersection of medical informatics, public health and business, referring to health services and information delivered or enhanced through the Internet and related technologies. In a broader sense, the term characterizes not only a technical development, but also a state-of-mind, a way of thinking, an attitude, and a commitment for networked, global thinking, to improve health care locally, regionally, and worldwide by using information and communication technology • 2001 Strategic Health Innovations (G) The use of information technology in the delivery of health care. • 2001 Robert J Wood Foundation (G) EHealth is the use of emerging information and communication technology, especially the Internet, to improve or enable health and health care. • 2001 Ontario Hospital eHealth Council (G) EHealth is a consumer-centred model of health care where stakeholders collaborate utilizing ICTs including Internet technologies to manage health, arrange, deliver, and account for care, and manage the health care system. • 2003 COACH (G) The leveraging of the information and communication technology (ICT) to connect provider and patients and governments; to educate and inform health care professionals, managers and consumers; to stimulate innovation in care delivery and health system management; and, to improve our health care system. • 2003 eEurope - eHealth2003 (G) The application of information and communication technologies (ICT) across the whole range of functions which one way or another, affect the health of citizens and patients. • 2003 Regional Office for the Eastern Mediterranean - World Health Organization (G) E-health is a new term for the combined use of electronic communication and information technology in the health sector OR is the use, in the health sector, of digital data-transmitted, stored and retrieved electronically-for clinical, educational and administrative purposes, both at the local site and at a distance • 2003 HMS Europe (G) The practice of leveraging the Internet to connect caregivers, healthcare systems and hospitals with consumers • The Medicalisation of Cyberspace, by Dr Andy Miah & Dr Emma Rich (http://cybermedicine.blogspot.com) • Cybermedicine by Warner V. Slack, Jossey Bass publisher Second Edition (http://www.amazon.com/dp/ 0787956317)
33
EHealth
EHealth eHealth (also written e-health) is a relatively recent term for healthcare practice supported by electronic processes and communication, dating back to at least 1999. Usage of the term varies: some would argue it is interchangeable with health informatics with a broad definition covering electronic/digital processes in health while others use it in the narrower sense of healthcare practice using the Internet.[1][2][3] It can also include health applications and links on mobile phones, referred to as m-health or mHealth. Since about 2011, the increasing recognition of the need for better cyber-security and regulation may result in the need for these specialized resources to develop safer eHealth solutions that can withstand these growing threats.
Forms of e-health The term can encompass a range of services or systems that are at the edge of medicine/healthcare and information technology, including: • Electronic health records: enabling the communication of patient data between different healthcare professionals (GPs, specialists etc.); • ePrescribing: access to prescribing options,printing prescriptions to patients and sometimes electronic transmission of prescriptions from doctors to pharmacists • Telemedicine: physical and psychological treatments at a distance, including telemonitoring of patients functions; • Consumer health informatics: use of electronic resources on medical topics by healthy individuals or patients; • Health knowledge management: e.g. in an overview of latest medical journals, best practice guidelines or epidemiological tracking (examples include physician resources such as Medscape and MDLinx); • Virtual healthcare teams: consisting of healthcare professionals who collaborate and share information on patients through digital equipment (for transmural care); • mHealth or m-Health: includes the use of mobile devices in collecting aggregate and patient level health data, providing healthcare information to practitioners, researchers, and patients, real-time monitoring of patient vitals, and direct provision of care (via mobile telemedicine); • Medical research using Grids: powerful computing and data management capabilities to handle large amounts of heterogeneous data.[4] • Healthcare Information Systems: also often refer to software solutions for appointment scheduling, patient data management, work schedule management and other administrative tasks surrounding health
Contested definition Several authors have noted the variable usage in the term, from being specific to the use of the Internet in healthcare to being generally around any use of computers in healthcare. Various authors have considered the evolution of the term and its usage and how this maps to changes in health informatics and healthcare generally. Oh et al., in a 2005 systematic review of the term's usage, offered the definition of eHealth as a set of technological themes in health today, more specifically based on commerce, activities, stakeholders, outcomes, locations, or perspectives. One thing that all sources seem to agree on is that e-Health initiatives do not originate with the patient, though the patient may be a member of a patient organization that seeks to do this (see e-Patient).
34
EHealth
E-Health data exchange One of the factors blocking the use of e-Health tools from widespread acceptance is the concern about privacy issues regarding patient records, most specifically the EPR (Electronic patient record). This main concern has to do with the confidentiality of the data. There is also concern about non-confidential data however. Each medical practise has its own jargon and diagnostic tools. To standardize the exchange of information, various coding schemes may be used in combination with international medical standards. Of the forms of e-Health already mentioned, there are roughly two types; front-end data exchange and back-end exchange. Front-end exchange typically involves the patient, while back-end exchange does not. A common example of a rather simple front-end exchange is a patient sending a photo taken by mobile phone of a healing wound and sending it by email to the family doctor for control. Such an actions may avoid the cost of an expensive visit to the hospital. A common example of a back-end exchange is when a patient on vacation visits a doctor who then may request access to the patient's health records, such as medicine prescriptions, x-ray photographs, or blood test results. Such an action may reveal allergies or other prior conditions that are relevant to the visit.
Thesaurus Successful e-Health initiatives such as e-Diabetes have shown that for data exchange to be facilitated either at the front-end or the back-end, a common thesaurus is needed for terms of reference. Various medical practises in chronic patient care (such as for diabetic patients) already have a well defined set of terms and actions, which makes standard communication exchange easier, whether the exchange is initiated by the patient or the caregiver. In general, explanatory diagnostic information (such as the standard ICD-10) may be exchanged insecurely, and private information (such as personal information from the patient) must be secured. E-health manages both flows of information, while ensuring the quality of the data exchange.
Early adopters Chronic patients over time often acquire a high level of knowledge about the processes involved in their own care, and often develop a routine in coping with their condition. For these types of routine patients, front-end e-Health solutions tend to be relatively easy to implement.
E-Mental Health E-mental health is frequently used to refer to internet based interventions and support for mental health conditions.[5] However, it can also refer to the use of information and communication technologies that also includes the use of social media, landline and mobile phones.[6] E-mental health services can include information; peer support services, computer and internet based programs, virtual applications and games as well as real time interaction with trained clinicians.[7] Programs can also be delivered using telephones and interactive voice response (IVR) [8] Mental disorders includes a range of conditions such as alcohol and drug use disorders, mood disorders such as depression, dementia and Alzheimer’s disease, delusional disorders such as schizophrenia and anxiety disorders.[9] The majority of e-mental health interventions have focused on the treatment of depression and anxiety. There are, however, programs also for problems as diverse as smoking cessation [10] gambling [11] and post-disaster mental health.[12]
35
EHealth
Advantages and Disadvantages E-mental health has a number of advantages such as being low cost, easily accessible and providing anonymity to users.[13] However, there are also a number of disadvantages such as concerns regarding user privacy and confidentiality. Online security involves the implementation of appropriate safeguards to protect user privacy and confidentiality. This includes appropriate collection and handling of user data, the protection of data from unauthorized access and modification and the safe storage of data.[14] E-mental health has been gaining momentum in the academic research as well as practical arenas in a wide variety of disciplines such as psychology, clinical social work, family and marriage therapy, and mental health counseling. Testifying to this momentum, the E-Mental Health movement has its own international organization, The International Society for Mental Health Online.[15] It also has its own academic peer review journals, such as the Journal of Medical Internet Research.[16]
Programs There are at least four programs currently available to treat anxiety and depression. Two programs have been identified by the UK National Institute for Clinical Excellence [17] as cost effective for use in primary care. The first is Fearfighter[18] which is a text based CBT program to treat people with phobias and the second is Beating the Blues,[19] an interactive text, cartoon and video CBT program for anxiety and depression. Two programs have been supported for use in primary care by the Australian Government. The first is Anxiety Online,[20] a text based program for the anxiety, depressive and eating disorders, and the second is This Way Up,[21] a set of interactive text, cartoon and video programs for the anxiety and depressive disorders. There are a number of online programs relating to smoking cessation. QuitCoach[22] is a personalised quit plan based on the users response to questions regarding giving up smoking and tailored individually each time the user logs in to the site. Freedom From Smoking[23] takes users through lessons that are grouped into modules that provide information and assignments to complete. The modules guide participants through steps such as preparing to quit smoking, stopping smoking and preventing relapse. Other internet programs have been developed specifically as part of research into treatment for specific disorders. For example, an online self-directed therapy for problem gambling was developed to specifically test this as a method of treatment.[24] All participants were given access to a website. The treatment group was provided with behavioural and cognitive strategies to reduce or quit gambling. This was presented in the form of a workbook which encouraged participants to self-monitor their gambling by maintaining an online log of gambling and gambling urges. Participants could also use a smartphone application to collect self-monitoring information. Finally participants could also choose to receive motivational email or text reminders of their progress and goals. An internet based intervention was also developed for use after Hurricane Ike in 2009.[25] During this study, 1,249 disaster-affected adults were randomly recruited to take part in the intervention. Participants were given a structured interview then invited to access the web intervention using a unique password. Access to the website was provided for a four month period. As participants accessed the site they were randomly assigned to either the intervention. those assigned to the intervention were provided with modules consisting of information regarding effective coping strategies to manage mental health and health risk behaviour.
36
EHealth
Cybermedicine Cybermedicine is the use of the Internet to deliver medical services, such as medical consultations and drug prescriptions. It is the successor to telemedicine, wherein doctors would consult and treat patients remotely via telephone or fax. Cybermedicine is already being used in small projects where images are transmitted from a primary care setting to a medical specialist, who comments on the case and suggests which intervention might benefit the patient. A field that lends itself to this approach is dermatology, where images of an eruption are communicated to a hospital specialist who determines if referral is necessary. A Cyber Doctor,[26] known in the UK as a Cyber Physician,[27] is a medical professional who does consultation via the internet, treating virtual patients, who may never meet face to face. This is a new area of medicine which has been utilized by the armed forces and teaching hospitals offering online consultation to patients before making their decision to travel for unique medical treatment only offered at a particular medical facility.[28]
Notes [1] "HIMSS SIG develops proposed e-health definition", HIMSS News, 13(7): 12 [2] Eysenbach G, Diepgen TL. The role of e-health and consumer health informatics for evidence-based patient choice in the 21st century. Clin Dermatol. 2001 Jan-Feb;19(1):11-7 [3] Ball MJ, Lillis J. E-health: transforming the physician/patient relationship. Int J Med Inform. 2001 Apr;61(1):1-10 [4] Jochen Fingberg, Marit Hansen et al.: Integrating Data Custodians in eHealth Grids – Security and Privacy Aspects (http:/ / www. ccrl-nece. de/ publications/ paper/ public/ LR-06-262. pdf), NEC Lab Report, 2006 [5] Bennett, K., Reynolds, J., Christensen, H., & Griffiths, K.M. (2010) e-hub: an online self-help mental health service in the community. "Medical Journal of Australia", 192(11) S48-S52. [6] The NHS Confederation (2013) "E-Mental Health: what’s all the fuss about?" London, UK. [7] Australian Government (2012) "E-Mental Health Strategy for Australia." Canberra, Australia. [8] National Institute for Health & Clinical Excellence (2008) "Computerised cognitive behaviour therapy for depression and anxiety." London, UK. [9] American Psychiatric Association (2000) "Diagnostic and Statistical Manual of Mental Disorders." Eigal Meirovich, Baltimore, US. [10] Civljak, M., Sheikh, A., Stead, L.F. & Car, J. (2012) Internet-based interventions for smoking cessation. "Cochrane Database of Systematic Reviews" 2010, (9) Art. No:CD007078. Retrieved 21st April, 2013, from The Cochrane Library Database. [11] Hodgins, D.C. , Fick, G.H., Murray, R. & Cunningham, J.A.(2013) Internet-based interventions for disordered gamblers: study protocol for a randomized controlled trial of online self-directed cognitive-behavioural motivational therapy "BMC Public Health" (13) 10. [12] Ruggiero, K.J., Resnick, H. s., Paul, L.A., Gros, K., McCauley, J.L., Acierno, R., Morgan, M. & Galea, S. (2012) Randomized Controlled Trial of an Internet-Based Intervention Using Random-Digit-Dial Recruitment: "The Disaster Recovery" Web Project "Contemporary Clinical Trials" 33 (1) 237-246. [13] Andrews G. & Titov, N. (2010) Treating people you never see: internet-based treatment of the internalising mental disorders, "Australian Health Review," 34,2 pg 144-147. [14] Bennett K., Bennett A.J. & Griffiths K.M. (2010) Security Considerations for E-Mental Health Interventions "Journal of Medical Internet Research" 12(5):e61 [15] http:/ / www. ismho. org/ home. asp [16] http:/ / www. jmir. org/ [17] http:/ / www. nice. org. uk/ [18] http:/ / www. fearfighter. com/ [19] http:/ / www. beatingtheblues. co. uk/ [20] http:/ / www. anxietyonline. org. au/ [21] http:/ / thiswayup. org. au [22] http:/ / www. quitcoach. org. au/ [23] http:/ / www. ffsonline. org/ [24] Hodgins, D.C. , Fick, G.H., Murray, R. & Cunningham, J.A.(2013) Internet-based interventions for disordered gamblers: study protocol for a randomized controlled trial of online self-directed cognitive-behavioural motivational therapy "BMC Public Health" (13) 10. [25] Ruggiero, K.J., Resnick, H. s., Paul, L.A., Gros, K., McCauley, J.L., Acierno, R., Morgan, M. & Galea, S. (2012) Randomized Controlled Trial of an Internet-Based Intervention Using Random-Digit-Dial Recruitment: "The Disaster Recovery" Web Project "Contemporary Clinical Trials" 33 (1) 237-246. [26] US term,http:/ / www. sfgate. com/ cgi-bin/ article. cgi?f=/ c/ a/ 2007/ 05/ 27/ BAGOKQ2IEQ1. DTL [27] UK term,http:/ / society. guardian. co. uk/ health/ story/ 0,,408225,00. html
37
EHealth [28] Online visits a boon for far-off patients, Sfgate.com,http:/ / www. sfgate. com/ cgi-bin/ article. cgi?f=/ c/ a/ 2007/ 05/ 27/ BAGOKQ2IEQ1. DTL
Further reading (M = Medline, W = Wilson Business Abstracts, G = Google) • 1999 Mitchell (G) A new term is needed to refer to the combined use of electronic communication and information technology in the health sector. The use in the health sector of digital data – transmitted, stored and retrieved electronically – for clinical, educational and administrative purposes, both at the local site and at a distance. • 2000 JHITA (G) Internet-related healthcare activities • 2000 McLendon (M) Ehealth refers to all forms of electronic healthcare delivered over the Internet, ranging from informational, educational and commercial "products" to direct services offered by professionals, non-professionals, businesses or consumers themselves. Ehealth includes a wide variety of the clinical activities that have traditionally characterized telehealth, but delivered through the Internet. Simply stated, Ehealth is making healthcare more efficient, while allowing patients and professionals to do the previously impossible. • 2000 DeLuca, Enmark - Frontiers of Medicine (W) (M) E-health is the embryonic convergence of wide-reaching technologies like the Internet, computer telephony/interactive voice response, wireless communications, and direct access to healthcare providers, care management, education, and wellness. • 2000 Pretlow (G) E-health is the process of providing health care via electronic means, in particular over the Internet. It can include teaching, monitoring ( e.g. physiologic data), and interaction with health care providers, as well as interaction with other patients afflicted with the same conditions. • 2001 Eysenbach (M) e-health is an emerging field in the intersection of medical informatics, public health and business, referring to health services and information delivered or enhanced through the Internet and related technologies. In a broader sense, the term characterizes not only a technical development, but also a state-of-mind, a way of thinking, an attitude, and a commitment for networked, global thinking, to improve health care locally, regionally, and worldwide by using information and communication technology • 2001 Strategic Health Innovations (G) The use of information technology in the delivery of health care. • 2001 Robert J Wood Foundation (G) EHealth is the use of emerging information and communication technology, especially the Internet, to improve or enable health and health care. • 2001 Ontario Hospital eHealth Council (G) EHealth is a consumer-centred model of health care where stakeholders collaborate utilizing ICTs including Internet technologies to manage health, arrange, deliver, and account for care, and manage the health care system. • 2003 COACH (G) The leveraging of the information and communication technology (ICT) to connect provider and patients and governments; to educate and inform health care professionals, managers and consumers; to stimulate innovation in care delivery and health system management; and, to improve our health care system. • 2003 eEurope - eHealth2003 (G) The application of information and communication technologies (ICT) across the whole range of functions which one way or another, affect the health of citizens and patients. • 2003 Regional Office for the Eastern Mediterranean - World Health Organization (G) E-health is a new term for the combined use of electronic communication and information technology in the health sector OR is the use, in the health sector, of digital data-transmitted, stored and retrieved electronically-for clinical, educational and administrative purposes, both at the local site and at a distance • 2003 HMS Europe (G) The practice of leveraging the Internet to connect caregivers, healthcare systems and hospitals with consumers • The Medicalisation of Cyberspace, by Dr Andy Miah & Dr Emma Rich (http://cybermedicine.blogspot.com) • Cybermedicine by Warner V. Slack, Jossey Bass publisher Second Edition (http://www.amazon.com/dp/ 0787956317)
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Health 2.0
Health 2.0 Health 2.0 (as well as the closely related concept of Medicine 2.0[1]) are terms representing the possibilities between health care, eHealth and Web 2.0, and has come into use after a recent spate of articles in newspapers, and by Physicians and Medical Librarians.[2][3] A concise definition of Health 2.0 is the use of a specific set of Web tools (blogs, Podcasts, tagging, search, wikis, etc) by actors in health care including doctors, patients, and scientists, using principles of open source and generation of content by users, and the power of networks in order to personalize health care, collaborate, and promote health education.[4] A possible explanation for the reason that Health has generated its own "2.0" term are its applications across health care in general, and in particular has a potential in public health promotion. One author describes the potential as "limitless.".[5] Another author interprets the "2.0" moniker as a "second generation medicine": "There is however also a broader idea behind Medicine 2.0 or “second generation medicine”: the notion that healthcare systems need to move away from hospital-based medicine, focus on promoting health, provide healthcare in people's own homes, and empower consumers to take responsibility for their own health". This is facilitated by emerging technologies, for example, a "combination of two trends—Personal Health Records combined with social networking —[which] may lead to a powerful new generation of health applications, where people share parts of their electronic health records with other consumers and “crowdsource” the collective wisdom of other patients and professionals."
Definitions and Inclusions The "2.0" moniker is associated with concepts like social networking, collaboration, openness, and participation. The "Traditional" definition focuses on technology as an enabler for care collaboration-"The use of social software and light-weight tools to promote collaboration between patients, their caregivers, medical professionals, and other stakeholders in health"[6] An expanded version of the traditional definition breaks this into components: 1. Personalized search that looks into the long tail, but cares about the user experience. 2. Communities that capture the accumulated knowledge of patients and caregivers; and clinicians—and explain it to the world, 3. Intelligent tools for content delivery—and transactions, and 4. Better integration of data with content. All with the result of patients increasingly guiding their own care[7] Scott Shreeve considers Health 2.0 as a wider system reform-"New concept of health care wherein all the constituents (patients, physicians, providers, and payers) focus on health care value (outcomes/price) and use competition at the medical condition level over the full cycle of care as the catalyst for improving the safety, efficiency, and quality of health care"[8] Then there's the concept of Health 2.0 as a participatory process between patient and clinician (with a couple of notable twists) -Health 2.0 defines the combination of health data and health information with (patient) experience through the use of ICT, enabling the citizen to become an active and responsible partner in his/her own health and care pathway.[9] Health 2.0 is participatory healthcare. Enabled by information, software, and community that we collect or create, we the patients can be effective partners in our own healthcare, and we the people can participate in reshaping the health system itself.[10] Definitions of Medicine 2.0 appear to be very similar but typically include more scientific and research aspects—Medicine 2.0: "Medicine 2.0 applications, services and tools are Web-based services for health care consumers, caregivers, patients, health professionals, and biomedical researchers, that use Web 2.0 technologies as well as semantic web and virtual reality tools, to enable and facilitate specifically social networking, participation, apomediation, collaboration, and openness within and between these user groups.[11][12] Published in JMIR Tom Van de Belt, Lucien Engelen et al. systematic review found 46 (!) unique definitions of health 2.0[13]
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Health 2.0 Health 2.0 is evolving quickly as the technology landscape changes along with the desire by healthcare professionals and by patients to embrace new technology and new services. However, already there are signs of Health 3.0 emerging. Health 3.0 [14] is defined as delivery of healthcare which leverages the use of elements of Semantic Web such as location awareness, the emerging Internet of Things and embedded sensors. Doctors 2.0 [15] are also leveraging social media as a powerful tool. Dedicated social networking sites for doctors like Sermo, SocialMD, Ozmosis etc. are doctor-only social networks. Here the doctors get a chance to interact and share knowledge with other doctors. Doctors are entering into the field of blogging, where they share their experiences in the form of case studies, give insight about diseases, discuss common healthcare issues, and offer simple remedies for them.
Overview Health 2.0 refers to a number of related concepts including telemedicine, electronic medical records, mHealth, Connected Health, and the use of the internet by patients themselves such as through messageboards, blogs, and other more advanced systems. A key concept is that patients themselves should have greater insight and control into information generated about them. Traditional models of medicine had patient records (held on paper or a proprietary computer A model of Health 2.0 system) that could only be accessed by a physician or other medical professional. Physicians acted as gatekeepers to this information, telling patients test results when and if they deemed necessary. Such a model operates relatively well in situations such as acute care, where information about specific blood results would be of little use to a lay person, or in general practice where results were generally benign. However, in the case of complex chronic diseases, psychiatric disorders, or diseases of unknown etiology patients were at risk of being left without well-coordinated care because data about them was stored in a variety of disparate places and in some cases might contain the opinions of healthcare professionals which were not to be shared with the patient. Increasingly, medical ethics considers such actions to be medical paternalism and are discouraged in modern medicine. A hypothetical example demonstrates the increased engagement of a patient operating in a Health 2.0 setting: A patient goes to see their primary care physician with a presenting complaint, having first ensured his own medical record was up to date via the internet. The treating physician might make a diagnosis or send for tests, the results of which could be transmitted direct to the patient's electronic medical record. If a second appointment is needed the patient will have had time to research what the results might mean for them, what diagnoses may be likely, and may have communicated with other patients who have had a similar set of results in the past. On a second visit a referral might be made to a specialist. The patient might have the opportunity to search for the views of other patients on the best specialist to go to, and in combination with their primary care physician decides who to see. The specialist gives a diagnosis along with a prognosis and potential options for treatment. The patient has the opportunity to research these treatment options and take a more proactive role in coming to a joint decision with their healthcare provider. They can also choose to submit more data about themselves, such as through a personalized genomics service to identify any risk factors that might improve or worsen their prognosis. As treatment commences, the patient can track their health outcomes through a data-sharing patient community to determine whether the treatment is having an effect for them, and can stay up to date on research opportunities and clinical trials for their condition. They also have the social support of communicating with other patients diagnosed with the same condition throughout the world.
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Health 2.0
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Level of use of Web 2.0 in Health Care Partly due to weak definitions, the novelty of the endeavor, and as an entrepreneurial (rather than academic) movement, little empirical evidence exists to understand how much Web 2.0 is being used in general. While it has been estimated that nearly one-third of the 100m Americans who have looked for health information online say that they or people they know have been significantly helped by what they found.,[16] this study considers only the broader use of the Internet for health management. A study examining physician practice has suggested that a segment of 245,000 physicians in the U.S are using Web 2.0 for their practice, indicating that use is beyond the stage of the early adopter with regard to physicians and Web 2.0.[17]
Types of Web 2.0 technology in Health Care Web 2.0 is commonly associated with technologies such as weblogs (health blogs), social bookmarking, wikis, podcasts, RSS feeds (and other forms of many-to-many publishing), social software, and web application programming interfaces (APIs) (see main article Web 2.0).
Types of Web 2.0 use in Health Care The following are examples of uses that have been documented in academic literature. Purpose
Description
Case example in academic literature
Users
Staying informed Used to stay informed of latest developments in a particular field
[18] RSS, Podcasts and search tools
All (medical professionals and public)
Medical education
Use for professional development for doctors, and public health promotion for by public health professionals and the general public
How podcasts can be used on the move to increase total [19] available educational time or the many applications of [20] these tools to public health
All (medical professionals and public)
Collaboration and practice
Web 2.0 tools use in daily practice for medical professionals to find information and make decisions
Google searches revealed the correct diagnosis in 15 out of 26 cases (58%, 95% confidence interval 38% to 77%) in a 2005 [21] study
Doctors, Nurses
Managing a Patients who use search tools to find out particular disease information about a particular condition
Sharing data for research
Shown that patients have different patterns of usage depending Public on if they are newly diagnosed or managing a severe long-term illness. Long-term patients are more likely to [22] connect to a community in Health 2.0
Completing patient-reported outcomes and Disease specific communities for patients with rare conditions aggregating the data for personal and aggregate data on treatments, symptoms, and outcomes to scientific research improve their decision making ability and carry out scientific [23] research such as observational trials
All (medical professionals and public)
Criticism of the use of Web 2.0 in health Several criticisms have been raised in the use of Web 2.0 in health. Firstly, the limitations for Medical Doctors (MDs) to use Google as a diagnostic tool, which may be more effective only for conditions with unique symptoms and signs that can easily be used as search term. Secondly, long-held concerns exist about the effects of patients obtaining information online, such as the idea that patients may delay seeking medical advice.[24] Finally concerns exist about the quality of user generated content leading to misinformation, though one study has suggested that in certain support groups only 6% of information is factually wrong and that only 3% reported that online advice had caused serious harm.[25] Other venues of information are likely to be less useful to the general public.
Health 2.0
Tensions in Health 2.0 Hughes et al. (2009) argue there are four major tensions represent in the literature on Health/Medicine 2.0: these are over the lack of clear definitions; issues around the loss of control over information that doctors perceive; safety and the dangers of inaccurate information; and issues of ownership and privacy.
Conferences and Trademarks • Medicine 2.0 [26] is an annual conference with a focus on the science and evidence behind Health 2.0. Medicine 2.0 is a registered trademark [27] of JMIR Publications, the producer of the conference and publisher of the leading peer-reviewed ehealth journal Journal of Medical Internet Research • Health 2.0 is a conference with a focus on the business of Health 2.0. Health 2.0 is a registered trademark [28] of Matthew Holt, the producer of that conference • Doctors 2.0 & You [29] is an annual international conference in Paris, dedicated to web 2.0, social media, and mobile applications, with a focus on disease conditions. Doctors 2.0 is a registered trademark [30] of Basil Strategies [31], conference producers.
References [1] Eysenbach G Medicine 2.0: Social Networking, Collaboration, Participation, Apomediation, and Openness (http:/ / www. jmir. org/ 2008/ 3/ e22/ ). J Med Internet Res 2008;10(3):e22 [2] Economist, The. 2007. Health 2.0 : Technology and society: Is the outbreak of cancer videos, bulimia blogs and other forms of “user generated” medical information a healthy trend? The Economist, September 6: 73-74 [3] Giustini, D. 2006. How Web 2.0 is changing medicine: Editorial. British Medical Journal, 333:1283-1284 [4] Hughes B, Joshi I, Wareham J. Health 2.0 and Medicine 2.0: Tensions and Controversies in the Field (http:/ / www. jmir. org/ 2008/ 3/ e23/ ), Journal of Medical Internet Research, 10(3): e23 [5] Crespo, R. 2007. Virtual Community Health Promotion. Preventing Chronic Disease, 4(3) : 75 [6] Adapted from Jane Sarasohn-Kahn's "Wisdom of Patients" report, by Matthew Holt, Last updated June 6, 2008 [7] "Holt's evolving view of a moving target", by Matthew Holt, updated from original September 20, 2007 presentation at Health 2.0 Conference, October 22, 2008 [8] Last updated on May 25, 2007 Scott Shreeve, MD - January 24, 2007) [9] "Patient 2.0 Empowerment", Lodewijk Bos, Andy Marsh, Denis Carroll, Sanjeev Gupta, Mike Rees, Proceedings of the 2008 International Conference on Semantic Web & Web Services SWWS08, Hamid R. Arabnia, Andy Marsh (eds), pp.164–167, 2008, http:/ / www. icmcc. org/ pdf/ ICMCCSWWS08. pdf [10] Ted Eytan MD, June 6, 2008, http:/ / www. tedeytan. com/ 2008/ 06/ 13/ 1089 [11] Eysenbach, Gunther. Medicine 2.0 Congress Website launched (and: Definition of Medicine 2.0 / Health 2.0) [12] Gunther Eysenbach's random research rants (Blog). URL: http:/ / gunther-eysenbach. blogspot. com/ 2008/ 03/ medicine-20-congress-website-launched. html. Accessed: 2008-03-07 [13] Van De Belt TH, Engelen LJ, Berben SAA, Schoonhoven L Definition of Health 2.0 and Medicine 2.0: A Systematic Review (J Med Internet Res 2010;12(2):e18)URL: http:/ / www. jmir. org/ 2010/ 2/ e18/ [14] http:/ / www. disruptivedemographics. com/ 2010/ 04/ health-30-new-data-on-aging-boomers. html [15] http:/ / www. socialf5. com/ blog/ 2011/ 05/ web-2-0-doctors/ [16] Levy, M. 2007. Online Health. Assessing the Risk and Opportunity of Social and One-to-One Media. Jupiter Research. Accessed at http:/ / www. jupiterresearch. com/ bin/ item. pl/ research:vision/ 103/ id=98795/ on 20/1/2008 [17] Manhattan Research, LLC. 2007. White Paper: Physicians and Web 2.0: 5 Things You Should Know about the Evolving Online Landscape for Physicians. Accessed at http:/ / www. manhattanresearch. com/ TTPWhitePaper. aspx on 20/1/2008 [18] Giustini, D. 2006. How Web 2.0 is changing medicine: Editorial. British Medical Journal, 333:1283-1284 [19] Sandars J, Haythornthwaite C. New horizons for e-learning in medical education: ecological and Web 2.0 perspectives. Med Teach. 2007 May;29(4):307-10. Review. PMID 17786742 [20] Crespo R. Virtual community health promotion. Prev Chronic Dis. 2007 Jul;4(3):A75. PMID 17572979 [21] Tan H, Ng JHK. Googling for a diagnosis—use of Google as a diagnostic aid: internet based study. BMJ 2006;333:1143-5. [22] Ferguson, T. ePatients white paper. www.e-patients.net. 2007. URL: http:/ / www. e-patients. net/ e-Patients_White_Paper. pdf on 22/1/08 [23] Frost JH, Massagli MP, Wicks P, Heywood J (2008) How the social web supports patient experimentation with a new therapy: The demand for patient-controlled and patient-centered informatics, AMIA Annu Symp Proc 6:217-21 [24] Ojalvo, H. E. (1996). Online advice: Good medicine or cyber-quackery? Retrieved September 22, 2007 from http:/ / www. acponline. org/ journals/ news/ dec96/ cybrquak. htm
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Health 2.0 [25] Economist, The. 2007. Health 2.0 : Technology and society: Is the outbreak of cancer videos, bulimia blogs and other forms of “user generated” medical information a healthy trend? The Economist, September 6: 73-74 [26] http:/ / www. medicine20congress. com [27] http:/ / tsdr. uspto. gov/ #caseNumber=77292954& caseType=SERIAL_NO& searchType=statusSearch [28] http:/ / tsdr. uspto. gov/ #caseNumber=77122908& caseType=SERIAL_NO& searchType=statusSearch [29] http:/ / www. doctors20. com/ [30] http:/ / tsdr. uspto. gov/ #caseNumber=85263947& caseType=SERIAL_NO& searchType=statusSearch [31] http:/ / www. basilstrategies. com/
External links • The term Health 2.0 (http://www.health2con.com/) is trademarked by this conference series • A set of useful resource on the Health 2.0 Wiki (http://health20.org/wiki/Main_Page) including a list of Health 2.0 companies (http://health20.org/wiki/Health_2.0_Companies) • A list of medical wiki websites (http://www.healthplusplus.com/wiki/Main_Page) including links to more than 40 medical wikis • Medicine 2.0 Congress (http://www.medicine20congress.com/), which is similar or identical to the Health 2.0 concept, but also includes "Science 2.0" • " Web Site Harnesses Power of Social Networks (http://www.washingtonpost.com/wp-dyn/content/article/ 2009/10/18/AR2009101801844.html)", The Washington Post, October 19, 2009
Health 2.0 Chapters Health 2.0 Boston Chapter Health 2.0 Hawaii Chapter Health 2.0 Japan Chapter Health 2.0 GCC Dubai Chapter • Health 2.0 NYC Chapter (http://health20nyc.org/)
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Public health informatics
Public health informatics Public Health Informatics has been defined as the systematic application of information and computer science and technology to public health practice, research, and learning. It is one of the subdomains of Health informatics.
United States In the United States, public health informatics is practiced by individuals in public health agencies at the federal and state levels and in the larger local health jurisdictions. Additionally, research and training in public health informatics takes place at a variety of academic institutions. At the federal Centers for Disease Control and Prevention in Atlanta, Georgia, the Public Health Surveillance and Informatics Program Office (PHSIPO) [1] focuses on advancing the state of information science and applies digital information technologies to aid in the detection and management of diseases and syndromes in individuals and populations. The bulk of the work of public health informatics in the United States, as with public health generally, takes place at the state and local level, in the state departments of health and the county or parish departments of health. At a state health department the activities may include: collection and storage of vital statistics (birth and death records); collection of reports of communicable disease cases from doctors, hospitals, and laboratories, used for infectious disease surveillance; display of infectious disease statistics and trends; collection of child immunization and lead screening information; daily collection and analysis of emergency room data to detect early evidence of biological threats; collection of hospital capacity information to allow for planning of responses in case of emergencies. Each of these activities presents its own information processing challenge.
Collection of public health data Before the advent of the internet, public health data in the United States, like other healthcare and business data, were collected on paper forms and stored centrally at the relevant public health agency. If the data were to be computerized they required a distinct data entry process, were stored in the various file formats of the day and analyzed by mainframe computers using standard batch processing. (TODO: describe CDC-provided DOS/desktop-based systems like TIMSS (TB), STDMIS (Sexually transmitted diseases); Epi-Info for epidemiology investigations; and others ) Since the beginning of the World Wide Web, public health agencies with sufficient information technology resources have been transitioning to web-based collection of public health data, and, more recently, to automated messaging of the same information. In the years roughly 2000 to 2005 the Centers for Disease Control and Prevention, under its National Electronic Disease Surveillance System [2] (NEDSS), built and provided free to states a comprehensive web and message-based reporting system called the NEDSS Base System [2] (NBS). Due to the funding being limited and it not being wise to have fiefdom-based systems, only a few states and larger counties have built their own versions of electronic disease surveillance systems, such as Pennsylvania's PA-NEDSS [3]. These do not provide timely full intestate notification services causing an increase in disease rates versus the NEDSS federal product. To promote interoperability, the CDC has encouraged the adoption in public health data exchange of several standard vocabularies and messaging formats from the health care world. The most prominent of these are: the Health Level 7 (HL7) standards for health care messaging; the LOINC system for encoding laboratory test and result information; and the Systematized Nomenclature of Medicine (SNOMED) vocabulary of health care concepts. Since about 2005, the CDC has promoted the idea of the Public Health Information Network to facilitate the transmission of data from various partners in the health care industry and elsewhere (hospitals, clinical and environmental laboratories, doctors' practices, pharmacies) to local health agencies, then to state health agencies, and
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Public health informatics then to the CDC. At each stage the entity must be capable of receiving the data, storing it, aggregating it appropriately, and transmitting it to the next level. A typical example would be infectious disease data, which hospitals, labs, and doctors are legally required to report to local health agencies; local health agencies must report to their state public health department; and which the states must report in aggregate form to the CDC. Among other uses, the CDC publishes the Morbidity and Mortality Weekly Report (MMWR) based on these data acquired systematically from across the United States. Major issues in the collection of public health data are: awareness of the need to report data; lack of resources of either the reporter or collector; lack of interoperability of data interchange formats, which can be at the purely syntactic or at the semantic level; variation in reporting requirements across the states, territories, and localities.
Storage of public health data Storage of public health data shares the same data management issues as other industries. And like other industries, the details of how these issues play out are affected by the nature of the data being managed. Due to the complexity and variability of public health data, like health care data generally, the issue of data modeling presents a particular challenge. While a generation ago flat data sets for statistical analysis were the norm, today's requirements of interoperability and integrated sets of data across the public health enterprise require more sophistication. The relational database is increasingly the norm in public health informatics. Designers and implementers of the many sets of data required for various public health purposes must find a workable balance between very complex and abstract data models such as HL7's Reference Information Model (RIM) or CDC's Public Health Logical Data Model [4], and simplistic, ad hoc models that untrained public health practitioners come up with and feel capable of working with. Due to the variability of the incoming data to public health jurisdictions, data quality assurance is also a major issue.
Analysis of public health data The need to extract usable public health information from the mass of data available requires the public health informaticist to become familiar with a range of analysis tools, ranging from business intelligence tools to produce routine or ad hoc reports, to sophisticated statistical analysis tools such as DAP/SAS and PSPP/SPSS, to Geographical Information Systems (GIS) to expose the geographical dimension of public health trends.
Applications in health surveillance and epidemiology • SAPPHIRE (Health care) or Situational Awareness and Preparedness for Public Health Incidences and Reasoning Engines is a semantics-based health information system capable of tracking and evaluating situations and occurrences that may affect public health.
References [1] [2] [3] [4]
http:/ / www. cdc. gov/ osels/ phsipo http:/ / www. cdc. gov/ nedss/ https:/ / www. nedss. state. pa. us/ nedss/ http:/ / www. cdc. gov/ phin/ library/ documents/ pdf/ PHIN_LDM_User_Guide_v1. 0. pdf
• Public Health Informatics and Information Systems by Patrick W. O’Carroll, William A. Yasnoff, M. Elizabeth Ward, Laura H. Ripp, Ernest L. Martin, D.A. Ross, A.R. Hinman, K. Saarlas, William H. Foege (Hardcover - Oct 16, 2002) ISBN 0-387-95474-0 • A Vision for More Effective Public Health Information Technology (http://papers.ssrn.com/sol3/papers. cfm?abstract_id=962532) on SSRN • Olmeda, Christopher J. (2000). Information Technology in Systems of Care. Delfin Press. ISBN 978-0-9821442-0-6
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Public health informatics • http://www.fda.gov/fdac/features/596_info.html on FDA • Health Data Tools and Statistics (http://phpartners.org/health_stats.html) • http://www.informatics-review.com/wiki/index.php/Public_Health_Informatics
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Applications in Healthcare Management Health Administration Informatics The emerging field of Health administration informatics is concerned with the evaluation, acquisition, implementation and day to day operation of information technology systems in support of all administration and clinical functions within the health care industry. The closely related field of biomedical informatics is primarily focused on the use of information systems for acquisition and application of patients' medical data, whereas nursing informatics deals with the delivery, administration and evaluation of patient care and disease prevention. What remains unclear, however, is how this emerging discipline should relate to the myriad of previously existing sub specializations within the broad umbrella of health informatics - including clinical informatics (which itself includes sub areas such as oncology informatics), bioinformatics and healthcare management informatics - particularly in light of the proposed "fundamental theorem" of biomedical informatics posed by Friedman in early 2009. The field of health administration informatics is emerging as attention continues to focus on the costly mistakes made by some health care organizations whilst implementing electronic medical records.
Relevance within the health care industry In a recent survey of health care CIOs and Information System (IS) directors, increasing patient safety and reducing medical errors was reported as among the top business issues. Two other key findings were that: • two-thirds of respondents indicated that the number of FTEs in their IT department will increase in the next 12 months; • and three-quarters of respondents indicated that their IT budgets would be increasing. The most likely staffing needs reported by the health care executives are network and architecture support (HIMMS, 2005). “The government and private insurers are beginning to pay hospitals more for higher quality care–and the only way to measure quality, and then improve it, is with more information technology. Hospital spending on such gear is expected to climb to $30.5 billion next year, from $25.8 billion in 2004, according to researcher Dorenfest Group” (Mullaney and Weintraub, 2005). This fundamental change in health care (pay for performance) means that hospitals and other health care providers will need to develop, adapt and maintain all of the technology necessary to measure and improve on quality. Physicians have traditionally lagged behind in their use of technology (i.e., electronic patient records). Only 7% of physicians work for hospitals, and so the task of “wooing them is an extremely delicate task” (Mullaney and Weintraub, 2005).
Careers The market demand for a specialized advanced degree that integrates Health Care Administration and Informatics is growing as the concept has gained support from the academic and professional communities. Recent articles in Health Management Technology cite the importance of integrating information technology with health care administration to meet the unique needs of the health care industry. The health care industry has been estimated to be around 10 years behind other industries in the application of technology and at least 10 to 15 years behind in leadership capability from the technology and perhaps the business perspective (Seliger, 2005; Thibault, 2005). This means there is quantifiable demand in the work force for health care administrators who are also prepared to lead in
Health Administration Informatics the field of health care administration informatics. In addition, the increasing costs and difficulties involved in evaluating the projected benefits from IT investments are requiring health care administrators to learn more about IT and how it affects business processes. The health care Chief Information Officer (CIO) must be able to build enterprise wide systems that will help reduce the administrative cost and streamline the automation of administrative processes and patient record keeping. Increasingly, the CIO is relied upon for specialized analytical and collaborative skills that will enable him/her to build systems that health care clinicians will use. A recent well-publicized debacle (shelving of a $34 million computer system after three months) at a top U. S. hospital underlines the need for leaders who understand the health care industry information technology requirements (Connolly, 2005). Several professional organizations have also addressed the need for academic preparation that integrates the two specializations addressed by UMUC’s MSHCAI degree. In the collaborative response to the Office of the National Coordinator for Health Information Technology (ONCHIT) request for information regarding future IT needs, thirteen major health and technology organizations endorsed a “Common Framework” to support health information exchange in the United States, while protecting patient privacy. The response cited the need for continuing education of health information management professionals as a significant barrier to implementation of a National Health Information Network (NHIN) (The Collaborative Response, 2005).
References • Connolly, C. (2005, March 21) Cedars-Sinai doctors cling to Pen and paper. The Washington Post. Health Informatics World Wide (2005, March). Health informatics index site. Retrieved March 30, 2005 from (http:/ /www.hiww.org/us.html). • Healthcare Information and Management Systems Society (HIMSS) (2005, February). 16th annual HIMSS leadership survey sponsored by Superior Consultant Company. Retrieved 3/30/2005 from (http://www.himss. org/2005survey/docs/Healthcare_CIO_finalreport.pdf). • Mullaney, T. J., & Weintraub, A. (2005 March 28). The digital hospital. Business Week 3926, 76. • Seliger, R. (2005). Healthcare IT tipping point. Health Management Technology 26(3), 48-49. • The Collaborative Response to the Office of the National Coordinator for Health Information Technology Request for Information (2005, January). Retrieved March 30, 2005 from (http://world.std.com/~goldberg/ collaborativeresponsenhin.pdf). • Thibault, B. (2005). Making beautiful music together. Behavioral Health 26(3), 28-29.
External links • University of Maryland University College (http://umuc.edu/programs/grad/mshai/) • University of Alabama - Birmingham (http://main.uab.edu/shrp/default.aspx?pid=77369) • University of Illinois at Chicago (http://healthinformatics.uic.edu/)
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Medical integration environment
Medical integration environment Medical integration environment (MIE) are specialised tools designed to simplify the sharing of medical and related data between medical equipment and electronic health records. Technically, they are similar to an Enterprise Service Bus but with several extra features allowing for legacy systems that do not use web services messaging. Typically, they use Java Message Service; most Enterprise Application Integration systems can be modified to be used as an MIE but may lack the crucial HL7 and Arden syntax for storing medical knowledge.
Health information exchange Health information exchange (HIE) is the mobilization of healthcare information electronically across organizations within a region, community or hospital system. HIE provides the capability to electronically move clinical information among disparate health care information systems while maintaining the meaning of the information being exchanged. The goal of HIE is to facilitate access to and retrieval of clinical data to provide safer and more timely, efficient, effective, and equitable patient-centered care. HIE is also useful to public health authorities to assist in analyses of the health of the population. HIE systems facilitate the efforts of physicians and clinicians to meet high standards of patient care through electronic participation in a patient's continuity of care with multiple providers. Secondary health care provider benefits include reduced expenses associated with: • the manual printing, scanning and faxing of documents, including paper and ink costs, as well as the maintenance of associated office machinery • the physical mailing of patient charts and records, and phone communication to verify delivery of traditional communications, referrals, and test results • the time and effort involved in recovering missing patient information, including any duplicate tests required to recover such information According to an internal study at Sushoo Health Information Exchange [1], the current method of exchanging patients' health information accounts for approximately $17,160 of expenses annually for a single-clinician practice. Formal organizations are now emerging to provide both form and function for health information exchange efforts, both on independent and governmental/regional levels. These organizations are, in many cases, enabled and supported financially by statewide health information exchange grants from the Office of the National Coordinator for Health Information Technology. These grants were legislated into the HITECH components of the American Reinvestment and Recovery Act in 2009.[2] The latter organizations (often called Regional Health Information Organizations, or RHIOs) are ordinarily geographically defined entities which develop and manage a set of contractual conventions and terms, arrange for the means of electronic exchange of information, and develop and maintain HIE standards. In the United States, federal and state regulations regarding HIEs and HIT (health information technology) are still being defined. Federal regulations such as "Meaningful Use" legislation[3] as well as the implementation of some state governments of state-sponsored HIEs (such as the North Carolina HIE[4]) in addition to fluctuating health care regulations among the states are rapidly changing the face of this relatively new industry. HIEs and RHIOs continue to struggle to achieve self-sustainability and the vast majority remain tied to Federal, State, or Independent grant funding in order to remain operational; with some exceptions such as the Indiana HIE.[5]
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Established HIE communities Chesapeake Regional Information System for our Patients CRISP is a non-profit corporation that is implementing health information exchange in the state of Maryland. The organization also serves as the Health IT Extension Center for Maryland. CRISP was created by Johns Hopkins Medicine, MedStar Health, the University of Maryland Medical System and Erickson Retirement Communities. Audacious Inquiry serves as program director and technical architect for the health information exchange while Dynamed Solutions provides project management and organizational support under CRISP. Colorado Regional Health Information Organization (CORHIO) CORHIO is Colorado’s state designated entity for health information exchange. As of April 2013, about 28 Colorado hospitals and more than 700 doctors were connected to the CORHIO HIE.[6]
Delaware Health Information Network ([7]) DHIN is a non-profit public-private partnership enacted by the Delaware General Assembly in 1997, for the benefit of all citizens of Delaware to advance the creation of a statewide health information network and to address Delaware's needs for timely, reliable and relevant health care information. DHIN has adopted regulations to govern its operations and has policies and procedures in place to support privacy and security of patient information. DHIN enhances a health care information exchange started in May 2007. In February 2012, The Delaware Health Information Network announced full participation of all acute care hospitals and skilled nursing facilities in the state, along with the vast majority of Delaware providers, in the first statewide community health record. As of June 2013, DHIN has attracted the participation of 97 percent of Delaware providers, tracks nearly 88 percent of Delaware's population, and delivers more than 10 million clinical results and reports to participating providers annually. Frysian Health Information Exchange ([8]) The Friesland Regional Cardiology Network speeds up the referral process, improves both diagnosis and the clinical decision process, and on average reduces by one or two days the length-of-stay for patients in hospitals. From their office workstations, cardiologists are able to consult the advanced clinical images provided by any hospital linked to the network. The distributed storage of records eliminates the duplication of records across multiple sites. Once uploaded to the cardiology network, records remain available for consultation at any time so that previous episodes of a patient’s care can be consulted in detail no matter where the care was provided in the region. Harvard Pilgrim Health Care HPHC is a non-profit insurance provider which serves members throughout Massachusetts, New Hampshire, and Maine. The provider offers variety health insurance options for companies, families and individuals. Customers health insurance expectations are met through a tailored options from preferred provider organization (PPO), point-of-sale (POS), and health maintenance organization (HMO). HPHC implements CRM, Master Data Management and is now implementing Oracle Policy Automation to support integrated call center and online self-service for plan purchase and management across their various customer groups additionally, HPHC is using their platform to support recruitment and to better analyze and improve service levels in a heavily competitive market. Indiana Health Information Exchange ([9]) The Indiana Health Information Exchange operates the U.S.'s largest HIE and one of the oldest with data on more than 7 million patients, connecting hospitals, rehabilitation centers, long term care facilities, laboratories, imaging centers, clinics, community health centers and other healthcare organizations. Created and ran by the Regenstrief Institute, a medical informatics think tank, the Indiana Network for Patient Care (INPC), a secure
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Health information exchange
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network provides a patient records to participating doctors. This HIE grew over time from 12 hospitals in the center of the state with approximately 5,000 physicians, to 93 hospitals out of 114 in the state and more than 14,000 physicians in Indiana. Utah Health Information Network ([10]) The Utah Health Information Network (UHIN) is a broad-based coalition of Utah healthcare insurers, providers, and other interested parties, including the Utah State government. Since 1993, UHIN members have come together for the common goal of reducing healthcare costs and improving the quality of care through the use of electronic data interchange (EDI) for healthcare transactions. Exchanging information electronically rather than by phone, fax or surface mail means that data can get to those who need it securely, economically and efficiently. UHIN currently serves nearly all the hospitals, ambulatory surgery centers, national laboratories, insurers, and approximately 90% of the medical providers in Utah as well as the Utah State government. As a community organization the focus is on creating data exchange solutions that work for the entire healthcare community, from large integrated networks to single-provider offices. The Clinical Health Information Exchange (cHIE) is a secure electronic way for medical professionals to share and view patient information that is needed at the point of care. The cHIE makes this information accessible, with patient consent, to authorized users while maintaining the highest standards of patient privacy.
References [1] http:/ / www. sushoo. com/ [2] [3] http:/ / healthit. hhs. gov/ portal/ server. pt?open=512& objID=1325& parentname=CommunityPage& parentid=1& mode=2 [4] http:/ / www. nchica. org/ GetInvolved/ NCHIE/ intro. htm [5] http:/ / www. ihie. org/ [6] http:/ / www. corhio. org/ media/ 54599/ smpc-corhio_hie_press_release_final. pdf [7] http:/ / www. dhin. org [8] http:/ / www. ihe-europe. net/ drupal/ sites/ default/ files/ IHE-Europe-Success-Story-FRIESLAND_Final. pdf [9] http:/ / www. ihie. com [10] http:/ / www. uhin. org
• McGowan JJ, Overhage JM, Barnes M, McDonald CJ (April 2004). "Indianapolis I3: the third generation Integrated Advanced Information Management Systems". J Med Libr Assoc 92 (2): 179–87. PMC 385298 (http:/ /www.ncbi.nlm.nih.gov/pmc/articles/PMC385298). PMID 15098046 (http://www.ncbi.nlm.nih.gov/ pubmed/15098046). • eHealth Initiative, Second Annual Survey of State, Regional and Community-based Health Information Exchange Initiatives and Organizations, August, 2005 • Hagland, Mark (2009). Transformative Quality: The Emerging Revolution in Health Care Performance (http:// www.productivitypress.com/shopping_cart/products/product_detail.asp?sku=PP8492& isbn=9781420084924&parent_id=4&pc). Productivity Press/Taylor & Francis Group, LLC, New York. ISBN 978-1-4200-8492-4.
External links • HHS Health Information Technology (http://healthit.hhs.gov/) • HIMSS information about HIE and RHIO (http://www.himss.org/ASP/topics_rhio.asp)
Hospital information system
Hospital information system A Hospital information system is a comprehensive, integrated information system designed to manage the all aspects of a hospital operation, such as medical, administrative, financial, legal and the corresponding service processing. Traditional approaches encompass paper-based information processing as well as resident work position and mobile data acquisition and presentation.
Hospital information system One of the most important issues is health services. Hospitals provide a medical assistance to people. The best introduction for hospital information systems has been made in 2011 International Conference on Social Science and Humanity, which is; Hospital Information Systems can be defined as massive, integrated systems that support the comprehensive information requirements of hospitals, including patient, clinical, ancillary and financial management. Hospitals are extremely complex institutions with large departments and units coordinate care for patients. Hospitals are becoming more reliant on the ability of hospital information system (HIS) to assist in the diagnosis, management and education for better and improved services and practices. In health organization such as hospitals, implementation of HIS inevitable due to many mediating and dominating factors such as organization, people and technology.
Architecture Hospital Information System architecture has three main levels, Central Government Level, Territory Level, and Patient Carrying Level. Generally all types of hospital information system (HIS) are supported in client-server architectures for networking and processing. Most work positions for HIS are currently resident types. Mobile computing began with wheeled PC stands. Now tablet computers and smartphone applications are used. Enterprise HIS with Internet architectures have been successfully deployed in Public Healthcare Territories and have been widely adopted by further entities. The Hospital Information System (HIS) is a province-wide initiative designed to improve access to patient information through a central electronic information system. HIS’s goal is to streamline patient information flow and its accessibility for doctors and other health care providers. These changes in service will improve patient care quality and patient safety over time. The patient carries system record patient information, patient laboratory test results, and patient’s doctor information. Doctors can access easily person information, test results, and previous prescriptions. Patient schedule organization and early warning systems can provide by related systems. A cloud computing alternative is not recommended, as data security of individual patient records services are not well accepted by the public. HIS can be composed of one or several software components with specialty-specific extensions, as well as of a large variety of sub-systems in medical specialties, for example Laboratory Information System (LIS), Policy and Procedure Management System, Radiology Information System (RIS) or Picture archiving and communication system (PACS). CISs are sometimes separated from HISs in that one focuses the flow management and clinical-state-related data and the other focuses the patient-related data with the doctor's letters and the electronic patient record. However, the naming differences are not standardised between suppliers. Architecture in based on a distributed approach and on the utilization of standard software products complying with the industrial and market standards must be utilized (such as: UNIX operating systems, MS-Windows, local area network based on Ethernet and TCP/IP protocols, relational database management systems based on SQL language
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Hospital information system or Oracle databases, C programming language).
Aim As an area of medical informatics the aim of an HIS is to achieve the best possible support of patient care and outcome and administration by presenting data where needed and acquiring data when generated with networked electronic data processing. Hospital Information Systems main demands are correct data storage, reliable usage, fast to reach data, secure to keep data on storage and lower cost of usage. Hospital Information Systems provide a common source of information about a patient’s health history. The system have to keep data in secure place and controls who can reach the data in certain circumstances. These systems enhance the ability of health care professionals to coordinate care by providing a patient’s health information and visit history at the place and time that it is needed. Patient’s laboratory test information also visual results such as X-ray may reachable from professionals. HIS provide internal and external communication among health care providers. The HIS may control organizations, which is Hospital in these case, official documentations, financial situation reports, personal data, utilities and stock amounts, also keeps in secure place patients information, patients medical history, prescriptions, operations and laboratory test results. The HIS may protect organizations, handwriting error, overstock problems, conflict of scheduling personnel, official documentation errors like tax preparations errors.
Organizational structure The head of the HIS department is a person who is qualified and experienced in computer systems. Graduate and postgraduate computer diploma/degree holders are available. Depending on the set-up and the extent of computerization and its sophistication, the department may have some or all of the following staff in addition to the head of the department. Organizational structure refers to levels of management within a hospital and these levels allow efficient management of hospital departments. The structure helps one understand the hospital’s chain of command and work flows. Common organizational structure groups are Administrative Services, Information system Services, Therapeutic Services, Diagnostic Services, and Support Services. Hospital Information systems also can extend as Database administrator, interface developer, and users which are patients and official users.
Systems administrator/database administrator The systems administrator-cum-database administrator is responsible for systems administration to ensure high uptime of the system and for handling all database back-up and restoration activities.
Application specialist and trainer The hospital’s application specialist together with the software vendor is involved in all the activities required for implementing the application software. Trainers train and retrain new employees in the hospital.
Hardware/network engineers Hardware/Network engineers are responsible for maintaining the hardware and network systems in the hospital. They undertake all troubleshooting activities that may be required to keep the system online and patient data available to doctors and nurses.
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Standardization There is no standardization but for data formats and for data interchange, as with the HL7 initiative supported by ISO.
Benefits of HIS • Easy access to doctors data to generate varied records, including classification based on demographic, gender, age, and so on. It is especially beneficial at ambulatory (out-patient) point, hence enhancing continuity of care. As well as, Internet-based access improves the ability to remotely access such data. • It helps as a decision support system for the hospital authorities for developing comprehensive health care policies. • Efficient and accurate administration of finance, diet of patient, engineering, and distribution of medical aid. It helps to view a broad picture of hospital growth • Improved monitoring of drug usage, and study of effectiveness. This leads to the reduction of adverse drug interactions while promoting more appropriate pharmaceutical utilization. • Enhances information integrity, reduces transcription errors, and reduces duplication of information entries. • Hospital software is easy to use and eliminates error caused by handwriting. New technology computer systems give perfect performance to pull up information from server or cloud servers.
References • Hospital Management Software - Medico Clinic [1] • Hospital Management Software - APMIS: All Purpose Medical Information System- Connected healthcare paltform [2] • NOVA SCOTIA HOSPITAL INFORMATION SYSTEM [3] • Hospital Information System Project [4] • Healthcare and Distributed Systems Technology [5] • 2011 International Conference on Social Science and Humanity [6] [1] [2] [3] [4] [5] [6]
https:/ / www. facebook. com/ pages/ Medico-Clinic-Hospital-Management-Software/ 414012225326659 https:/ / www. sabaothtechnologies. com/ APMIS. pdf http:/ / www. oag-ns. ca/ June2005/ ch6%20June2005%20NSHis. pdf http:/ / www. manitoba-ehealth. ca/ ehr_hisp. html http:/ / www. ansa. co. uk/ ANSATech/ 95/ ansaworks-95/ hltcare. pdf http:/ / www. ipedr. com/ vol5/ no1/ 45-H00097. pdf
Further reading • Shortliffe, E.H., and Cimino, J.J. eds. Biomedical Informatics: Computer Applications in Health Care and Biomedicine (3rd edition). New York: Springer, 2006. • National Institute of Clinical Excellence, Principles of Best Practice in Clinical Audit. London: NICE, 2002. (ISBN 1-85775-976-1) • Olmeda, Christopher J. (2000). Information Technology in Systems of Care. Delfin Press. ISBN 978-0-9821442-0-6 • Payne, P.R., Greaves, A.W., and Kipps, T.J. CRC Clinical Trials Management System (CTMS): an integrated information management solution for collaborative clinical research, AMIA Annu Symp Proc. 2003:967.
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Healthcare workflow
Healthcare workflow Workflow in health care is an important term for today's physicians.[citation needed] The workflow describes the full process of how the office and patient work with each other. From the moment the patient calls to set up an exam, to the billing staff working on the claims, this is all inclusive in a true workflow. Office administrators are tasked with improving the workflow and making it more economical and time efficient. Workflow in recent yearsWikipedia:Manual of Style/Dates and numbers#Chronological items has started to conjure an additional meaning – how an Electronic Medical Record (EMR) operates. Workflow management technology is not used in health care as often as in other domains. However workflow engines can be very useful to manage processes.[citation needed] Workflow software can be also used as a clinical guideline execution engine.
External links • http://healthcareworkflow.wordpress.com
Computer physician order entry Computerized physician order entry (CPOE) (also sometimes referred to as Computerized Provider Order Entry) (also sometimes referred to as Computerized Provider Order Management ) is a process of electronic entry of medical practitioner instructions for the treatment of patients (particularly hospitalized patients) under his or her care. These orders are communicated over a computer network to the medical staff or to the departments (pharmacy, laboratory, or radiology) responsible for fulfilling the order. CPOE decreases delay in order completion, reduces errors related to handwriting or transcription, allows order entry at the point of care or off-site, provides error-checking for duplicate or incorrect doses or tests, and simplifies inventory and posting of charges. CPOE is a form of patient management software.[1]
Terminology related to order entry Filler The application responding to, i.e., performing, a request for services (orders) or producing an observation. The filler can also originate requests for services (new orders), add additional services to existing orders, replace existing orders, put an order on hold, discontinue an order, release a held order, or cancel existing orders.
Order A request for a service from one application to a second application. In some cases an application is allowed to place orders with itself.
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Computer physician order entry
Order detail segment One of several segments that can carry order information. Future ancillary specific segments may be defined in subsequent releases of the Standard if they become necessary.
Placer The application or individual originating a request for services (order).
Placer order group A list of associated orders coming from a single location regarding a single patient.
Order Set A grouping of orders used to standardize and expedite the ordering process for a common clinical scenario. (Typically, these orders are started, modified, and stopped by a licensed physician.)
Protocol A grouping of orders used to standardize and automate a clinical process on behalf of a physician. (Typically, these orders are started, modified, and stopped by a nurse, pharmacist, or other licensed health professional.)
Features of CPOE systems Features of the ideal computerized physician order entry system (CPOE) include: Ordering Physician orders are standardized across the organization, yet may be individualized for each doctor or specialty by using order sets. Orders are communicated to all departments and involved caregivers, improving response time and avoiding scheduling problems and conflict with existing orders. Patient-centered decision support The ordering process includes a display of the patient's medical history and current results and evidence-based clinical guidelines to support treatment decisions. Often uses medical logic module and/or Arden syntax to facilitate fully integrated Clinical Decision Support Systems (CDSS). Patient safety features The CPOE system allows real-time patient identification, drug dose recommendations, adverse drug reaction reviews, and checks on allergies and test or treatment conflicts. Physicians and nurses can review orders immediately for confirmation. Intuitive Human interface The order entry workflow corresponds to familiar "paper-based" ordering to allow efficient use by new or infrequent users. Regulatory compliance and security Access is secure, and a permanent record is created, with electronic signature. Portability The system accepts and manages orders for all departments at the point-of-care, from any location in the health system (physician's office, hospital or home) through a variety of devices, including wireless PCs and tablet computers. Management
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Computer physician order entry The system delivers statistical reports online so that managers can analyze patient census and make changes in staffing, replace inventory and audit utilization and productivity throughout the organization. Data is collected for training, planning, and root cause analysis for patient safety events. Billing Documentation is improved by linking diagnoses (ICD-9-CM or ICD-10-CM codes) to orders at the time of order entry to support appropriate charges.
Patient safety benefits of CPOE In the past, physicians have traditionally hand-written or verbally communicated orders for patient care, which are then transcribed by various individuals (such as unit clerks, nurses, and ancillary staff) before being carried out. Handwritten reports or notes, manual order entry, non-standard abbreviations and poor legibility lead to errors and injuries to patients, according to a 1999 Institute of Medicine (IOM) report. A follow up IOM report in 2001 advised use of electronic medication ordering, with computer- and internet-based information systems to support clinical decisions. Prescribing errors are the largest identified source of preventable hospital medical error. A 2006 report by the Institute of Medicine estimated that a hospitalized patient is exposed to a medication error each day of his or her stay. Studies of computerized physician order entry (CPOE) has yielded evidence that suggests the medication error rate can be reduced by 80%, and errors that have potential for serious harm or death for patients can be reduced by 55%, and other studies have also suggested benefits.[2] Further, in 2005, CMS and CDC released a report that showed only 41 percent of prophylactic antibacterials were correctly stopped within 24 hours of completed surgery. The researchers conducted an analysis over an eight-month period, implementing a CPOE system designed to stop the administration of prophylactic antibacterials. Results showed CPOE significantly improved timely discontinuation of antibacterials from 38.8 percent of surgeries to 55.7 percent in the intervention hospital.[3] CPOE/e-Prescribing systems can provide automatic dosing alerts (for example, letting the user know that the dose is too high and thus dangerous) and interaction checking (for example, telling the user that 2 medicines ordered taken together can cause health problems). In this way, specialists in pharmacy informatics work with the medical and nursing staffs at hospitals to improve the safety and effectiveness of medication use by utilizing CPOE systems.
Risks of CPOE CPOE presents several possible dangers by introducing new types of errors. Prescriber and staff inexperience may cause slower entry of orders at first, use more staff time, and is slower than person-to-person communication in an emergency situation. Physician to nurse communication can worsen if each group works alone at their workstations. Automation causes a false sense of security, a misconception that when technology suggests a course of action, errors are avoided. These factors contributed to an increased mortality rate in the Children's Hospital of Pittsburgh's Pediatric ICU when a CPOE systems was introduced. In other settings, shortcut or default selections can override non-standard medication regimens for elderly or underweight patients, resulting in toxic doses.[citation needed] Frequent alerts and warnings can interrupt work flow, causing these messages to be ignored or overridden due to alert fatigue. CPOE and automated drug dispensing was identified as a cause of error by 84% of over 500 health care facilities participating in a surveillance system by the United States Pharmacopoeia. Introducing CPOE to a complex medical environment requires ongoing changes in design to cope with unique patients and care settings, close supervision of overrides caused by automatic systems, and training, testing and re-training all users.
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Implementation CPOE systems can take years to install and configure. Despite ample evidence of the potential to reduce medication errors, adoption of this technology by doctors and hospitals in the United States has been slowed by resistance to changes in physician's practice patterns, costs and training time involved, and concern with interoperability and compliance with future national standards. According to a study by RAND Health, the US healthcare system could save more than 81 billion dollars annually, reduce adverse medical events and improve the quality of care if it were to widely adopt CPOE and other health information technology.[4] As more hospitals become aware of the financial benefits of CPOE, and more physicians with a familiarity with computers enter practice, increased use of CPOE is predicted. Several high profile failures of CPOE implementation have occurred, so a major effort must be focused on change management, including restructuring workflows, dealing with physicians' resistance to change, and creating a collaborative environment. An early success with CPOE by the United States Department of Veterans Affairs (VA) is the Veterans Health Information Systems and Technology Architecture or VistA. A graphical user interface known as the Computerized Patient Record System (CPRS) allows health care providers to review and update a patient’s record at any computer in the VA's over 1,000 healthcare facilities. CPRS includes the ability to place orders by CPOE, including medications, special procedures, x-rays, patient care nursing orders, diets and laboratory tests. The world's first successful implementation of a CPOE system was at El Camino Hospital in Mountain View, California in the early 1970s. The Medical Information System (MIS) was originally developed by a software and hardware team at Lockheed in Sunnyvale, California, which became the TMIS group at Technicon Instruments Corporation. The MIS system used a light pen to allow physicians and nurses to quickly point and click items to be ordered. As of 2005[5], one of the largest projects for a national EHR is by the National Health Service (NHS) in the United Kingdom. The goal of the NHS is to have 60,000,000 patients with a centralized electronic health record by 2010. The plan involves a gradual roll-out commencing May 2006, providing general practices in England access to the National Programme for IT (NPfIT). The NHS component, known as the "Connecting for Health Programme",[6] includes office-based CPOE for medication prescribing and test ordering and retrieval, although some concerns have been raised about patient safety features. In 2008, the Massachusetts Technology Collaborative and the New England Healthcare Institute (NEHI) published research showing that 1 in 10 patients admitted to a Massachusetts community hospital suffered a preventable medication error. The study argued that Massachusetts hospitals could prevent 55,000 adverse drug events per year and save $170 million annually if they fully implemented CPOE. The findings prompted the Commonwealth of Massachusetts to enact legislation requiring all hospitals to implement CPOE by 2012 as a condition of licensure.[7][8] In addition, the study also concludes that it would cost approximately $2.1 million to implement a CPOE system, and a cost of $435,000 to maintain it in the state of Massachusetts while it saves annually about $2.7 million per hospital. The hospitals will still see payback within 26 months through reducing hospitalizations generated by error. Despite the advantages and cost savings, the CPOE is still not well adapted by many hospitals in the US. The Leapfrog’s 2008 Survey showed that most hospitals are still not complying with having a fully implemented, effective CPOE system. The CPOE requirement became more challenging to meet in 2008 because the Leapfrog introduced a new requirement: Hospitals must test their CPOE systems with Leapfrog’s CPOE Evaluation Tool. So the number of hospitals in the survey considered to be fully meeting the standard dropped to 7% in 2008 from 11% the previous year. Though the adoption rate seems very low in 2008, it is still an improvement from 2002 when only 2% of hospitals met this Leapfrog standard.
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External links • Certification Commission for Healthcare Information Technology (CCHIT) [9] • AHRQ National Resource Center for Health IT [10]
Notes [1] Agency for Healthcare Research and Quality (2009). http:/ / healthit. ahrq. gov/ images/ jan09cpoereport/ cpoe_issue_paper. htm [2] http:/ / www. eurekalert. org/ pub_releases/ 2010-05/ sumc-ssf042710. php [3] http:/ / www. beckersasc. com/ asc-quality-infection-control/ study-cpoe-systems-improve-prophylactic-antibacterial-use-in-surgical-patients. html [4] RAND Healthcare: Health Information Technology: Can HIT Lower Costs and Improve Quality? (http:/ / www. rand. org/ pubs/ research_briefs/ RB9136/ index1. html) Retrieved on July 8, 2006 [5] http:/ / en. wikipedia. org/ w/ index. php?title=Computerized_physician_order_entry& action=edit [6] NHS Connecting for Health: Delivering the National Programme for IT (http:/ / www. connectingforhealth. nhs. uk/ delivery/ ) Retrieved August 4, 2006 [7] http:/ / www. todayshospitalist. com/ index. php?b=articles_read& cnt=614 [8] Massachusetts Hospitals Must Have CPOE By 2012 And CCHIT-Certified EHRS By 2015: (http:/ / www. hl7standards. com/ blog/ 2008/ 08/ 13/ massachusetts-hospitals-must-have-cpoe-by-2012-and-cchit-certified-ehrs-by-2015/ ) Retrieved April 11, 2012 [9] http:/ / www. cchit. org/ [10] http:/ / healthit. ahrq. gov/ cpoe
ICU quality and management tools The intensive care unit (ICU) is one of the major components of the current health care system. The advances in supportive care and monitoring resulted in significant improvements in the care of surgical and clinical patients. Nowadays aggressive surgical therapies as well as transplantation are made safer by the monitoring in a closed environment, the surgical ICU, in the post-operative period. Moreover, the care and full recovery of many severely ill clinical patients as those with life-threatening infections occurs as a result of intensive care. However, despite many significant advances in various fields as mechanical ventilation, renal replacement therapy, antimicrobial therapy and hemodynamic monitoring this increased knowledge and the wise use of such technology is not available for all patients. Shortage of ICU beds are an important issue, however even when ICU beds are available significant variability in treatment and in the adherence to evidence-based interventions do not occur.
Tools for ICU quality monitoring Several measures of ICU performance have been proposed in the past 30 years. It is intuitive, and correct, to assume that ICU mortality may be a useful marker of quality. However, crude mortality rates does not take into consideration the singular aspects of each specific patient population that is treated in a certain geographic region, hospital or ICU. Therefore approaches looking for standardized mortality ratios that are adjusted for disease severity, comorbidities and other clinical aspects are often sought. Severity of illness is usually evaluated by scoring systems that integrates clinical, physiologic and demographic variables. Scoring systems are interesting tools to describe ICU populations and explain their different outcomes. The most frequently used are the APACHE II, SAPS II and MPM. However, newer scores as APACHE IV and SAPS III have been recently introduced in clinical practice. More than only using scoring systems, one should search for a high rate of adherence to clinically effective interventions. Adherence to interventions as deep venous thrombosis prophylaxis, reduction of ICU-acquired infections, adequate sedation regimens and decreasing and reporting serious adverse events are essential and have been accepted as benchmarking of quality. The complex task of collecting and analyzing data on performance measures are made easier when clinical information systems are available. Although several clinical information systems focus on important aspects as
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ICU quality and management tools computerized physician order entry systems and individual patient tracking information, few have attempted to gather clinical information generating full reports that provide a panorama of the ICU performance and detailed data on several domains as mortality, length of stay, severity of illness, clinical scores, nosocomial infections, adverse events and adherence to good clinical practice. Through implementing quality initiatives, increasing the quality of care and patient safety are major and feasible goals. Such systems (for example: Epimed Monitor [1]) are available for clinical use and may facilitate the process of care on a daily basis and provide data for an in-depth analysis of ICU performance.
References • A.O. Gallesio,D. Ceraso,F. Palizas (July 2006). "Improving quality in the intensive care unit setting". Crit Care Clin. 22 (3): 547–571. doi:10.1016/j.ccc.2006.04.002 [2]. ISSN3 [3]. PMID16893740 [4]. • A. Garland (June 2005). "Improving the ICU: part 1" [5] (Wikipedia:Link rot). Chest (American College of Chest Physicians) 127 (6): 2151–2164. doi:10.1378/chest.127.6.2151 [6]. PMID15947333 [7]. • A. Garland (June 2005). "Improving the ICU: part 2" [8] (Wikipedia:Link rot). Chest (American College of Chest Physicians) 127 (6): 2165–2179. doi:10.1378/chest.127.6.2165 [9]. PMID15947334 [10]. • T.R. McMillan and R.C. Hyzy (February 2007). "Bringing quality improvement into the intensive care unit" [11]. Critical Care Medicine (Society of Critical Care Medicine and Lippincott Williams & Wilkins) 35 (2 Suppl): S59–S65. doi:10.1097/01.CCM.0000252914.22497.44 [12]. PMID17242607 [13].
References [1] http:/ / www. epimedsolutions. com/ index. php?lang=english [2] http:/ / dx. doi. org/ 10. 1016%2Fj. ccc. 2006. 04. 002 [3] http:/ / www. worldcat. org/ issn/ 3 [4] http:/ / www. ncbi. nlm. nih. gov/ pubmed/ 16893740 [5] http:/ / www. chestjournal. org. / cgi/ content/ full/ 127/ 6/ 2151 [6] http:/ / dx. doi. org/ 10. 1378%2Fchest. 127. 6. 2151 [7] http:/ / www. ncbi. nlm. nih. gov/ pubmed/ 15947333 [8] http:/ / www. chestjournal. org. / cgi/ content/ full/ 127/ 6/ 2165 [9] http:/ / dx. doi. org/ 10. 1378%2Fchest. 127. 6. 2165 [10] http:/ / www. ncbi. nlm. nih. gov/ pubmed/ 15947334 [11] http:/ / www. ccmjournal. com. / pt/ re/ ccm/ abstract. 00003246-200702001-00009. htm [12] http:/ / dx. doi. org/ 10. 1097%2F01. CCM. 0000252914. 22497. 44 [13] http:/ / www. ncbi. nlm. nih. gov/ pubmed/ 17242607
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Laboratory information management system
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Laboratory information management system A Laboratory Information Management System (LIMS), sometimes referred to as a Laboratory Information System (LIS) or Laboratory Management System (LMS), is a software-based laboratory and information management system that offers a set of key features that support a modern laboratory's operations. Those key features include — but are not limited to — workflow and data tracking support, flexible architecture, and smart data exchange interfaces, which fully "support its use in regulated environments." The features and uses of a LIMS have evolved over the years from simple sample tracking to an enterprise resource planning tool that manages multiple aspects of laboratory informatics.
Laboratories around the world depend on a LIMS to manage data, assign rights, manage inventory, and more.
The definition of a LIMS is somewhat controversial: LIMSs are dynamic because the modern laboratory's requirements are rapidly evolving and needs often vary significantly from lab to lab. Therefore, a working definition of a LIMS ultimately depends on the interpretation by the individuals or groups involved. Dr. Alan McLelland of the Institute of Biochemistry, Royal Infirmary, Glasgow highlighted this problem in the late 1990s by explaining how a LIMS is perceived by an analyst, a laboratory manager, an information systems manager, and an accountant, "all of them correct, but each of them limited by the users' own perceptions." Historically the LIMS, LIS, and Process Development Execution System (PDES) have all performed similar functions. Historically the term "LIMS" has tended to be used to reference informatics systems targeted for environmental, research, or commercial analysis such as pharmaceutical or petrochemical work. "LIS" has tended to be used to reference laboratory informatics systems in the forensics and clinical markets, which often required special case management tools. The term "PDES" has generally applied to a wider scope, including, for example, virtual manufacturing techniques, while not necessarily integrating with laboratory equipment. In recent times LIMS functionality has spread even farther beyond its original purpose of sample management. Assay data management, data mining, data analysis, and electronic laboratory notebook (ELN) integration are all features that have been added to many LIMS, enabling the realization of translational medicine completely within a single software solution. Additionally, the distinction between a LIMS and a LIS has blurred, as many LIMS now also fully support comprehensive case-centric clinical data.
History Up until the late 1970s, the management of laboratory samples and the associated analysis and reporting were time-consuming manual processes often riddled with transcription errors. This gave some organizations impetus to streamline the collection of data and how it was reported. Custom in-house solutions were developed by a few individual laboratories, while some enterprising entities at the same time sought to develop a more commercial reporting solution in the form of special instrument-based systems. In 1982 the first generation of LIMS was introduced in the form of a single centralized minicomputer, which offered laboratories the first opportunity to utilize automated reporting tools. As the interest in these early LIMS grew, industry leaders like Gerst Gibbon of the Federal Energy Technology Centre in Pittsburgh began planting the seeds through LIMS-related conferences. By 1988 the second-generation commercial offerings were tapping into relational databases to expand LIMS into more application-specific territory, and International LIMS Conferences were in full swing. As personal computers became more powerful and prominent, a third generation of LIMS emerged in the early 1990s. These new LIMS took advantage of the developing client/server architecture, allowing laboratories to
Laboratory information management system implement better data processing and exchanges. By 1995 the client/server tools had developed to the point of allowing processing of data anywhere on the network. Web-enabled LIMS were introduced the following year, enabling researchers to extend operations outside the confines of the laboratory. From 1996 to 2002 additional functionality was included in LIMS, from wireless networking capabilities and georeferencing of samples, to the adoption of XML standards and the development of Internet purchasing. As of 2012, some LIMS have added additional characteristics that continue to shape how a LIMS is defined. Examples include the addition of clinical functionality, electronic laboratory notebook (ELN) functionality, as well a rise in the software as a service (SaaS) distribution model.
Technology Operations The LIMS is an evolving concept, with new features and functionality being added often. As laboratory demands change and technological progress continues, the functions of a LIMS will likely also change. Despite these changes, a LIMS tends to have a base set of functionality that defines it. That functionality can roughly be divided into five laboratory processing phases, with numerous software functions falling under each: • • • • •
the reception and log in of a sample and its associated customer data the assignment, scheduling, and tracking of the sample and the associated analytical workload the processing and quality control associated with the sample and the utilized equipment and inventory the storage of data associated with the sample analysis the inspection, approval, and compilation of the sample data for reporting and/or further analysis
There are several pieces of core functionality associated with these laboratory processing phases that tend to appear in most LIMS: Sample management The core function of LIMS has traditionally been the management of samples. This typically is initiated when a sample is received in the laboratory, at which point the sample will be registered in the LIMS. Some LIMS will allow the customer to place an "order" for a sample directly to the LIMS at which point the sample is generated in an "unreceived" state. The processing could then include a step where the sample container is registered and sent to the customer for the sample to be taken and then returned to the lab. The registration process may involve accessioning the sample and producing barcodes to affix to the sample container. Various other parameters such as clinical or A lab worker matches blood samples to phenotypic information corresponding with the sample are also often documents. With a LIMS, this sort of sample management is made more efficient. recorded. The LIMS then tracks chain of custody as well as sample location. Location tracking usually involves assigning the sample to a particular freezer location, often down to the granular level of shelf, rack, box, row, and column. Other event tracking such as freeze and thaw cycles that a sample undergoes in the laboratory may be required. Modern LIMS have implemented extensive configurability, as each laboratory's needs for tracking additional data points can vary widely. LIMS vendors cannot typically make assumptions about what these data tracking needs are, and therefore vendors must create LIMS that are adaptable to individual environments. LIMS users may also have regulatory concerns to comply with such as CLIA, HIPAA, GLP, and FDA specifications, affecting certain aspects
62
Laboratory information management system of sample management in a LIMS solution. One key to compliance with many of these standards is audit logging of all changes to LIMS data, and in some cases a full electronic signature system is required for rigorous tracking of field-level changes to LIMS data. Instrument and application integration Modern LIMS offer an increasing amount of integration with laboratory instruments and applications. A LIMS may create control files that are "fed" into the instrument and direct its operation on some physical item such as a sample tube or sample plate. The LIMS may then import instrument results files to extract data for quality control assessment of the operation on the sample. Access to the instrument data can sometimes be regulated based on chain of custody assignments or other security features if need be. Modern LIMS products now also allow for the import and management of raw assay data results. Modern targeted assays such as qPCR and deep sequencing can produce tens of thousands of data points per sample. Furthermore, in the case of drug and diagnostic development as many as 12 or more assays may be run for each sample. In order to track this data, a LIMS solution needs to be adaptable to many different assay formats at both the data layer and import creation layer, while maintaining a high level of overall performance. Some LIMS products address this by simply attaching assay data as BLOBs to samples, but this limits the utility of that data in data mining and downstream analysis. Electronic data exchange The exponentially growing volume of data created in laboratories, coupled with increased business demands and focus on profitability, have pushed LIMS vendors to increase attention to how their LIMS handles electronic data exchanges. Attention must be paid to how an instrument's input and output data is managed, how remote sample collection data is imported and exported, and how mobile technology integrates with the LIMS. The successful transfer of data files in Microsoft Excel and other formats, as well as the import and export of data to Oracle, SQL, and Microsoft Access databases is a pivotal aspect of the modern LIMS. In fact, the transition "from proprietary databases to standardized database management systems such as Oracle ... and SQL" has arguably had one of the biggest impacts on how data is managed and exchanged in laboratories. Additional functions Aside from the key functions of sample management, instrument and application integration, and electronic data exchange, there are numerous additional operations that can be managed in a LIMS. This includes but is not limited to: audit management fully track and maintain an audit trail barcode handling assign one or more data points to a barcode format; read and extract information from a barcode chain of custody assign roles and groups that dictate access to specific data records and who is managing them compliance follow regulatory standards that affect the laboratory customer relationship management handle the demographic information and communications for associated clients document management process and convert data to certain formats; manage how documents are distributed and accessed
63
Laboratory information management system instrument calibration and maintenance schedule important maintenance and calibration of lab instruments and keep detailed records of such activities inventory and equipment management measure and record inventories of vital supplies and laboratory equipment manual and electronic data entry provide fast and reliable interfaces for data to be entered by a human or electronic component method management provide one location for all laboratory process and procedure (P&P) and methodology to be housed and managed as well as connecting each sample handling step with current instructions for performing the operation personnel and workload management organize work schedules, workload assignments, employee demographic information, training, and financial information quality assurance and control gauge and control sample quality, data entry standards, and workflow reports create and schedule reports in a specific format; schedule and distribute reports to designated parties time tracking calculate and maintain processing and handling times on chemical reactions, workflows, and more traceability show audit trail and/or chain of custody of a sample workflows track a sample, a batch of samples, or a "lot" of batches through its lifecycle
Client-side options A LIMS has utilized many architectures and distribution models over the years. As technology has changed, how a LIMS is installed, managed, and utilized has also changed with it. The following represents architectures which have been utilized at one point or another: Thick-client A thick-client LIMS is a more traditional client/server architecture, with some of the system residing on the computer or workstation of the user (the client) and the rest on the server. The LIMS software is installed on the client computer, which does all of the data processing. Later it passes information to the server, which has the primary purpose of data storage. Most changes, upgrades, and other modifications will happen on the client side. This was one of the first architectures implemented into a LIMS, having the advantage of providing higher processing speeds (because processing is done on the client and not the server) and slightly more security (as access to the server data is limited only to those with client software). Additionally, thick-client systems have also provided more interactivity and customization, though often at a greater learning curve. The disadvantages of client-side LIMS include the need for more robust client computers and more time-consuming upgrades, as well as a lack of base functionality through a web browser. The thick-client LIMS can become web-enabled through an add-on component.
64
Laboratory information management system Thin-client A thin-client LIMS is a more modern architecture which offers full application functionality accessed through a device's web browser. The actual LIMS software resides on a server (host) which feeds and processes information without saving it to the user's hard disk. Any necessary changes, upgrades, and other modifications are handled by the entity hosting the server-side LIMS software, meaning all end-users see all changes made. To this end, a true thin-client LIMS will leave no "footprint" on the client's computer, and only the integrity of the web browser need be maintained by the user. The advantages of this system include significantly lower cost of ownership and fewer network and client-side maintenance expenses. However, this architecture has the disadvantage of requiring real-time server access, a need for increased network throughput, and slightly less functionality. A sort of hybrid architecture that incorporates the features of thin-client browser usage with a thick client installation exists in the form of a web-based LIMS. Some LIMS vendors are beginning to rent hosted, thin-client solutions as "software as a service" (SaaS). These solutions tend to be less configurable than on premise solutions and are therefore considered for less demanding implementations such as laboratories with few users and limited sample processing volumes. Another implementation of the thin client architecture is the maintenance, warranty, and support (MSW) agreement. Pricing levels are typically based on a percentage of the license fee, with a standard level of service for 10 concurrent users being approximately 10 hours of support and additional customer service, at a roughly $200 per hour rate. Though some may choose to opt out of an MSW after the first year, it's often more economical to continue the plan in order to receive updates to the LIMS, giving it a longer life span in the laboratory. Web-enabled A web-enabled LIMS architecture is essentially a thick-client architecture with an added web browser component. In this setup, the client-side software has additional functionality that allows users to interface with the software through their device's browser. This functionality is typically limited only to certain functions of the web client. The primary advantage of a web-enabled LIMS is the end-user can access data both on the client side and the server side of the configuration. As in a thick-client architecture, updates in the software must be propagated to every client machine. However, the added disadvantages of requiring always-on access to the host server and the need for cross-platform functionality mean that additional overhead costs may arise. Web-based Arguably one of the most confusing architectures, web-based LIMS architecture is a hybrid of the thick- and thin-client architectures. While much of the client-side work is done through a web browser, the LIMS also requires the additional support of Microsoft's .NET Framework technology installed on the client device. The end result is a process that is apparent to the end-user through the Microsoft-compatible web browser, but perhaps not so apparent as it runs thick-client-like processing in the background. In this case, web-based architecture has the advantage of providing more functionality through a more friendly web interface. The disadvantages of this setup are more sunk costs in system administration and support for Internet Explorer and .NET technologies, and reduced functionality on mobile platforms.
Configurability LIMS implementations are notorious for often being lengthy and costly. This is due in part to the diversity of requirements within each lab, but also to the inflexible nature of LIMS products for adapting to these widely varying requirements. Newer LIMS solutions are beginning to emerge that take advantage of modern techniques in software design that are inherently more configurable and adaptable — particularly at the data layer — than prior solutions. This means not only that implementations are much faster, but also that the costs are lower and the risk of obsolescence is minimized.
65
Laboratory information management system
Distinction between a LIMS and a LIS Up until recently, the LIMS and laboratory information system (LIS) have exhibited a few key differences, making them noticeably separate entities. • A LIMS traditionally has been designed to process and report data related to batches of samples from biology labs, water treatment facilities, drug trials, and other entities that handle complex batches of data. A LIS has been designed primarily for processing and reporting data related to individual patients in a clinical setting. • A LIMS needs to satisfy good manufacturing practice (GMP) and meet the reporting and audit needs of the U.S. Food and Drug Administration and research scientists in many different industries. A LIS, however, must satisfy the reporting and auditing needs of hospital accreditation agencies, HIPAA, and other clinical medical practitioners. • A LIMS is most competitive in group-centric settings (dealing with "batches" and "samples") that often deal with mostly anonymous research-specific laboratory data, whereas a LIS is usually most competitive in patient-centric settings (dealing with "subjects" and "specimens") and clinical labs.
Standards A LIMS covers standards such as: • • • •
21 CFR Part 11 from the Food and Drug Administration (United States) ISO/IEC 17025 ISO 15189 Good laboratory practice
Further reading • Gibbon, G.A. (1996). "A brief history of LIMS" [1] (PDF). Laboratory Automation and Information Management 32 (1): 1–5. doi:10.1016/1381-141X(95)00024-K [2]. • Wood, Simon (September 2007). "Comprehensive Laboratory Informatics: A Multilayer Approach" [3] (PDF). American Laboratory. p.1. • Jones, John (2012). "The LIMS Book & Buyer's Guide" [4]. Laboratory Informatics Institute,Inc.
References [1] [2] [3] [4]
http:/ / www. sciencedirect. com/ science/ article/ pii/ 1381141X9500024K http:/ / dx. doi. org/ 10. 1016%2F1381-141X%2895%2900024-K http:/ / www. starlims. com/ Intl/ AL-Wood-Reprint-9-07. pdf http:/ / www. limsbook. com
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Laboratory information system
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Laboratory information system A Laboratory Information Management System (LIMS), sometimes referred to as a Laboratory Information System (LIS) or Laboratory Management System (LMS), is a software-based laboratory and information management system that offers a set of key features that support a modern laboratory's operations. Those key features include — but are not limited to — workflow and data tracking support, flexible architecture, and smart data exchange interfaces, which fully "support its use in regulated environments." The features and uses of a LIMS have evolved over the years from simple sample tracking to an enterprise resource planning tool that manages multiple aspects of laboratory informatics.
Laboratories around the world depend on a LIMS to manage data, assign rights, manage inventory, and more.
The definition of a LIMS is somewhat controversial: LIMSs are dynamic because the modern laboratory's requirements are rapidly evolving and needs often vary significantly from lab to lab. Therefore, a working definition of a LIMS ultimately depends on the interpretation by the individuals or groups involved. Dr. Alan McLelland of the Institute of Biochemistry, Royal Infirmary, Glasgow highlighted this problem in the late 1990s by explaining how a LIMS is perceived by an analyst, a laboratory manager, an information systems manager, and an accountant, "all of them correct, but each of them limited by the users' own perceptions." Historically the LIMS, LIS, and Process Development Execution System (PDES) have all performed similar functions. Historically the term "LIMS" has tended to be used to reference informatics systems targeted for environmental, research, or commercial analysis such as pharmaceutical or petrochemical work. "LIS" has tended to be used to reference laboratory informatics systems in the forensics and clinical markets, which often required special case management tools. The term "PDES" has generally applied to a wider scope, including, for example, virtual manufacturing techniques, while not necessarily integrating with laboratory equipment. In recent times LIMS functionality has spread even farther beyond its original purpose of sample management. Assay data management, data mining, data analysis, and electronic laboratory notebook (ELN) integration are all features that have been added to many LIMS, enabling the realization of translational medicine completely within a single software solution. Additionally, the distinction between a LIMS and a LIS has blurred, as many LIMS now also fully support comprehensive case-centric clinical data.
History Up until the late 1970s, the management of laboratory samples and the associated analysis and reporting were time-consuming manual processes often riddled with transcription errors. This gave some organizations impetus to streamline the collection of data and how it was reported. Custom in-house solutions were developed by a few individual laboratories, while some enterprising entities at the same time sought to develop a more commercial reporting solution in the form of special instrument-based systems. In 1982 the first generation of LIMS was introduced in the form of a single centralized minicomputer, which offered laboratories the first opportunity to utilize automated reporting tools. As the interest in these early LIMS grew, industry leaders like Gerst Gibbon of the Federal Energy Technology Centre in Pittsburgh began planting the seeds through LIMS-related conferences. By 1988 the second-generation commercial offerings were tapping into relational databases to expand LIMS into more application-specific territory, and International LIMS Conferences were in full swing. As personal computers became more powerful and prominent, a third generation of LIMS emerged in the early 1990s. These new LIMS took advantage of the developing client/server architecture, allowing laboratories to
Laboratory information system implement better data processing and exchanges. By 1995 the client/server tools had developed to the point of allowing processing of data anywhere on the network. Web-enabled LIMS were introduced the following year, enabling researchers to extend operations outside the confines of the laboratory. From 1996 to 2002 additional functionality was included in LIMS, from wireless networking capabilities and georeferencing of samples, to the adoption of XML standards and the development of Internet purchasing. As of 2012, some LIMS have added additional characteristics that continue to shape how a LIMS is defined. Examples include the addition of clinical functionality, electronic laboratory notebook (ELN) functionality, as well a rise in the software as a service (SaaS) distribution model.
Technology Operations The LIMS is an evolving concept, with new features and functionality being added often. As laboratory demands change and technological progress continues, the functions of a LIMS will likely also change. Despite these changes, a LIMS tends to have a base set of functionality that defines it. That functionality can roughly be divided into five laboratory processing phases, with numerous software functions falling under each: • • • • •
the reception and log in of a sample and its associated customer data the assignment, scheduling, and tracking of the sample and the associated analytical workload the processing and quality control associated with the sample and the utilized equipment and inventory the storage of data associated with the sample analysis the inspection, approval, and compilation of the sample data for reporting and/or further analysis
There are several pieces of core functionality associated with these laboratory processing phases that tend to appear in most LIMS: Sample management The core function of LIMS has traditionally been the management of samples. This typically is initiated when a sample is received in the laboratory, at which point the sample will be registered in the LIMS. Some LIMS will allow the customer to place an "order" for a sample directly to the LIMS at which point the sample is generated in an "unreceived" state. The processing could then include a step where the sample container is registered and sent to the customer for the sample to be taken and then returned to the lab. The registration process may involve accessioning the sample and producing barcodes to affix to the sample container. Various other parameters such as clinical or A lab worker matches blood samples to phenotypic information corresponding with the sample are also often documents. With a LIMS, this sort of sample management is made more efficient. recorded. The LIMS then tracks chain of custody as well as sample location. Location tracking usually involves assigning the sample to a particular freezer location, often down to the granular level of shelf, rack, box, row, and column. Other event tracking such as freeze and thaw cycles that a sample undergoes in the laboratory may be required. Modern LIMS have implemented extensive configurability, as each laboratory's needs for tracking additional data points can vary widely. LIMS vendors cannot typically make assumptions about what these data tracking needs are, and therefore vendors must create LIMS that are adaptable to individual environments. LIMS users may also have regulatory concerns to comply with such as CLIA, HIPAA, GLP, and FDA specifications, affecting certain aspects
68
Laboratory information system of sample management in a LIMS solution. One key to compliance with many of these standards is audit logging of all changes to LIMS data, and in some cases a full electronic signature system is required for rigorous tracking of field-level changes to LIMS data. Instrument and application integration Modern LIMS offer an increasing amount of integration with laboratory instruments and applications. A LIMS may create control files that are "fed" into the instrument and direct its operation on some physical item such as a sample tube or sample plate. The LIMS may then import instrument results files to extract data for quality control assessment of the operation on the sample. Access to the instrument data can sometimes be regulated based on chain of custody assignments or other security features if need be. Modern LIMS products now also allow for the import and management of raw assay data results. Modern targeted assays such as qPCR and deep sequencing can produce tens of thousands of data points per sample. Furthermore, in the case of drug and diagnostic development as many as 12 or more assays may be run for each sample. In order to track this data, a LIMS solution needs to be adaptable to many different assay formats at both the data layer and import creation layer, while maintaining a high level of overall performance. Some LIMS products address this by simply attaching assay data as BLOBs to samples, but this limits the utility of that data in data mining and downstream analysis. Electronic data exchange The exponentially growing volume of data created in laboratories, coupled with increased business demands and focus on profitability, have pushed LIMS vendors to increase attention to how their LIMS handles electronic data exchanges. Attention must be paid to how an instrument's input and output data is managed, how remote sample collection data is imported and exported, and how mobile technology integrates with the LIMS. The successful transfer of data files in Microsoft Excel and other formats, as well as the import and export of data to Oracle, SQL, and Microsoft Access databases is a pivotal aspect of the modern LIMS. In fact, the transition "from proprietary databases to standardized database management systems such as Oracle ... and SQL" has arguably had one of the biggest impacts on how data is managed and exchanged in laboratories. Additional functions Aside from the key functions of sample management, instrument and application integration, and electronic data exchange, there are numerous additional operations that can be managed in a LIMS. This includes but is not limited to: audit management fully track and maintain an audit trail barcode handling assign one or more data points to a barcode format; read and extract information from a barcode chain of custody assign roles and groups that dictate access to specific data records and who is managing them compliance follow regulatory standards that affect the laboratory customer relationship management handle the demographic information and communications for associated clients document management process and convert data to certain formats; manage how documents are distributed and accessed
69
Laboratory information system instrument calibration and maintenance schedule important maintenance and calibration of lab instruments and keep detailed records of such activities inventory and equipment management measure and record inventories of vital supplies and laboratory equipment manual and electronic data entry provide fast and reliable interfaces for data to be entered by a human or electronic component method management provide one location for all laboratory process and procedure (P&P) and methodology to be housed and managed as well as connecting each sample handling step with current instructions for performing the operation personnel and workload management organize work schedules, workload assignments, employee demographic information, training, and financial information quality assurance and control gauge and control sample quality, data entry standards, and workflow reports create and schedule reports in a specific format; schedule and distribute reports to designated parties time tracking calculate and maintain processing and handling times on chemical reactions, workflows, and more traceability show audit trail and/or chain of custody of a sample workflows track a sample, a batch of samples, or a "lot" of batches through its lifecycle
Client-side options A LIMS has utilized many architectures and distribution models over the years. As technology has changed, how a LIMS is installed, managed, and utilized has also changed with it. The following represents architectures which have been utilized at one point or another: Thick-client A thick-client LIMS is a more traditional client/server architecture, with some of the system residing on the computer or workstation of the user (the client) and the rest on the server. The LIMS software is installed on the client computer, which does all of the data processing. Later it passes information to the server, which has the primary purpose of data storage. Most changes, upgrades, and other modifications will happen on the client side. This was one of the first architectures implemented into a LIMS, having the advantage of providing higher processing speeds (because processing is done on the client and not the server) and slightly more security (as access to the server data is limited only to those with client software). Additionally, thick-client systems have also provided more interactivity and customization, though often at a greater learning curve. The disadvantages of client-side LIMS include the need for more robust client computers and more time-consuming upgrades, as well as a lack of base functionality through a web browser. The thick-client LIMS can become web-enabled through an add-on component.
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Laboratory information system Thin-client A thin-client LIMS is a more modern architecture which offers full application functionality accessed through a device's web browser. The actual LIMS software resides on a server (host) which feeds and processes information without saving it to the user's hard disk. Any necessary changes, upgrades, and other modifications are handled by the entity hosting the server-side LIMS software, meaning all end-users see all changes made. To this end, a true thin-client LIMS will leave no "footprint" on the client's computer, and only the integrity of the web browser need be maintained by the user. The advantages of this system include significantly lower cost of ownership and fewer network and client-side maintenance expenses. However, this architecture has the disadvantage of requiring real-time server access, a need for increased network throughput, and slightly less functionality. A sort of hybrid architecture that incorporates the features of thin-client browser usage with a thick client installation exists in the form of a web-based LIMS. Some LIMS vendors are beginning to rent hosted, thin-client solutions as "software as a service" (SaaS). These solutions tend to be less configurable than on premise solutions and are therefore considered for less demanding implementations such as laboratories with few users and limited sample processing volumes. Another implementation of the thin client architecture is the maintenance, warranty, and support (MSW) agreement. Pricing levels are typically based on a percentage of the license fee, with a standard level of service for 10 concurrent users being approximately 10 hours of support and additional customer service, at a roughly $200 per hour rate. Though some may choose to opt out of an MSW after the first year, it's often more economical to continue the plan in order to receive updates to the LIMS, giving it a longer life span in the laboratory. Web-enabled A web-enabled LIMS architecture is essentially a thick-client architecture with an added web browser component. In this setup, the client-side software has additional functionality that allows users to interface with the software through their device's browser. This functionality is typically limited only to certain functions of the web client. The primary advantage of a web-enabled LIMS is the end-user can access data both on the client side and the server side of the configuration. As in a thick-client architecture, updates in the software must be propagated to every client machine. However, the added disadvantages of requiring always-on access to the host server and the need for cross-platform functionality mean that additional overhead costs may arise. Web-based Arguably one of the most confusing architectures, web-based LIMS architecture is a hybrid of the thick- and thin-client architectures. While much of the client-side work is done through a web browser, the LIMS also requires the additional support of Microsoft's .NET Framework technology installed on the client device. The end result is a process that is apparent to the end-user through the Microsoft-compatible web browser, but perhaps not so apparent as it runs thick-client-like processing in the background. In this case, web-based architecture has the advantage of providing more functionality through a more friendly web interface. The disadvantages of this setup are more sunk costs in system administration and support for Internet Explorer and .NET technologies, and reduced functionality on mobile platforms.
Configurability LIMS implementations are notorious for often being lengthy and costly. This is due in part to the diversity of requirements within each lab, but also to the inflexible nature of LIMS products for adapting to these widely varying requirements. Newer LIMS solutions are beginning to emerge that take advantage of modern techniques in software design that are inherently more configurable and adaptable — particularly at the data layer — than prior solutions. This means not only that implementations are much faster, but also that the costs are lower and the risk of obsolescence is minimized.
71
Laboratory information system
Distinction between a LIMS and a LIS Up until recently, the LIMS and laboratory information system (LIS) have exhibited a few key differences, making them noticeably separate entities. • A LIMS traditionally has been designed to process and report data related to batches of samples from biology labs, water treatment facilities, drug trials, and other entities that handle complex batches of data. A LIS has been designed primarily for processing and reporting data related to individual patients in a clinical setting. • A LIMS needs to satisfy good manufacturing practice (GMP) and meet the reporting and audit needs of the U.S. Food and Drug Administration and research scientists in many different industries. A LIS, however, must satisfy the reporting and auditing needs of hospital accreditation agencies, HIPAA, and other clinical medical practitioners. • A LIMS is most competitive in group-centric settings (dealing with "batches" and "samples") that often deal with mostly anonymous research-specific laboratory data, whereas a LIS is usually most competitive in patient-centric settings (dealing with "subjects" and "specimens") and clinical labs.
Standards A LIMS covers standards such as: • • • •
21 CFR Part 11 from the Food and Drug Administration (United States) ISO/IEC 17025 ISO 15189 Good laboratory practice
Further reading • Gibbon, G.A. (1996). "A brief history of LIMS" [1] (PDF). Laboratory Automation and Information Management 32 (1): 1–5. doi:10.1016/1381-141X(95)00024-K [2]. • Wood, Simon (September 2007). "Comprehensive Laboratory Informatics: A Multilayer Approach" [3] (PDF). American Laboratory. p.1. • Jones, John (2012). "The LIMS Book & Buyer's Guide" [4]. Laboratory Informatics Institute,Inc.
References
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MHealth mHealth (also written as m-health or mobile health) is a term used for the practice of medicine and public health, supported by mobile devices. The term is most commonly used in reference to using mobile communication devices, such as mobile phones, tablet computers and PDAs, for health services and information, but also to affect emotional states. The mHealth field has emerged as a sub-segment of eHealth, the use of information and communication technology (ICT), such as computers, mobile phones, communications satellite, patient monitors, etc., for health services and information. mHealth applications include the use of mobile devices in collecting community and clinical health data, delivery of healthcare information to practitioners, researchers, and patients, real-time monitoring of patient vital signs, and direct provision of care (via mobile telemedicine).[1]
Medical nurse uses a mobile phone in Accra, Ghana
While mHealth certainly has application for industrialized nations, the field has emerged in recent years as largely an application for developing countries, stemming from the rapid rise of mobile phone penetration in low-income nations. The field, then, largely emerges as a means of providing greater access to larger segments of a population in developing countries, as well as improving the capacity of health systems in such countries to provide quality healthcare. Within the mHealth space, projects operate with a variety of objectives, including increased access to healthcare and health-related information (particularly for hard-to-reach populations); improved ability to diagnose and track diseases; timelier, more actionable public health information; and expanded access to ongoing medical education and training for health workers. According to the analyst firm Berg Insight, around 2.8 million patients worldwide were using a home monitoring service based on equipment with integrated connectivity at the end of 2012. The figure does not include patients that use monitoring devices connected to a PC or mobile phone. It only includes systems that rely on monitors with integrated connectivity or systems that use monitoring hubs with integrated cellular or fixed-line modems. Berg Insight forecasts that the number of home monitoring systems with integrated communication capabilities will grow at a compound annual growth rate (CAGR) of 26.9 percent between 2011 and 2017 reaching 9.4 million connections globally by the end of the forecast period. The number of these devices that have integrated cellular connectivity increased from 0.73 million in 2011 to about 1.03 million in 2012, and is projected to grow at a CAGR of 46.3 percent to 7.10 million in 2017.[2]
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Definitions Mobile eHealth or mHealth broadly encompasses the use of mobile telecommunication and multimedia technologies as they are integrated within increasingly mobile and wireless health care delivery systems.The field broadly encompasses the use of mobile telecommunication and multimedia technologies in health care delivery. The term mHealth was coined by Professor Robert Istepanian as use of "emerging mobile communications and network technologies for healthcare". A definition used at the 2010 mHealth Summit of the Foundation for the National Institutes of Health (FNIH) was "the delivery of healthcare services via mobile communication devices".
Malaria Clinic in Tanzania helped by SMS for Life program that uses cell phones to efficiently deliver malaria vaccine
While there are some projects that are considered solely within the field of mHealth, the linkage between mHealth and eHealth is unquestionable. For example, an mHealth project that uses mobile phones to access data on HIV/AIDS rates would require an eHealth system in order to manage, store, and assess the data. Thus, eHealth projects many times operate as the backbone of mHealth projects. In a similar vein, while not clearly bifurcated by such a definition, eHealth can largely be viewed as technology that supports the functions and delivery of healthcare, while mHealth rests largely on providing healthcare access. Because mHealth is by definition based on mobile technology such as smartphones, healthcare, through information and delivery, can better reach areas, people, and/or healthcare practitioners with previously limited exposure to certain aspects of healthcare.
Motivation of mHealth Mobile Health is one aspect of eHealth that is pushing the limits of how to acquire, transport, store, process, and secure the raw and processed data to deliver meaningful results. mHealth offers the ability of remote individuals to participate in the health care value matrix, which may not have been possible in the past. Participation does not imply just consumption of health care services. In many cases remote users are valuable contributors to gather data regarding disease and public health concerns such as outdoor pollution, drugs and violence. The motivation behind the development of the mHealth field arises from two factors. The first factor concerns the myriad constraints felt by healthcare systems of developing nations. These constraints include high population growth, a high burden of disease prevalence,[3] low health care workforce, large numbers of rural inhabitants, and limited financial resources to support healthcare infrastructure and health information systems. The second factor is the recent rapid rise in mobile phone penetration in developing countries to large segments of the healthcare workforce, as well as the population of a country as a whole.[4] With greater access to mobile phones to all segments of a country, including rural areas, the potential of lowering information and transaction costs in order to deliver healthcare improves. The combination of these two factors have motivated much discussion of how greater access to mobile phone technology can be leveraged to mitigate the numerous pressures faced by developing countries' healthcare systems. Both factors are discussed here.
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Healthcare and mHealth in low- and middle-income countries Middle income and especially low-income countries face a plethora of constraints in their healthcare systems. These countries face a severe lack of human and physical resources, as well as some of the largest burdens of disease, extreme poverty, and large population growth rates. Additionally, healthcare access to all reaches of society is generally low in these countries.
more than 80,000}
According to a World Health Organization (WHO) report from June 2011, higher-income countries show more mHealth activity than do lower-income countries (as consistent with eHealth trends in general). Countries in the European Region are currently the most active and those in the African Region the least active. The WHO report findings also included that mHealth is most easily incorporated into processes and services that historically use voice communication through conventional telephone networks. The report [5] was the result of a mHealth survey module designed by researchers at the Earth Institute's Center for Global Health and Economic Development [6], Columbia University. The WHO notes an extreme deficit within the global healthcare workforce. The WHO notes critical healthcare workforce shortages in 57 countries—most of which are characterized as developing countries—and a global deficit of 2.4 million doctors, nurses, and midwives.[7] The WHO, in a study of the healthcare workforce in 12 countries of Africa, finds an average density of physicians, nurses and midwives per 1000 population of 0.64.[8] The density of the same metric is four times as high in the United States, at 2.6.[9] The burden of disease is additionally much higher in low- and middle-income countries than high-income countries. The burden of disease, measured in disability-adjusted life year (DALY), which can be thought of as a measurement of the gap between current health status and an ideal situation where everyone lives into old age, free of disease and disability, is about five times higher in Africa than in high-income countries.[10] In addition, low- and middle-income countries are forced to face the burdens of both extreme poverty and the growing incidence of chronic diseases, such as diabetes and heart disease, an effect of new-found (relative) affluence. Considering poor infrastructure and low human resources, the WHO notes that the healthcare workforce in sub-Saharan Africa would need to be scaled up by as much as 140% to attain international health development targets such as those in the Millennium Declaration.[11] The WHO, in reference to the healthcare condition in sub-saharan Africa, states: The problem is so serious that in many instances there is simply not enough human capacity even to absorb, deploy and efficiently use the substantial additional funds that are considered necessary to improve health in these countries. Mobile technology has made a recent and rapid appearance into low- and middle-income nations.[12] While, in the mHealth field, mobile technology usually refers to mobile phone technology, the entrance of other technologies into these nations to facilitate healthcare are also discussed here. Health and development The link between health and development can be found in three of the Millennium Development Goals (MDGs), as set forth by the United Nations Millennium Declaration in 2000. The MDGs that specifically address health include reducing child mortality; improving maternal health; combating HIV and AIDS, malaria, and other diseases; and increasing access to safe drinking water.[13] A progress report published in 2006 indicates that childhood immunization and deliveries by skilled birth attendants are on the rise, while many regions continue to struggle to achieve reductions in the prevalence of the diseases of poverty including malaria, HIV and AIDS and tuberculosis.[14]
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Healthcare and mHealth in developed countries In developed countries, healthcare systems have different policies and goals in relation to the personal and population health care goals. In US and EU many patients and consumers use their cell phones and tablets to access health information and look for healthcare services. In parallel the number of mHealth applications grew significantly the last years. Doctors, nurses and clinicians use mobile devices to access patient information and other databases and resources.
Technology and Market Basic SMS functions and real-time voice communication serve as the backbone and the current most common use of mobile phone technology. The broad range of potential benefits to the health sector that the simple functions of mobile phones can provide should not be understated.[15] The appeal of mobile communication technologies is that they enable communication in motion, allowing individuals to contact each other irrespective of time and place.[16][17] This is particularly beneficial for work in remote areas where the mobile phone, and now increasingly wireless infrastructure, is able to reach more people, faster. As a result of such technological advances, the capacity for improved access to information and two-way communication becomes more available at the point of need.
Mobile Phones Mobile phones have made a recent and rapid entrance into many parts of the low- and middle-income world, with the global Mobile phone penetration rate drastically increasing over the last decade. Improvements in telecommunications technology infrastructure, reduced costs of mobile handsets, and a general increase in non-food expenditure have influenced this trend. Low- and middle-income countries are utilizing mobile phones as "leapfrog technology" (see leapfrogging). That is, mobile phones have allowed many developing countries, even those with relatively poor infrastructure, to bypass 20th century fixed-line technology and jump to modern mobile technology.[18] Mobile phone subscribers per 100 inhabitants 1997–2007
The number of global mobile phone subscribers in 2007 was estimated at 3.1 billion of an estimated global population of 6.6 billion (47%).[19] These figures are expected to grow to 4.5 billion by 2012, or a 64.7% mobile penetration rate. The greatest growth is expected in Asia, the Middle East, and Africa. In many countries, the number of mobile phone subscribers has bypassed the number of fixed-line telephones, this is particularly true in developing countries.[20] Globally, there were 4.1 billion mobile phones in use in December 2008 . See List of countries by number of mobile phones in use. While mobile phone penetration rates are on the rise, globally, the growth within countries is not generally evenly distributed. In India, for example, while mobile penetration rates have increased markedly, by far the greatest growth rates are found in urban areas. Mobile penetration, in September 2008, was 66% in urban areas, while only 9.4% in rural areas. The all India average was 28.2% at the same time.[21] So, while mobile phones may have the potential to provide greater healthcare access to a larger portion of a population, there are certainly within-country equity issues to consider.
MHealth Mobile phones are spreading because the cost of mobile technology deployment is dropping and people are, on average, getting wealthier in low- and middle-income nations.[22] Vendors, such as Nokia, are developing cheaper infrastructure technologies (CDMA) and cheaper phones (sub $50–100, such as Sun's Java phone). Non-food consumption expenditure is increasing in many parts of the developing world, as disposable income rises, causing a rapid increase spending on new technology, such as mobile phones. In India, for example, consumers have become and continue to become wealthier. Consumers are shifting their expenditure from necessity to discretionary. For example, on average, 56% of Indian consumers' consumption went towards food in 1995, compared to 42% in 2005. The number is expected to drop to 34% by 2015. That being said, although total share of consumption has declined, total consumption of food and beverages increased 82% from 1985 to 2005, while per-capita consumption of food and beverages increased 24%. Indian consumers are getting wealthier and they are spending more and more, with a greater ability to spend on new technologies.[23]
Smartphones More advanced mobile phone technologies are enabling the potential for further healthcare delivery. Smartphone technologies are now in the hands of a large number of physicians and other healthcare workers in low- and middle-income countries. Although far from ubiquitous, the spread of Smartphone technologies opens up doors for mHealth projects such as technology-based diagnosis support, remote diagnostics and telemedicine, web browsing, GPS navigation, access to web-based patient information, post-visit patient surveillance, and decentralized health management information systems (HMIS). While uptake of Smartphone technology by the medical field has grown in low- and middle-income countries, it is worth noting that the capabilities of mobile phones in low- and middle-income countries has not reached the sophistication of those in high-income countries. The infrastructure that enables web browsing, GPS navigation, and email through Smartphones is not as well developed in much of the low- and middle-income countries.[24] Increased availability and efficiency in both voice and data-transfer systems in addition to rapid deployment of wireless infrastructure will likely accelerate the deployment of mobile-enabled health systems and services throughout the world.[25]
Other mHealth Technologies Beyond mobile phones, wireless-enabled laptops and specialized health-related software applications are currently being developed, tested, and marketed for use in the mHealth field. Many of these technologies, while having some application to low- and middle-income nations, are developing primarily in high-income countries. However, with broad advocacy campaigns for free and open source software (FOSS), applications are beginning to be tailored for and make inroads in low- and middle-income countries. Some other mHealth technologies include • • • • • • •
Patient monitoring devices Mobile telemedicine/telecare devices MP3 players for mLearning Laptop computers Microcomputers Data collection software Mobile Operating System Technology
Mobile Device Operating System Technology Technologies relates to the Operating Systems that orchestrate mobile device hardware while maintaining confidentiality, integrity and availability are required to build trust. This may foster greater adoption of mHealth Technologies and Services, by exploiting lower cost multi purpose mobile devices such as tablets pcs and smart phones. Devices in this class may include Apple's iPad 1&2 and Motorola's Xoom. Operating systems that control
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MHealth these emerging classes of devices include Google's Android, Apple's iPhone OS, Microsoft's Windows Mobile, Nokia Symbian OS and RIM's BlackBerry OS. Operating Systems must be agile and evolve to effectively balance and deliver the desired level of service to an application and end user, while managing display real estate, power consumption and security posture. As advances in capabilities such as integrating voice, video and Web 2.0 collaboration tools into mobile devices, significant benefits can be achieved in the delivery of health care services. New sensor technologies such as HD video and audio capabilities, accelerometers, GPS, ambient light detectors, barometers and gyroscopes can enhance the methods of describing and studying cases, close to the patient or consumer of the health care service. This could include diagnosis, education, treatment and monitoring. Air Quality Sensing Technologies Environmental conditions have a significant impact to public health. Per the World Health Organization, outdoor air pollution accounts for about 1.4% of total mortality. Utilizing Participatory sensing technologies in mobile telephone, public health research can exploit the wide penetration of mobile devices to collect air measurements, which can be utilized to assess the impact of pollution. Projects such as the Urban Atmospheres [26] are utilizing embedded technologies in mobile phones to acquire real time conditions from millions of user mobile phones. By aggregating this data, public health policy shall be able to craft initiatives to mitigate risk associated with outdoor air pollution. Data has become an especially important aspect of mHealth. Data collection requires both the collection device (mobile phones, computer, or portable device) and the software that houses the information. Data is primarily focused on visualizing static text but can also extend to interactive decision support algorithms, other visual image information, and also communication capabilities through the integration of e-mail and SMS features. Integrating use of GIS and GPS with mobile technologies adds a geographical mapping component that is able to "tag" voice and data communication to a particular location or series of locations. These combined capabilities have been used for emergency health services as well as for disease surveillance, health facilities and services mapping, and other health-related data collection.
mHealth and Health Outcomes The mHealth field operates on the premise that technology integration within the health sector has the great potential to promote a better health communication to achieve healthy lifestyles, improve decision-making by health professionals (and patients) and enhance healthcare quality by improving access to medical and health information and facilitating instantaneous communication in places where this was not previously possible.[27][28] It follows that the increased use of technology can help reduce health care costs by improving efficiencies in the health care system and promoting prevention through behavior change communication (BCC). The mHealth field also houses the idea that there exists a powerful potential to advance clinical care and public health services by facilitating health professional practice and communication and reducing health disparities through the use of mobile technology. The growth of health-related Apps and the availability of mobile device drives the growth of mHealth.In 2010, only about 4,000 health-related app available and now more than 20,000 health-related apps are available for mobile device. Revenues from remote patient monitoring services that use mobile networks will rise to $1.9 billion globally by 2014, according to Juniper Research’s recent report in 2011. Efforts are ongoing to explore how a broad range of technologies, and most recently mHealth technologies, can improve such health outcomes as well as generate cost savings within the health systems of low- and middle-income countries. In some ways, the potential of mHealth lies in its ability to offer opportunities for direct voice communication (of particular value in areas of poor literacy rates and limited local language-enable phones) and information transfer capabilities that previous technologies did not have. Overall, mobile communication technologies are tools that can be leveraged to support existing workflows within the health sector and between the health sector and the general public.[29]
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MHealth Within the mHealth space, projects operate with a variety of objectives, as stated by the UN Foundation and Vodafone Foundation's report on mHealth for Development: • • • •
increased access to healthcare and health-related information (particularly for hard-to-reach populations) improved ability to diagnose and track diseases timelier, more actionable public health information expanded access to ongoing medical education and training for health workers
Applications in the mHealth Field While others exist, the UN Foundation and Vodafone Foundation report presents seven application categories within the mHealth field. • • • • • •
Education and awareness Helpline Diagnostic and treatment support Communication and training for healthcare workers Disease and epidemic outbreak tracking Remote monitoring
• Remote data collection Each application category as well as specific project within the category will be described.
Education and awareness Education and awareness programs within the mHealth field are largely about the spreading of mass information from source to recipient through short message services (SMS). In education and awareness applications, SMS messages are sent directly to users' phones to offer information about various subjects, including testing and treatment methods, availability of health services, and disease management. SMSs provide an advantage of being relatively unobtrusive, offering patients confidentiality in environments where disease (especially HIV/AIDS) is often taboo. Additionally, SMSs provide an avenue to reach far-reaching areas—such as rural areas—which may have limited access to public health information and education, health clinics, and a deficit of healthcare workers.
Helpline Helpline typically consists of a specific phone number that any individual is able to call to gain access to a range of medical services. These include phone consultations, counseling, service complaints, and information on facilities, drugs, equipment, and/or available mobile health clinics
Diagnostic support, treatment support, communication and training for healthcare workers Diagnostic and treatment support systems are typically designed to provide healthcare workers in remote areas advice about diagnosis and treatment of patients. While some projects may provide mobile phone applications—such as a step-by-step medical decision tree systems—to help healthcare workers diagnosis, other projects provide direct diagnosis to patients themselves. In such cases, known as telemedicine, patients might take a photograph of a wound or illness and allow a remote physician diagnose to help treat the medical problem. Both diagnosis and treatment support projects attempt to mitigate the cost and time of travel for patients located in remote areas mHealth projects within the communication and training for healthcare workers subset involve connecting healthcare workers to sources of information through their mobile phone. This involves connecting healthcare workers to other healthcare workers, medical institutions, ministries of health, or other houses of medical information. Such projects additionally involve using mobile phones to better organize and target in-person training. Improved communication projects attempt to increase knowledge transfer amongst healthcare workers and improve patient outcomes through
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MHealth such programs as patient referral processes
Disease surveillance, remote data collection, and epidemic outbreak tracking Projects within this area operate to utilize mobile phones' ability to collect and transmit data quickly, cheaply, and relatively efficiently. Data concerning the location and levels of specific diseases (such as malaria, HIV/AIDS, TB, Avian Flu) can help medical systems or ministries of health or other organizations identify outbreaks and better target medical resources to areas of greatest need. Such projects can be particularly useful during emergencies, in order to identify where the greatest medical needs are within a country Policymakers and health providers at the national, district, and community level need accurate data in order to gauge the effectiveness of existing policies and programs and shape new ones. In the developing world, collecting field information is particularly difficult since many segments of the population are rarely able to visit a hospital, even in the case of severe illness. A lack of patient data creates an arduous environment in which policy makes can decide where and how to spend their (sometimes limited) resources. While some software within this area is specific to a particular content or area, other software can be adapted to any data collection purpose.
Treatment support and medication compliance for patients, including chronic disease management Remote monitoring and treatment support allows for greater involvement in the continued care of patients. Recent studies seem to show also the efficacy of inducing positive and negative affective states, using smart phones. Within environments of limited resources and beds—and subsequently a 'outpatient' culture—remote monitoring allows healthcare workers to better track patient conditions, medication regimen adherence, and follow-up scheduling. Such projects can operate through either one- or two-way communications systems. Remote monitoring has been used particularly in the area of medication adherence for AIDS and diabetes; technical process evaluations have confirmed the feasibility of deploying dynamically tailored, SMS-based interventions designed to provide ongoing behavioral reinforcement for persons living with HIV. [30]
Emerging trends and areas of interest in mHealth • Emergency response systems (e.g., road traffic accidents, emergency obstetric care) • Human resources coordination, management, and supervision • Mobile synchronous (voice) and asynchronous (SMS) telemedicine diagnostic and decision support to remote clinicians[31] • Clinician-focused, evidence-based formulary, database and decision support information available at the point-of-care • Pharmaceutical Supply Chain Integrity & Patient Safety Systems (e.g. Sproxil and mPedigree)[32] • Clinical care and remote patient monitoring • Health extension services • Health services monitoring and reporting • Health-related mLearning for the general public • Training and continuing professional development for health care workers • Health promotion and community mobilization • Support of long-term conditions *[33], for example in diabetes self-management[34] According to Vodafone Group Foundation on February 13, 2008, a partnership for emergency communications was created between the group and United Nations Foundation. Such partnership will increase the effectiveness of the information and communications technology response to major emergencies and disasters around the world.
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References [1] Germanakos P., Mourlas C., & Samaras G. "A Mobile Agent Approach for Ubiquitous and Personalized eHealth Information Systems" (http:/ / www. media. uoa. gr/ ~pgerman/ publications/ published_papers/ A_Mobile_Agent_Approach_for_Ubiquitous_and_Personalized_eHealth_Information_Systems. pdf) Proceedings of the Workshop on 'Personalization for e-Health' of the 10th International Conference on User Modeling (UM'05). Edinburgh, July 29, 2005, pp. 67–70. [2] mHealth and Home Monitoring by Berg Insight [3] The Global Burden of Disease 2004 Update 2008 [4] Global Mobile Phone Subscribers to Reach 4.5 Billion by 2012 (http:/ / www. cellular-news. com/ story/ 29824. php). Cellular-news.com. 4/1/2010 [5] http:/ / www. who. int/ goe/ publications/ goe_mhealth_web. pdf [6] http:/ / cghed. ei. columbia. edu/ [7] World Health Organization, The World Health Report 2006: Working Together for Health. 2006, WHO: Geneva. [8] Kinfu, Y., Dal Poz, M., Mercer, H., Evans, D.B., The health worker shortage in Africa: are enough physicians and nurses being trained? (http:/ / www. who. int/ bulletin/ volumes/ 87/ 3/ 08-051599-table-T3. html) Bulletin of the World Health Organization, Vol. 87, No. 3, March 2009, 225-230 [9] UNData. Statistics Physicians density (per 10 000 population) (http:/ / data. un. org/ Data. aspx?d=WHO& f=inID:HSR02). WHO Data [10] The Global Burden of Disease 2004 Update 2008, [11] Kinfu, Y., Dal Poz, M., Mercer, H., Evans, D.B., The health worker shortage in Africa: are enough physicians and nurses being trained? (http:/ / www. who. int/ bulletin/ volumes/ 87/ 3/ 08-051599/ en/ index. html) Bulletin of the World Health Organization Vol. 87, No. 3, March 2009, 225–230 [12] Economist. "The power of mobile money". The Economist 14 Sept, 2009. [13] United Nations, United Nations Millennium Declaration (General Assembly Resolution 55/2). 2000, United Nations: New York. [14] United Nations, The Millennium Development Goals Report. 2006, United Nations: New York. [15] Mechael, P. (2006). Exploring Health-related Uses of Mobile Phones: An Egyptian Case Study, Public Health & Policy (p. 264). London: London School of Hygiene and Tropical Medicine [16] Agar, J. (2003). Constant Touch: A Global History of the Mobile Phone Cambridge: Icon Books Ltd. [17] Ling, R. (2004). The mobile connection: The cell phone's impact on society London: Morgan Kaufmann [18] Economist. Leaders: The limits of leapfrogging; Technology and development. The Economist: 2008 [19] Global Mobile Phone Subscribers to Reach 4.5 Billion by 2012. Cellular-news.com. 4/1/2010 http:/ / www. cellular-news. com/ story/ 29824. php [20] ITU (2003). Mobile overtakes fixed: Implications for policy and regulation. Geneva: International Telecommunications Union [21] Kathuria, R, Uppal M., Mamta (2009). An econometric analysis of the impact of mobile. Case paper in India: The impact of mobile phones. Vodafone Group Plc. The Policy Paper Series. November 2009 [22] Global Economic Prospects 2007: Managing the Next Wave of Globalization (http:/ / econ. worldbank. org/ WBSITE/ EXTERNAL/ EXTDEC/ EXTDECPROSPECTS/ GEPEXT/ EXTGEP2007/ 0,,menuPK:3016160~pagePK:64167702~piPK:64167676~theSitePK:3016125,00. html). World Bank report. [23] McKinsey&Company (2007). The 'bird of gold': the rise of India's consumer market. McKinsey Global Institute [24] Mechael, P. (2006). Exploring Health-related Uses of Mobile Phones: An Egyptian Case Study, Public Health & Policy (p. 264). London: London School of Hygiene and Tropical Medicine. [25] Istepanian, R. (2004). Introduction to the Special Section on M-Health: Beyond Seamless Mobility and Global Wireless Health-care Connectivity. IEEE Transactions on Information Technology in Biomedicine, 8(4), 405–413. [26] http:/ / www. urban-atmospheres. net/ ParticipatoryUrbanism/ index. html [27] Shields, T., A. Chetley, and J. Davis, ICT in the health sector: Summary of the online consultation. 2005, infoDev. [28] World Health Organization, eHealth Tools and Services: Needs of Member States. 2005, WHO: Geneva. [29] Malhotra K, Gardner S, Rees D. (2005). Evaluation of GPRS Enabled Secure Remote Patient Monitoring System. ASMTA 2005, Riga, Latvia, 41–48. [30] Furberg RD, Uhrig JD, Bann CM, Lewis MA, Harris JL, Williams P, Coomes C, Martin N, Kuhns L. Technical Implementation of a Multi-Component, Text Message–Based Intervention for Persons Living with HIV JMIR Res Protoc 2012;1(2):e17 http:/ / www. researchprotocols. org/ 2012/ 2/ e17/ [31] Mechael, P. "WHO mHealth Review: Towards the Development of an mHealth Strategy". August 2007. [32] Ghana News :: West African Innovation Hits Global Stage ::: Breaking News | News in Ghana | features (http:/ / news. myjoyonline. com/ features/ 201005/ 46200. asp). News.myjoyonline.com (2010-05-14). Retrieved on 2010-08-14. [33] http:/ / news. bbc. co. uk/ 1/ hi/ health/ 8436092. stm [34] Chomutare T, Fernandez-Luque L, Arsand E, Hartvigsen G. (2011). Features of mobile diabetes applications (http:/ / www. jmir. org/ 2011/ 3/ e65/ ) J Med Internet Res.;13(3):e65. PMID 21979293
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Further reading • "Technology plays crucial role in vaccination distribution" Computer Weekly: April 2008. Technology plays crucial role in vaccination distribution - 03/04/2008 (http://www.computerweekly.com/Articles/2008/04/03/ 230128/technology-plays-crucial-role-in-vaccination-distribution.htm). Computer Weekly (2008-04-03). Retrieved on 2010-08-14.. Discusses use of handheld electronic data collection in managing public health data and activities. • "A world of witnesses" The Economist: January 2008. A survey of mobility: A world of witnesses (http://www. economist.com/surveys/displaystory.cfm?story_id=10950499). The Economist. Retrieved on 2010-08-14. Discusses use of EpiSurveyor software in public health monitoring in Africa. • "Charting the Future of Capacity Building for mHealth" TechChange: July 2013. http://techchange.org/2013/ 07/22/charting-the-future-of-capacity-building-for-mhealth/ • "Globally, deaths from measles drop sharply" The Washington Post: November 2007. Globally, Deaths From Measles Drop Sharply (http://www.washingtonpost.com/wp-dyn/content/article/2007/11/29/ AR2007112902021.html). washingtonpost.com. Retrieved on 2010-08-14. Describes role of EpiSurveyor mobile data collection software in contributing to the highly successful fight against measles mortality. • Kaplan, Warren. "Can the ubiquitous power of mobile phones be used to improve health outcomes in developing countries?" Globalization and Health 2 (2006): 9. Full text | Can the ubiquitous power of mobile phones be used to improve health outcomes in developing countries? (http://www.globalizationandhealth.com/content/2/1/ 9). Globalization and Health. Retrieved on 2010-08-14. • Olmeda, Christopher J. (2000). Information Technology in Systems of Care. Delfin Press. ISBN 978-0-9821442-0-6 • United Nations. "Compendium of ICT Applications on Electronic Government, Volume 1: Mobile Applications on Health and Learning". United Nations: 2006.http://unpan1.un.org/intradoc/groups/public/documents/UN/ UNPAN030003.pdf • Economist "The doctor in your pocket [Medical technology: Nearly everyone in the developed world carries a mobile phone- so why not use it to deliver health care?]" The Economist: 2005. MONITOR | The doctor in your pocket | Economist.com (http://www.wirelesslifesciences.org/pdfs/ MONITOR_doctorinyourpocket_Economist.pdf). (PDF) . Retrieved on 2010-08-14. • Mechael, P. "Exploring Health-related Uses of Mobile Phones: An Egyptian Case Study". Public Health & Policy (p.264). London: London School of Hygiene and Tropical Medicine 2006. (http://open.intrahealth.org/wiki/ upload/PatriciaMechaelThesisFinalDecember2006.pdf)Wikipedia:Link rot • Mechael, Patricia (2009). The Case for mHealth in Developing Countries. Mobilizing Markets: Special Edition of MIT Innovations Journal for the GSMA Mobile World Congress 2009. Cambridge: MIT Press, pages 153–168. • Mechael, P and D. Sloninsky. Towards the Development of an mHealth Strategy: A Literature Review. New York: Earth Institute at Columbia University: Working Document. • Asangansi, I., & Braa, K. (2010). The emergence of mobile-supported national health information systems in developing countries. Studies in health technology and informatics, 160(Pt 1), 540–4. Retrieved from http:// www.ncbi.nlm.nih.gov/pubmed/20841745 • Istepanian, R. "Introduction to the Special Section on M-Health: Beyond Seamless Mobility and Global Wireless Health-care Connectivity". IEEE Transactions on Information Technology in Biomedicine: 2004. 8(4), 405–413. • Istepanian, Robert et al., eds. (2006) M-Health: Emerging Mobile Health Systems. Springer Verlag. ISBN 0-387-26558-9 • UNICEF and Women's Net (2007). Rapid Assessment of Cell Phones for Development. Written and compiled by Sally-Jean Shackleton. • Vital Wave Consulting (February 2009). mHealth for Development: The Opportunity of Mobile Technology for Healthcare in the Developing World (http://www.vitalwaveconsulting.com/pdf/mHealth.pdf). United Nations Foundation, Vodafone Foundation.
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MHealth • Anta R., El-Wahab S., and Giuffrida A. [Mobile Health: The potential of mobile telephony to bring health care to the majority.]http://www.iadb.org/document.cfm?id=1861959 Inter-American Development Bank. February 2009. • Adesina Iluyemi, [Community-based health workers in developing countries and the role of m-health. In Telehealth in Developing Countries]http://www.idrc.ca/en/ev-137420-201-1-DO_TOPIC.htm • mHealthInfo.org (http://www.mhealthinfo.org) offers information on the current use, potential and limitations of mHealth in low-resource settings
Practice management software Practice management software may refer to software used for the management of a professional office: • Law practice management software • Medical practice management software There are also practice management software for accounting, architecture, veterinary, dental, optometry and other practices. __DISAMBIG__
Clinical Quality Management System A Clinical Quality Management System (CQMS) allows an entire practice staff to take part in increasing the quality of care delivered to their patients. Acting as a real-time dashboard on care, a CQMS lets a care team know the status of each of their patients regarding needed preventive, screening and chronic disease management services based on practice-specified care guidelines. With tools to reach patients at the point-of-care and those not scheduled for a visit, a CQMS ensures the entire patient population is managed effectively and efficiently.
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Health Electronic Records Electronic health record An electronic health record (EHR) is an evolving concept defined as a systematic collection of electronic health information about individual patients or populations. It is a record in digital format that is theoretically capable of being shared across different health care settings. In some cases this sharing can occur by way of network-connected, enterprise-wide information systems and other information networks or exchanges. EHRs may include a range of data, including demographics, medical history, medication and allergies, immunization status, laboratory test results, radiology images, vital signs, personal statistics like age and weight, and billing information.
Sample view of an electronic health record based on images
The system is designed to capture and re-present data that accurately capture the state of the patient at all times. It allows for an entire patient history to be viewed without the need to track down the patient’s previous medical record volume and assists in ensuring data is accurate, appropriate and legible. It reduces the chances of data replication as there is only one modifiable file, which means the file is constantly up to date when viewed at a later date and eliminates the issue of lost forms or paperwork. Due to all the information being Sample view of an electronic health record in a single file, it makes it much more effective when extracting medical data for the examination of possible trends and long term changes in the patient.
Electronic health record
Terminology The terms EHR, EPR (electronic patient record) and EMR (electronic medical record) are often used interchangeably, although differences between them can be defined. The EMR can, for example, be defined as the patient record created in hospitals and ambulatory environments, and which can serve as a data source for the EHR. It is important to note that an EHR is generated and maintained within an institution, such as a hospital, integrated delivery network, clinic, or physician office, to give patients, physicians and other health care providers, employers, and payers or insurers access to a patient's medical records across facilities. A personal health record (PHR) is, in modern parlance, generally defined as an EHR that the individual patient controls.
Comparison with paper-based records Paper-based records require a significant amount of storage space compared to digital records. In the United States, most states require physical records be held for a minimum of seven years. The costs of storage media, such as paper and film, per unit of information differ dramatically from that of electronic storage media. When paper records are stored in different locations, collating them to a single location for review by a health care provider is time consuming and complicated, whereas the process can be simplified with electronic records. This is particularly true in the case of person-centered records, which are impractical to maintain if not electronic (thus difficult to centralize or federate). When paper-based records are required in multiple locations, copying, faxing, and transporting costs are significant compared to duplication and transfer of digital records.[citation needed] Because of these many "after-entry" benefits, federal and state governments, insurance companies and other large medical institutions are heavily promoting the adoption of electronic medical records. The US Congress included a formula of both incentives (up to $44,000 per physician under Medicare or up to $65,000 over six years, under Medicaid) and penalties (i.e. decreased Medicare and Medicaid reimbursements for covered patients to doctors who fail to use EMRs by 2015) for EMR/EHR adoption versus continued use of paper records as part of the Health Information Technology for Economic and Clinical Health (HITECH) Act, enacted as part of the American Recovery and Reinvestment Act of 2009.[1] One study estimates electronic medical records improve overall efficiency by 6% per year, and the monthly cost of an EMR may (depending on the cost of the EMR) be offset by the cost of only a few "unnecessary" tests or admissions. Jerome Groopman disputed these results, publicly asking "how such dramatic claims of cost-saving and quality improvement could be true". However, the increased portability and accessibility of electronic medical records may also increase the ease with which they can be accessed and stolen by unauthorized persons or unscrupulous users versus paper medical records, as acknowledged by the increased security requirements for electronic medical records included in the Health Information and Accessibility Act and by large-scale breaches in confidential records reported by EMR users.[2][3] Concerns about security contribute to the resistance shown to their widespread adoption. Handwritten paper medical records can be associated with poor legibility, which can contribute to medical errors. Pre-printed forms, the standardization of abbreviations, and standards for penmanship were encouraged to improve reliability of paper medical records. Electronic records help with the standardization of forms, terminology and abbreviations, and data input. Digitization of forms facilitates the collection of data for epidemiology and clinical studies.[4] In contrast, EMRs can be continuously updated (within certain legal limitations – see below). The ability to exchange records between different EMR systems ("interoperability"[5]) would facilitate the co-ordination of health care delivery in non-affiliated health care facilities. In addition, data from an electronic system can be used anonymously for statistical reporting in matters such as quality improvement, resource management and public health communicable disease surveillance.
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In ambulances Ambulance services in Australia have introduced the use of EMR systems [6] The benefits of EMR in ambulances include the following: better training for paramedics, review of clinical standards, better research options for pre-hospital care and design of future treatment options [7] Automated handwriting recognition of ambulance medical forms has also been successful. These systems allow paper-based medical documents to be converted to digital text with substantially less cost overhead. Patient identifying information would not be converted to comply with government privacy regulations. The data can then be efficiently used for epidemiological analysis.
Technical features • • • •
Digital formatting enables information to be used and shared over secure networks Track care (e.g. prescriptions) and outcomes (e.g. blood pressure) Trigger warnings and reminders Send and receive orders, reports, and results
Health Information Exchange • Technical and social framework that enables information to move electronically between organizations • Reporting to public health • ePrescribing • Sharing laboratory results with providers Using an EMR to read and write a patient's record is not only possible through a workstation but, depending on the type of system and health care settings, may also be possible through mobile devices that are handwriting capable.[8] Electronic Medical Records may include access to Personal Health Records (PHR) which makes individual notes from an EMR readily visible and accessible for consumers. Some EMR systems automatically monitor clinical events, by analyzing patient data from an electronic health record to predict, detect and potentially prevent adverse events. This can include discharge/transfer orders, pharmacy orders, radiology results, laboratory results and any other data from ancillary services or provider notes.[9] This type of event monitoring has been implemented using the Louisiana Public health information exchange linking state wide public health with electronic medical records. This system alerted medical providers when a patient with HIV/AIDS had not received care in over twelve months. This system greatly reduced the number of missed critical opportunities.
Philosophical views of the EHR Within a meta-narrative systematic review of research in the field, Prof. Trish Greenhalgh and colleagues defined a number of different philosophical approaches to the EHR. The health information systems literature has seen the EHR as a container holding information about the patient, and a tool for aggregating clinical data for secondary uses (billing, audit etc.). However, other research traditions see the EHR as a contextualised artifact within a socio-technical system. For example, actor-network theory would see the EHR as an actant in a network,[10] while research in computer supported cooperative work (CSCW) sees the EHR as a tool supporting particular work. Several possible advantages to EHRs over paper records have been proposed, but there is debate about the degree to which these are achieved in practice.[11]
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Implementation, end user and patient considerations Quality Several studies call into question whether EHRs improve the quality of care.[12] However, a recent multi-provider study in diabetes care, published in the New England Journal of Medicine, found evidence that practices with EHR provided better quality care. EHR do help improve care coordination. Since anyone with that EHR can view the patients chart it cuts down on guessing histories, seeing multiple specialists, smoothing transitions between care settings, and better care in emergency situations EHRs may also improve prevention by providing doctors and patients better access to test results, identifying missing patient information, and offering evidence-based recommendations for preventive services.
Costs The steep price of EHR and provider uncertainty regarding the value they will derive from adoption in the form of return on investment has a significant influence on EHR adoption. In a project initiated by the Office of the National Coordinator for Health Information (ONC), surveyors found that hospital administrators and physicians who had adopted EHR noted that any gains in efficiency were offset by reduced productivity as the technology was implemented, as well as the need to increase information technology staff to maintain the system. The U.S. Congressional Budget Office concluded that the cost savings may occur only in large integrated institutions like Kaiser Permanente, and not in small physician offices. They challenged the Rand Corp. estimates of savings. "Office-based physicians in particular may see no benefit if they purchase such a product—and may even suffer financial harm. Even though the use of health IT could generate cost savings for the health system at large that might offset the EHR's cost, many physicians might not be able to reduce their office expenses or increase their revenue sufficiently to pay for it. For example. the use of health IT could reduce the number of duplicated diagnostic tests. However, that improvement in efficiency would be unlikely to increase the income of many physicians."[13] One CEO of an EHR company has argued if a physician performs tests in the office, it might reduce his or her income. Doubts have been raised about cost saving from EHRs by researchers at Harvard University, the Wharton School of the University of Pennsylvania, Stanford University, and others.[14][15]
Software quality and usability deficiencies The Healthcare Information and Management Systems Society (HIMSS), a very large U.S. healthcare IT industry trade group, observed that EHR adoption rates "have been slower than expected in the United States, especially in comparison to other industry sectors and other developed countries. A key reason, aside from initial costs and lost productivity during EMR implementation, is lack of efficiency and usability of EMRs currently available."[16] The U.S. National Institute of Standards and Technology of the Department of Commerce studied usability in 2011 and lists a number of specific issues that have been reported by health care workers.[17] The U.S. military's EHR, AHLTA, was reported to have significant usability issues.[18] However, physicians are embracing mobile technologies such as smartphones and tablets at a rapid pace. According to a 2012 survey by Physicians Practice, 62.6 percent of respondents (1,369 physicians, practice managers, and other healthcare providers) say they use mobile devices in the performance of their job. Mobile devices are increasingly able to synch up with electronic health record systems thus allowing physicians to access patient records from remote locations. Most devices are extensions of desk-top EHR systems, using a variety of software to communicate and access files remotely. The advantages of instant access to patient records at any time and any place are clear, but bring a host of security concerns. As mobile systems become more prevalent, practices will need comprehensive policies that govern security measures and patient privacy regulations.[19]
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Unintended consequences Per empirical research in social informatics, information and communications technology (ICT) use can lead to both intended and unintended consequences.[20][21][22] A 2008 Sentinel Event Alert from the U.S. Joint Commission, the organization that accredits American hospitals to provide healthcare services, states that "As health information technology (HIT) and 'converging technologies'—the interrelationship between medical devices and HIT—are increasingly adopted by health care organizations, users must be mindful of the safety risks and preventable adverse events that these implementations can create or perpetuate. Technology-related adverse events can be associated with all components of a comprehensive technology system and may involve errors of either commission or omission. These unintended adverse events typically stem from human-machine interfaces or organization/system design."[23] The Joint Commission cites as an example the United States Pharmacopeia MEDMARX database[24] where of 176,409 medication error records for 2006, approximately 25 percent (43,372) involved some aspect of computer technology as at least one cause of the error. The National Health Service (NHS) in the UK reports specific examples of potential and actual EHR-caused unintended consequences in their 2009 document on the management of clinical risk relating to the deployment and use of health software.[25] In a Feb. 2010 U.S. Food and Drug Administration (FDA) memorandum, FDA notes EHR unintended consequences include EHR-related medical errors due to (1) errors of commission (EOC), (2) errors of omission or transmission (EOT), (3) errors in data analysis (EDA), and (4) incompatibility between multi-vendor software applications or systems (ISMA) and cites examples. In the memo FDA also notes the "absence of mandatory reporting enforcement of H-IT safety issues limits the numbers of medical device reports (MDRs) and impedes a more comprehensive understanding of the actual problems and implications."[26] A 2010 Board Position Paper by the American Medical Informatics Association (AMIA) contains recommendations on EHR-related patient safety, transparency, ethics education for purchasers and users, adoption of best practices, and re-examination of regulation of electronic health applications.[27] Beyond concrete issues such as conflicts of interest and privacy concerns, questions have been raised about the ways in which the physician-patient relationship would be affected by an electronic intermediary.[28]
Privacy and confidentiality In the United States in 2011 there were 380 major data breaches involving 500 or more patients' records listed on the website kept by the United States Department of Health and Human Services (HHS) Office for Civil Rights. So far, from the first wall postings in September 2009 through the latest on December 8, 2012, there have been 18,059,831 "individuals affected," and even that massive number is an undercount of the breach problem. The civil rights office has not released the records of tens of thousands of breaches it has received under a federal reporting mandate on breaches affecting fewer than 500 patients per incident.
Governance, privacy and legal issues Privacy concerns In the United States, Great Britain, and Germany, the concept of a national centralized server model of healthcare data has been poorly received. Issues of privacy and security in such a model have been of concern. Privacy concerns in healthcare apply to both paper and electronic records. According to the Los Angeles Times, roughly 150 people (from doctors and nurses to technicians and billing clerks) have access to at least part of a patient's records during a hospitalization, and 600,000 payers, providers and other entities that handle providers' billing data have some access also. Recent revelations of "secure" data breaches at centralized data repositories, in
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Electronic health record banking and other financial institutions, in the retail industry, and from government databases, have caused concern about storing electronic medical records in a central location. Records that are exchanged over the Internet are subject to the same security concerns as any other type of data transaction over the Internet. The Health Insurance Portability and Accountability Act (HIPAA) was passed in the US in 1996 to establish rules for access, authentications, storage and auditing, and transmittal of electronic medical records. This standard made restrictions for electronic records more stringent than those for paper records. However, there are concerns as to the adequacy of these standards. In the United States, information in electronic medical records is referred to as Protected Health Information (PHI) and its management is addressed under the Health Insurance Portability and Accountability Act (HIPAA) as well as many local laws.[29] The HIPAA protects a patient's information; the information that is protected under this act are: information doctors and nurses input into the electronic medical record, conversations between a doctor and a patient that may have been recorded, as well as billing information. Under this act there is a limit as to how much information can be disclosed, and as well as who can see a patients information. Patients also get to have a copy of their records if they desire, and get a notified if their information is ever to be shared with third parties. Covered entities may disclose protected health information to law enforcement officials for law enforcement purposes as required by law (including court orders, court-ordered warrants, subpoenas) and administrative requests; or to identify or locate a suspect, fugitive, material witness, or missing person.[30] Medical and health care providers experienced 767 security breaches resulting in the compromised confidential health information of 23,625,933 patients during the period of 2006–2012.[31] In the European Union (EU), several directives of the European Parliament and of the Council protect the processing and free movement of personal data, including for purposes of health care.[32] Threats to health care information can be categorized under three headings: • Human threats, such as employees or hackers • Natural and environmental threats, such as earthquakes, hurricanes and fires. • Technology failures, such as a system crashing These threats can either be internal, external, intentional and unintentional. Therefore, one will find health information systems professionals having these particular threats in mind when discussing ways to protect the health information of patients. The Health Insurance Portability and Accountability Act (HIPAA) has developed a framework to mitigate the harm of these threats that is comprehensive but not so specific as to limit the options of healthcare professionals who may have access to different technology. In the European Union (EU), several Directives of the European Parliament and of the Council protect the processing and free movement of personal data, including for purposes of health care. Personal Information Protection and Electronic Documents Act (PIPEDA) was given Royal Assent in Canada on April 13, 2000 to establish rules on the use, disclosure and collection of personal information. The personal information includes both non-digital and electronic form. In 2002, PIPEDA extended to the health sector in Stage 2 of the law's implementation. There are four provinces where this law does not apply because its privacy law was considered similar to PIPEDA: Alberta, British Columbia, Ontario and Quebec. One major issue that has risen on the privacy of the US network for electronic health records is the strategy to secure the privacy of patients. Former US president Bush called for the creation of networks, but federal investigators report that there is no clear strategy to protect the privacy of patients as the promotions of the electronic medical records expands throughout the United States. In 2007, the Government Accountability Office reports that there is a "jumble of studies and vague policy statements but no overall strategy to ensure that privacy protections would be built into computer networks linking insurers, doctors, hospitals and other health care providers." The privacy threat posed by the interoperability of a national network is a key concern. One of the most vocal critics of EMRs, New York University Professor Jacob M. Appel, has claimed that the number of people who will need to
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Electronic health record have access to such a truly interoperable national system, which he estimates to be 12 million, will inevitable lead to breaches of privacy on a massive scale. Appel has written that while "hospitals keep careful tabs on who accesses the charts of VIP patients," they are powerless to act against "a meddlesome pharmacist in Alaska" who "looks up the urine toxicology on his daughter's fiance in Florida, to check if the fellow has a cocaine habit." This is a significant barrier for the adoption of an EHR. Accountability among all the parties that are involved in the processing of electronic transactions including the patient, physician office staff, and insurance companies, is the key to successful advancement of the EHR in the US Supporters of EHRs have argued that there needs to be a fundamental shift in "attitudes, awareness, habits, and capabilities in the areas of privacy and security" of individual's health records if adoption of an EHR is to occur. According to the Wall Street Journal, the DHHS takes no action on complaints under HIPAA, and medical records are disclosed under court orders in legal actions such as claims arising from automobile accidents. HIPAA has special restrictions on psychotherapy records, but psychotherapy records can also be disclosed without the client's knowledge or permission, according to the Journal. For example, Patricia Galvin, a lawyer in San Francisco, saw a psychologist at Stanford Hospital & Clinics after her fiance committed suicide. Her therapist had assured her that her records would be confidential. But after she applied for disability benefits, Stanford gave the insurer her therapy notes, and the insurer denied her benefits based on what Galvin claims was a misinterpretation of the notes. Within the private sector, many companies are moving forward in the development, establishment and implementation of medical record banks and health information exchange. By law, companies are required to follow all HIPAA standards and adopt the same information-handling practices that have been in effect for the federal government for years. This includes two ideas, standardized formatting of data electronically exchanged and federalization of security and privacy practices among the private sector. Private companies have promised to have "stringent privacy policies and procedures." If protection and security are not part of the systems developed, people will not trust the technology nor will they participate in it.
Legal issues Liability Legal liability in all aspects of healthcare was an increasing problem in the 1990s and 2000s. The surge in the per capita number of attorneys and changes in the tort system caused an increase in the cost of every aspect of healthcare, and healthcare technology was no exception. Failure or damages caused during installation or utilization of an EHR system has been feared as a threat in lawsuits. Similarly, it's important to recognize that the implementation of electronic health records carries with it significant legal risks. This liability concern was of special concern for small EHR system makers. Some smaller companies may be forced to abandon markets based on the regional liability climate.[33]Wikipedia:Identifying reliable sources Larger EHR providers (or government-sponsored providers of EHRs) are better able to withstand legal assaults. While there is no argument that electronic documentation of patient visits and data brings improved patient care, there is increasing concern that such documentation could open physicians to an increased incidence of malpractice suits. Disabling physician alerts, selecting from dropdown menus, and the use of templates can encourage physicians to skip a complete review of past patient history and medications, and thus miss important data. Another potential problem is electronic time stamps. Many physicians are unaware that EHR systems produce an electronic time stamp every time the patient record is updated. If a malpractice claim goes to court, through the process of discovery, the prosecution can request a detailed record of all entries made in a patient's electronic record. Waiting to chart patient notes until the end of the day and making addendums to records well after the patient visit can be problematic, in that this practice could result in less than accurate patient data or indicate possible intent to illegally alter the patient's record.[34]
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Electronic health record In some communities, hospitals attempt to standardize EHR systems by providing discounted versions of the hospital's software to local healthcare providers. A challenge to this practice has been raised as being a violation of Stark rules that prohibit hospitals from preferentially assisting community healthcare providers. In 2006, however, exceptions to the Stark rule were enacted to allow hospitals to furnish software and training to community providers, mostly removing this legal obstacle.Wikipedia:Identifying reliable sourcesWikipedia:Identifying reliable sources Legal interoperability In cross-border use cases of EHR implementations, the additional issue of legal interoperability arises. Different countries may have diverging legal requirements for the content or usage of electronic health records, which can require radical changes of the technical makeup of the EHR implementation in question. (especially when fundamental legal incompatibilities are involved) Exploring these issues is therefore often necessary when implementing cross-border EHR solutions.[35]
Regulatory compliance • Consumer Credit Act 2006 • HIPAA • Health Level 7
Contribution under UN administration and accredited organizations The United Nations World Health Organization (WHO) administration intentionally does not contribute to an internationally standardized view of medical records nor to personal health records. However, WHO contributes to minimum requirements definition for developing countries. The United Nations accredited standardisation body International Organization for Standardization (ISO) however has settled thorough wordWikipedia:Please clarify for standards in the scope of the HL7 platform for health care informatics. Respective standards are available with ISO/HL7 10781:2009 Electronic Health Record-System Functional Model, Release 1.1 and subsequent set of detailing standards.
Medical Data Breach The Security Rule, according to Health and Human Services (HHS), establishes a security framework for small practices as well as large institutions. All covered entities must have a written security plan. The HHS identifies three components as necessary for the security plan: administrative safeguards, physical safeguards, and technical safeguards. However, medical and healthcare providers have experienced 767 security breaches resulting in the compromised confidential health information of 23,625,933 patients during the period of 2006-2012.[36] The majority of the counties in Europe have made a strategy for the development and implementation of the Electronic Health Record Systems. This would mean greater access to health records by numerous stakeholders, even from countries with lower levels of privacy protection. The forthcoming implementation of the Cross Border Health Directive and the EU Commission's plans to centralize all health records are of prime concern to the EU public who believe that the health care organizations and governments cannot be trusted to manage their data electronically and expose them to more threats. The idea of a centralized electronic health record system has been poorly received by the public who are wary that the governments may extend the use of the system beyond its purpose. There is also the risk for privacy breaches that could allow sensitive health care information to fall into the wrong hands. Some countries have enacted laws requiring safeguards to be put in place to protect the security and confidentiality of medical information as it is shared electronically and to give patients some important rights to monitor their medical records and receive
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Electronic health record notification for loss and unauthorized acquisition of health information. The United States and the EU have imposed mandatory medical data breach notifications.[37] The Health Insurance Portability and Accessibility Act (HIPAA) requires safeguards to limit the number of people who have access to personal information. However, given the number of people who may have access to your information as part of the operations and business of the health care provider or plan, there is no realistic way to estimate the number of people who may come across your records.[38] Additionally, law enforcement access is authorized under HIPAA. In some cases, medical information may be disclosed without a warrant or court order.
Breach notification The purpose of a personal data breach notification is to protect individuals so that they can take all the necessary actions to limit the undesirable effects of the breach and to motivate the organization to improve the security of the infrastructure to protect the confidentiality of the data. The US law requires the entities to inform the individuals in the event of breach while the EU Directive currently requires breach notification only when the breach is likely to adversely affect the privacy of the individual. Personal health data is valuable to individuals and is therefore difficult to make an assessment whether the breach will cause reputational or financial harm or cause adverse effects on one's privacy. The Security Rule that was adopted in 2005 did not require breach notification. However, notice might be required by state laws that apply to a variety of industries, including health care providers. In California, a law has been in place since 2003 requiring that a HIPAA covered organization's breach could have triggered a notice even though notice was not required by the HIPAA Security Rule. Since January 1, 2009, California residents are required to receive notice of a health information breach. Federal law and regulations now provide rights to notice of a breach of health information. The Health Information Technology for Economic and Clinical Health (HITECH) Act requires HHS and the Federal Trade Commission (FTC) to jointly study and report on privacy and data security of personal health information. HITECH also requires the agencies to issue breach notification rules that apply to HIPAA covered entities and Web-based vendors that store health information electronically. The FTC has adopted rules regarding breach notification for internet-based vendors.[39] The Breach notification law in the EU provides better privacy safeguards with fewer exemptions, unlike the US law which exempts unintentional acquisition, access, or use of protected health information and inadvertent disclosure under a good faith belief.
Technical issues Standards • ANSI X12 (EDI) - transaction protocols used for transmitting patient data. Popular in the United States for transmission of billing data. • CEN's TC/251 provides EHR standards in Europe including: • EN 13606, communication standards for EHR information • CONTSYS (EN 13940), supports continuity of care record standardization. • HISA (EN 12967), a services standard for inter-system communication in a clinical information environment. • Continuity of Care Record - ASTM International Continuity of Care Record standard • DICOM - an international communications protocol standard for representing and transmitting radiology (and other) image-based data, sponsored by NEMA (National Electrical Manufacturers Association)
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Electronic health record • HL7 - a standardized messaging and text communications protocol between hospital and physician record systems, and between practice management systems • ISO - ISO TC 215 provides international technical specifications for EHRs. ISO 18308 describes EHR architectures The U.S. federal government has issued new rules of electronic health records. Open Specifications • openEHR: an open community developed specification for a shared health record with web-based content developed online by experts. Strong multilingual capability. • Virtual Medical Record: HL7's proposed model for interfacing with clinical decision support systems. • SMART (Subsitutable Medical Apps, reusable technologies): an open platform specification to provide a standard base for healthcare applications. Customization Each healthcare environment functions differently, often in significant ways. It is difficult to create a "one-size-fits-all" EHR system. An ideal EHR system will have record standardization but interfaces that can be customized to each provider environment. Modularity in an EHR system facilitates this. Many EHR companies employ vendors to provide customization. This customization can often be done so that a physician's input interface closely mimics previously utilized paper forms.[40] At the same time they reported negative effects in communication, increased overtime, and missing records when a non-customized EMR system was utilized.[41] Customizing the software when it is released yields the highest benefits because it is adapted for the users and tailored to workflows specific to the institution.[42] Customization can have its disadvantages. There is, of course, higher costs involved to implementation of a customized system initially. More time must be spent by both the implementation team and the healthcare provider to understand the workflow needs. Development and maintenance of these interfaces and customizations can also lead to higher software implementation and maintenance costs.[43]Wikipedia:Identifying reliable sources[44]Wikipedia:Identifying reliable sources
Long-term preservation and storage of records An important consideration in the process of developing electronic health records is to plan for the long-term preservation and storage of these records. The field will need to come to consensus on the length of time to store EHRs, methods to ensure the future accessibility and compatibility of archived data with yet-to-be developed retrieval systems, and how to ensure the physical and virtual security of the archives[citation needed]. Additionally, considerations about long-term storage of electronic health records are complicated by the possibility that the records might one day be used longitudinally and integrated across sites of care. Records have the potential to be created, used, edited, and viewed by multiple independent entities. These entities include, but are not limited to, primary care physicians, hospitals, insurance companies, and patients. Mandl et al. have noted that "choices about the structure and ownership of these records will have profound impact on the accessibility and privacy of patient information." The required length of storage of an individual electronic health record will depend on national and state regulations, which are subject to change over time. Ruotsalainen and Manning have found that the typical preservation time of patient data varies between 20 and 100 years. In one example of how an EHR archive might function, their research
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Electronic health record "describes a co-operative trusted notary archive (TNA) which receives health data from different EHR-systems, stores data together with associated meta-information for long periods and distributes EHR-data objects. TNA can store objects in XML-format and prove the integrity of stored data with the help of event records, timestamps and archive e-signatures." In addition to the TNA archive described by Ruotsalainen and Manning, other combinations of EHR systems and archive systems are possible. Again, overall requirements for the design and security of the system and its archive will vary and must function under ethical and legal principles specific to the time and place[citation needed]. While it is currently unknown precisely how long EHRs will be preserved, it is certain that length of time will exceed the average shelf-life of paper records. The evolution of technology is such that the programs and systems used to input information will likely not be available to a user who desires to examine archived data. One proposed solution to the challenge of long-term accessibility and usability of data by future systems is to standardize information fields in a time-invariant way, such as with XML language. Olhede and Peterson report that "the basic XML-format has undergone preliminary testing in Europe by a Spri project and been found suitable for EU purposes. Spri has advised the Swedish National Board of Health and Welfare and the Swedish National Archive to issue directives concerning the use of XML as the archive-format for EHCR (Electronic Health Care Record) information."
Synchronization of records When care is provided at two different facilities, it may be difficult to update records at both locations in a co-ordinated fashion. Two models have been used to satisfy this problem: a centralized data server solution, and a peer-to-peer file synchronization program (as has been developed for other peer-to-peer networks). Synchronization programs for distributed storage models, however, are only useful once record standardization has occurred. Merging of already existing public healthcare databases is a common software challenge. The ability of electronic health record systems to provide this function is a key benefit and can improve healthcare delivery.[45]
eHealth and teleradiology The sharing of patient information between health care organizations and IT systems is changing from a "point to point" model to a "many to many" one. The European Commission is supporting moves to facilitate cross-border interoperability of e-health systems and to remove potential legal hurdles, as in the project www.epsos.eu/. To allow for global shared workflow, studies will be locked when they are being read and then unlocked and updated once reading is complete. Radiologists will be able to serve multiple health care facilities and read and report across large geographical areas, thus balancing workloads. The biggest challenges will relate to interoperability and legal clarity. In some countries it is almost forbidden to practice teleradiology. The variety of languages spoken is a problem and multilingual reporting templates for all anatomical regions are not yet available. However, the market for e-health and teleradiology is evolving more rapidly than any laws or regulations.[46]
European Union: Directive 2011/24/EU on patients' rights in cross-border healthcare The European Commission wants to boost the digital economy by enabling all Europeans to have access to online medical records anywhere in Europe by 2020. With the newly enacted Directive 2011/24/EU on patients' rights in cross-border healthcare due for implementation by 2013, it is inevitable that a centralised European health record system will become a reality even before 2020. However, the concept of a centralised supranational central server raises concern about storing electronic medical records in a central location. The privacy threat posed by a
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Electronic health record supranational network is a key concern. Cross-border and Interoperable electronic health record systems make confidential data more easily and rapidly accessible to a wider audience and increase the risk that personal data concerning health could be accidentally exposed or easily distributed to unauthorised parties by enabling greater access to a compilation of the personal data concerning health, from different sources, and throughout a lifetime.[47]
National contexts United States
EHR adoption of all physicians in the US. Source: DesRoches et al. (2008). Fully functional EHR system (4%) Basic EHR system (13%) Bought but not implemented yet (13%) EHR purchase planned in 2 years (22%) No EHR system (48%) Usage Even though EMR systems with a computerized provider order entry (CPOE) have existed for more than 30 years, fewer than 10 percent of hospitals as of 2006 had a fully integrated system.[48] In a 2008 survey by DesRoches et al. of 4484 physicians (62% response rate), 83% of all physicians, 80% of primary care physicians, and 86% of non-primary care physicians had no EHRs. "Among the 83% of respondents who did not have electronic health records, 16%" had bought, but not implemented an EHR system yet. The 2009 National Ambulatory Medical Care Survey of 5200 physicians (70% response rate) by the National Center for Health Statistics showed that 51.7% of office-based physicians did not use any EMR/EHR system. In the United States, the CDC reported that the EMR adoption rate had steadily risen to 48.3 percent at the end of 2009.[49] This is an increase over 2008, when only 38.4% of office-based physicians reported using fully or partially electronic medical record systems (EMR) in 2008.[50] However, the same study found that only 20.4% of all physicians reported using a system described as minimally functional and including the following features: orders for prescriptions, orders for tests, viewing laboratory or imaging results, and clinical progress notes. As of 2012, 72 percent of office physicians are using basic electronic medical records. The healthcare industry spends only 2% of gross revenues on HIT, which is low compared to other information intensive industries such as finance, which spend upwards of 10%. The usage of electronic medical records can vary depending on who the user is and how they are using it. Electronic medical records can help improve the quality of medical care given to patients. Many doctors and office-based physicians refuse to get rid of the traditional paper records. Harvard University has conducted an experiment in which they tested how doctors and nurses use electronic medical records to keep their patients' information up to
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Electronic health record date. The studies found that electronic medical records were very useful; a doctor or a nurse was able to find a patient's information fast and easy just by typing their name; even if it was misspelled. The usage of electronic medical records increases in some work places due to the ease of use of the system; whereas the president of the Canadian Family Practice Nurses Association says that using electronic medical records can be time consuming, and it isn't very helpful due to the complexity of the system. Beth Israel Deaconess Medical Center reported that doctors and nurses prefer to use a much more friendly user software due to the difficulty and time it takes for a medical staff to input the information as well as to find a patients information. A study was done and the amount of information that was recorded in the EMRs was recorded; about 44% of the patients information was recorded in the EMRs. This shows that EMRs are not very efficient most of the time. The cost of implementing an EMR system for smaller practices has also been criticized.Wikipedia:Manual of Style/Words to watch#Unsupported attributions Despite this, tighter regulations regarding meaningful use criteria have resulted in more physicians adopting EMR systems. Software, hardware and other services for EMR system implementation are provided for cost by various companies, including Dell. Open source EMR systems exist, but have not seen widespread adoption of open-source EMR system software. Beyond financial concerns there are a number of legal and ethical dilemmas created by increasing EMR use. Legal status Electronic medical records, like other medical records, must be kept in unaltered form and authenticated by the creator.[51] Under data protection legislation, responsibility for patient records (irrespective of the form they are kept in) is always on the creator and custodian of the record, usually a health care practice or facility. The physical medical records are the property of the medical provider (or facility) that prepares them. This includes films and tracings from diagnostic imaging procedures such as X-ray, CT, PET, MRI, ultrasound, etc. The patient, however, according to HIPAA, has a right to view the originals, and to obtain copies under law.[52] The Health Information Technology for Economic and Clinical Health Act (Pub.L. 111–5 [53],§2.A.III & B.4) (a part of the 2009 stimulus package) set meaningful use of interoperable EHR adoption in the health care system as a critical national goal and incentivized EHR adoption. The "goal is not adoption alone but 'meaningful use' of EHRs — that is, their use by providers to achieve significant improvements in care." Title IV of the act promises maximum incentive payments for Medicaid to those who adopt and use "certified EHRs" of $63,750 over 6 years beginning in 2011. Eligible professionals must begin receiving payments by 2016 to qualify for the program. For Medicare the maximum payments are $44,000 over 5 years. Doctors who do not adopt an EHR by 2015 will be penalized 1% of Medicare payments, increasing to 3% over 3 years. In order to receive the EHR stimulus money, the HITECH Act requires doctors to show "meaningful use" of an EHR system. As of June 2010, there are no penalty provisions for Medicaid. Health information exchange (HIE) has emerged as a core capability for hospitals and physicians to achieve "meaningful use" and receive stimulus funding. Healthcare vendors are pushing HIE as a way to allow EHR systems to pull disparate data and function on a more interoperable level[citation needed]. Starting in 2015, hospitals and doctors will be subject to financial penalties under Medicare if they are not using electronic health records.
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Electronic health record Goals And Objectives • Improve care quality, safety, efficiency, and reduce health disparities Quality and safety measurement Clinical decision support (automated advice) for providers Patient registries (e.g., “a directory of patients with diabetes”) • Improve care coordination • Engage patients and families in their care • Improve population and public health Electronic laboratory reporting for reportable conditions (hospitals) Immunization reporting to immunization registries Syndromic surveillance (health event awareness) • Ensure adequate privacy and security protections Quality Studies call into question whether, in real life, EMRs improve the quality of care. 2009 produced several articles raising doubts about EMR benefits. A major concern is the reduction of physician-patient interaction due to formatting constraints. For example, some doctors have reported that the use of check-boxes has led to fewer open-ended questions.[54] Meaningful use The main components of Meaningful Use are: • The use of a certified EHR in a meaningful manner, such as e-prescribing. • The use of certified EHR technology for electronic exchange of health information to improve quality of health care. • The use of certified EHR technology to submit clinical quality and other measures. In other words, providers need to show they're using certified EHR technology in ways that can be measured significantly in quality and in quantity. The meaningful use of EHRs intended by the US government incentives is categorized as follows: • • • • •
Improve care coordination Reduce healthcare disparities Engage patients and their families Improve population and public health Ensure adequate privacy and security
The Obama Administration's Health IT program intends to use federal investments to stimulate the market of electronic health records: • Incentives: to providers who use IT • Strict and open standards: To ensure users and sellers of EHRs work towards the same goal • Certification of software: To provide assurance that the EHRs meet basic quality, safety, and efficiency standards The detailed definition of "meaningful use" is to be rolled out in 3 stages over a period of time until 2015. Details of each stage are hotly debated by various groups. Only stage 1 has been defined while the remaining stages will evolve over time. Meaningful use Stage 1
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Electronic health record The first steps in achieving meaningful use are to have a certified electronic health record (EHR) and to be able to demonstrate that it is being used to meet the requirements. Stage 1 contains 25 objectives/measures for Eligible Providers (EPs) and 24 objectives/measures for eligible hospitals. The objectives/measures have been divided into a core set and menu set. EPs and eligible hospitals must meet all objectives/measures in the core set (15 for EPs and 14 for eligible hospitals). EPs must meet 5 of the 10 menu-set items during Stage 1, one of which must be a public health objective. Full list of the Core Requirements and a full list of the Menu Requirements. Core Requirements: 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14. 15.
Use computerized order entry for medication orders. Implement drug-drug, drug-allergy checks. Generate and transmit permissible prescriptions electronically. Record demographics. Maintain an up-to-date problem list of current and active diagnoses. Maintain active medication list. Maintain active medication allergy list. Record and chart changes in vital signs. Record smoking status for patients 13 years old or older. Implement one clinical decision support rule. Report ambulatory quality measures to CMS or the States. Provide patients with an electronic copy of their health information upon request. Provide clinical summaries to patients for each office visit. Capability to exchange key clinical information electronically among providers and patient authorized entities. Protect electronic health information (privacy & security)
Menu Requirements: 1. Implement drug-formulary checks. 2. Incorporate clinical lab-test results into certified EHR as structured data. 3. Generate lists of patients by specific conditions to use for quality improvement, reduction of disparities, research, and outreach. 4. Send reminders to patients per patient preference for preventive/ follow-up care 5. Provide patients with timely electronic access to their health information (including lab results, problem list, medication lists, allergies) 6. Use certified EHR to identify patient-specific education resources and provide to patient if appropriate. 7. Perform medication reconciliation as relevant 8. Provide summary care record for transitions in care or referrals. 9. Capability to submit electronic data to immunization registries and actual submission. 10. Capability to provide electronic syndromic surveillance data to public health agencies and actual transmission. To receive federal incentive money, CMS requires participants in the Medicare EHR Incentive Program to "attest" that during a 90-day reporting period, they used a certified EHR and met Stage 1 criteria for meaningful use objectives and clinical quality measures. For the Medicaid EHR Incentive Program, providers follow a similar process using their state's attestation system.[55] Meaningful use Stage 2 The government released its final ruling on achieving Stage 2 of meaningful use in August 2012. Eligible providers will need to meet 17 of 20 core objectives in Stage 2, and fulfill three out of six menu objectives. The required percentage of patient encounters that meet each objective has generally increased over the Stage 1 objectives.
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Electronic health record While Stage 2 focuses more on information exchange and patient engagement, many large EHR systems have this type of functionality built into their software, making it easier to achieve compliance. Also, for those eligible providers who have successfully attested to Stage 1, meeting Stage 2 should not be as difficult, as it builds incrementally on the requirements for the first stage.[56][57] Barriers to adoption Costs The steepWikipedia:Please clarify price of EMR and provider uncertainty regarding the value they will derive from adoption in the form of return on investment have a significant influence on EMR adoption.[] In a project initiated by the Office of the National Coordinator for Health Information (ONC), surveyors found that hospital administrators and physicians who had adopted EMR noted that any gains in efficiency were offset by reduced productivity as the technology was implemented, as well as the need to increase information technology staff to maintain the system. The U.S. Congressional Budget Office concluded that the cost savings may occur only in large integrated institutions like Kaiser Permanente, and not in small physician offices. They challenged the Rand Corp. estimates of savings. "Office-based physicians in particular may see no benefit if they purchase such a product—and may even suffer financial harm. Even though the use of health IT could generate cost savings for the health system at large that might offset the EMR's cost, many physicians might not be able to reduce their office expenses or increase their revenue sufficiently to pay for it. For example. the use of health IT could reduce the number of duplicated diagnostic tests. However, that improvement in efficiency would be unlikely to increase the income of many physicians."[13] "Given the ease at which information can be exchanged between health IT systems, patients whose physicians use them may feel that their privacy is more at risk than if paper records were used." Doubts have been raised about cost saving from EMRs by researchers at Harvard University, the Wharton School of the University of Pennsylvania, Stanford University, and others. Start-up costs In a survey by DesRoches et al. (2008), 66% of physicians without EHRs cited capital costs as a barrier to adoption, while 50% were uncertain about the investment. Around 56% of physicians without EHRs stated that financial incentives to purchase and/or use EHRs would facilitate adoption. In 2002, initial costs were estimated to be $50,000–70,000 per physician in a 3-physician practice. Since then, costs have decreased with increasing adoption. A 2011 survey estimated a cost of $32,000 per physician in a 5-physician practice during the first 60 days of implementation.[58] One case study by Miller et al. (2005) of 14 small primary-care practices found that the average practice paid for the initial and ongoing costs within 2.5 years. A 2003 cost-benefit analysis found that using EMRs for 5 years created a net benefit of $86,000 per provider. Some physicians are skeptical of the positive claims and believe the data is skewed by vendors and others with an interest in EHR implementation.[citation needed] Brigham and Women's Hospital in Boston, Massachusetts, estimated it achieved net savings of $5 million to $10 million per year following installation of a computerized physician order entry system that reduced serious medication errors by 55 percent. Another large hospital generated about $8.6 million in annual savings by replacing paper medical charts with EHRs for outpatients and about $2.8 million annually by establishing electronic access to laboratory results and reports.
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Electronic health record Maintenance costs Maintenance costs can be high. Miller et al. found the average estimated maintenance cost was $8500 per FTE health-care provider per year. Furthermore, software technology advances at a rapid pace. Most software systems require frequent updates, often at a significant ongoing cost. Some types of software and operating systems require full-scale re-implementation periodically, which disrupts not only the budget but also workflow. Costs for upgrades and associated regression testing can be particularly high where the applications are governed by FDA regulations (e.g. Clinical Laboratory systems). Physicians desire modular upgrades and ability to continually customize, without large-scale reimplementation[citation needed]. Training costs Training of employees to use an EHR system is costly, just as for training in the use of any other hospital system. New employees, permanent or temporary, will also require training as they are hired.[59] In the United States, a substantial majority of healthcare providers train at a VA facility sometime during their career. With the widespread adoption of the Veterans Health Information Systems and Technology Architecture (VistA) electronic health record system at all VA facilities, few recently-trained medical professionals will be inexperienced in electronic health record systems. Older practitioners who are less experienced in the use of electronic health record systems will retire over time.[citation needed] Software quality and usability deficiencies The Healthcare Information and Management Systems Society (HIMSS), a very large U.S. health care IT industry trade group, observed that EMR adoption rates "have been slower than expected in the United States, especially in comparison to other industry sectors and other developed countries. A key reason, aside from initial costs and lost productivity during EMR implementation, is lack of efficiency and usability of EMRs currently available." The U.S. National Institute of Standards and Technology of the Department of Commerce studied usability in 2011 and lists a number of specific issues that have been reported by health care workers. The U.S. military's EMR "AHLTA" was reported to have significant usability issues. Lack of semantic interoperability In the United States, there are no standards for semantic interoperability of health care data; there are only syntactic standards. This means that while data may be packaged in a standard format (using the pipe notation of HL7, or the bracket notation of XML), it lacks definition, or linkage to a common shared dictionary. The addition of layers of complex information models (such as the HL7 v3 RIM) does not resolve this fundamental issue. Implementations In the United States, the Department of Veterans Affairs (VA) has the largest enterprise-wide health information system that includes an electronic medical record, known as the Veterans Health Information Systems and Technology Architecture (VistA). A key component in VistA is their VistA imaging System which provides a comprehensive multimedia data from many specialties, including cardiology, radiology and orthopedics. A graphical user interface known as the Computerized Patient Record System (CPRS) allows health care providers to review and update a patient's electronic medical record at any of the VA's over 1,000 healthcare facilities. CPRS includes the ability to place orders, including medications, special procedures, X-rays, patient care nursing orders, diets, and laboratory tests.[citation needed] The 2003 National Defense Authorization Act (NDAA) ensured that the VA and DoD would work together to establish a bidirectional exchange of reference quality medical images. Initially, demonstrations were only worked in El Paso, Texas, but capabilities have been expanded to six different locations of VA and DoD facilities. These
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facilities include VA polytrauma centers in Tampa and Richmond, Denver, North Chicago, Biloxi, and the National Capitol Area medical facilities. Radiological images such as CT scans, MRIs, and x-rays are being shared using the BHIE. Goals of the VA and DoD in the near future are to use several image sharing solutions (VistA Imaging and DoD Picture Archiving & Communications System (PACS) solutions).[60] Clinical Data Repository/Health Data Repository (CDHR)is a database that allows for sharing of patient records, especially allergy and pharmaceutical information, between the Department of Veteran Affairs (VA) and the Department of Defense (DoD) in the United States. The program shares data by translating the various vocabularies of the information being transmitted, allowing all of the VA facilities to access and interpret the patient records.[61] The Laboratory Data Sharing and Interoperability (LDSI) application is a new program being implemented to allow sharing at certain sites between the VA and DoD of "chemistry and hematology laboratory tests." Unlike the CHDR, the LDSI is currently limited in its scope.[62]
Electronic health records flow chart
One attribute for the start of implementing EHRs in the States is the development of the Nationwide Health Information Network which is a work in progress and still being developed. This started with the North Carolina Healthcare Information and Communication Alliance founded in 1994 and who received funding from Department of Health and Human Services. The Department of Veterans Affairs and Kaiser Permanente has a pilot program to share health records between their systems VistA and HealthConnect, respectively. This software called 'CONNECT' uses Nationwide Health Information Network standards and governance to make sure that health information exchanges are compatible with other exchanges being set up throughout the country. CONNECT is an open source software solution that supports electronic health information exchange.[63] The CONNECT initiative is a Federal Health Architecture project that was conceived in 2007 and initially built by 20 various federal agencies and now comprises more than 500 organizations including federal agencies, states, healthcare providers, insurers, and health IT vendors.[64] The US Indian Health Service uses an EHR similar to Vista called RPMS. VistA Imaging is also being used to integrate images and co-ordinate PACS into the EHR system. In Alaska, use of the EHR by the Kodiak Area Native Association has improved screening services and helped the organization reach all 21 clinical performance measures defined by the Indian Health Service as required by the Government Performance and Results Act.
UK In 2005 the National Health Service (NHS) in the United Kingdom began deployment of EHR systems. The goal was to have all patients with a centralized electronic health record by 2010. While many hospitals acquired electronic patient records systems in this process, there was no national healthcare information exchange. [65][66][67] Ultimately, the program was dismantled after a cost to the UK taxpayer was over $24 Billion (12 Billion GPB), and is considered one of the most expensive healthcare IT failures . The UK Government is now considering open-source healthcare platform from the United States Veterans Affairs following on the success of the VistA_EHR deployment in Jordan [citation needed]. GP2GP is an NHS Connecting for Health project in the United Kingdom. It enables GPs to transfer a patient's electronic medical record to another practice when the patient moves onto the list.[68]
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Australia Australia is dedicated to the development of a lifetime electronic health record for all its citizens. PCEHR - the Personally Controlled Electronic Health Record - is the major national EHR initiative in Australia, being delivered through territory, state, and federal governments. This electronic health record was initially deployed in July 2012, and is under active development and extension.[69] MediConnect is an earlier program that provides an electronic medication record to keep track of patient prescriptions and provide stakeholders with drug alerts to avoid errors in prescribing. Within Australia, there is a not-for-profit organisation called Standards Australia, which has created a electronic health website relating to information not only about Australia and what is currently going on about EHRs but also globally. There is a large number of key stakeholders that contribute to the process of integrating EHRs within Australia,they range from each States Departments of Health to Universities around Australia and National E-Health Transition Authority [70] to name a few.
Austria In December 2012 Austria introduced an Electronic Health Records Act (EHR-Act).[71] These provisions are the legal foundation for a national EHR system based upon a substantial public interest according to Art 8(4) of the Data Protection Directive 95/46/EC.[72] In compliance to the Data Protection Directive (DPD) national electronic health records could be based upon explicit consent (Art 8(2)(a) DPD), the necessity for healthcare purposes (Art8(3) DPD) or substantial public interests (Art 8(4) DPD). The Austrian EHR-Act pursues an opt-out approach in order to harmonize the interests of public health and privacy in the best possible manner.
Structure and basic components of the Austrian EHR (ELGA)
The 4th Part of the Austrian Health Telematics Act 2012 (HTA 2012) - these are the EHR provisions - are one of the most detailed data protection rules within Austrian legislation. Numerous safeguards according to Art 8(4) DPD guarantee a high level of data protection. For example: • personal health data needs to be encrypted prior to transmission (§ 6 HTA 2012), or • strict rules on data usage allow personal health data only to be used for treatment purposes or exercising patients' rights (§ 14 HTA 2012), or • patients may declare their right to opt out from the national EHR at any time (§ 15 HTA 2012), or • the implementation of an EHR-Ombudsman, to support the patients in exercising their rights (§ 17 HTA 2012), or • the Access Control Center provides EHR-participants with full control over their data (§ 21 HTA 2012), or • judicial penalties for privacy breaches (Art 7 of the EHR-Act).
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Canada The Canadian province of Alberta started a large-scale operational EHR system project in 2005 called Alberta Netcare, which is expected to encompass all of Alberta by 2008. The College of Dental Surgeons of British Columbia [73] has compiled the Dental Records Management document which lays out the requirements, for records for their industry within the province of British Columbia.
[74]
Jordan In 2009, the Jordanian Government made a strategic decision to address quality and cost challenges in their healthcare system by investing in an effective, national e-health infrastructure. Following a period of detailed consultation and investigation, Jordan adopted the electronic health record system of the US Veterans Health Administration VistA_EHR because it was a proven, national-scale enterprise system capable of scaling to hundreds of hospitals and millions of patients. [75] In 2010 three of the country's largest hospitals went live with VistA_EHR. It is anticipated that all further hospital deployments based on this 'gold' version will require less than 20% effort and cost of the original hospitals, enabling rapid national coverage. The implementation of VistA EHR was estimated at 75% less cost than proprietary products, with the greatest savings related to reduced costs of configuration, customization, implementation and support. When completed, Jordan will be the largest country in the world with a single, comprehensive, national electronic health care delivery network to care for the country’s entire population in a single electronic network of over 850 hospitals and clinics.
Denmark The five regions each use their own setup of electronic health record systems. However, all patient data is registered in the national e-journal [76].
Estonia Estonia is the first country in the world that has implemented a nationwide EHR system, registering virtually all residents' medical history from birth to death.
Netherlands The vast majority of GP's [77] and all pharmacies and hospitals use EHR's. In hospitals computerized ordermanagment and medical imaging systems (PACS) are widely accepted. Whereas healthcare institutions continue to upgrade their EHR's functionalties, the national infrastructure is still far from being generally accepted. In 2012 the national EHR restarted under the joined ownership of GP's, pharmacies and hospitals. A major change is that as of January 2013 patients have to give their explicit permission that their data may be exchanged over the national infrastrucure. The national EHR is a virtual EHR and basically is a reference server which knows in which local EHR what kind of patientdata is stored. EDIFACT still is the most common way to exchange patient information electroniaclly between hospitals and GP's.
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UAE Abu Dhabi is leading the way in using national EHR data as a live longitudinal cohort in the assessment of risk of cardiovascular disease.
Saudi Arabia Arab Health Awards 2010 recognizes Saudi Arabia National Guard Health Affairs for greatest advancement in EHR development.
In veterinary medicine In UK veterinary practice, the replacement of paper recording systems with electronic methods of storing animal patient information escalated from the 1980s and the majority of clinics now use electronic medical records. In a sample of 129 veterinary practices, 89% used a Practice Management System (PMS) for data recording.[78] There are more than ten PMS providers currently in the UK. Collecting data directly from PMSs for epidemiological analysis abolishes the need for veterinarians to manually submit individual reports per animal visit and therefore increases the reporting rate.[79] Veterinary electronic medical record data are being used to investigate antimicrobial efficacy; risk factors for canine cancer; and inherited diseases in dogs and cats, in the small animal disease surveillance project 'VetCOMPASS' [80] (Veterinary Companion Animal Surveillance System) at the Royal Veterinary College, London, in collaboration with the University of Sydney and RxWorks [81] (the VetCOMPASS project was formerly known as VEctAR).[82]
The Future of Electronic Health Records – Personally Controlled Electronic Health Records A Personally Controlled Electronic Health Record (PCEHR) is a system that proposes to store admission or event summaries in an electronic format over a large network accessible by doctors, nurses, GPs and chemists without the need for written scripts or requesting medical files from another hospital. The system proposes to record and store any health information provided by a health care professional that has agreed to be a part of the system. This allows the storage and retrieval of a lifetimes worth of clinical and demographic information of a patient that can be viewed as event summaries and reports with the appropriate authorization
References [1] U.S. Department of Health and Human Services Centers for Medicare & Medicaid Services 42 CFR Parts 412, 413, 422 et al. Medicare and Medicaid Programs; Electronic Health Record Incentive Program; Final Rule [2] "Griffin Hospital reports breach of dozens of patient medical records", CtPost.com, March 29, 2010 [3] Kate Ramunni; "UCLA hospital scandal grows" Los Angeles Times, August 05, 2008 [4] "Health Information Exchanges and Your EMR Selection Process", New England Journal of Medicine, January 25, 2011 [5] Adapted from the IEEE definition of interoperability, and legal definitions used by the FCC (47 CFR 51.3), in statutes regarding copyright protection (17 USC 1201), and e-government services (44 USC 3601) [6] EMR in Ambulances (http:/ / emergencymedicalparamedic. com/ emr-for-paramedics), "Emergency Medical Paramedic", May 5, 2011, Retrieved June 4, 2011 [7] Ambulance Victoria Annual Report (http:/ / www. ambulance. vic. gov. au/ annualreport0809/ VACIS. htm), “Ambulance Victoria”, October 4, 2009, Retrieved June 4, 2011 [8] Handwriting and mobile computing experts: (http:/ / www. medscribbler. com/ handwriting_electronic_medical_records. html) Retrieved August 20, 2008 [9] M958 revision-Event monitors in PHS 1-02-02.PDF (http:/ / rods. health. pitt. edu/ Technical Reports/ 2002 JAMIA Event monitors in PHS. pdf) [10] E.g. [11] E.g. [12] Electronic health records not a panacea (http:/ / www. healthcareitnews. com/ news/ electronic-health-records-not-panacea-researchers-say)
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Electronic health record [13] Evidence on the costs and benefits of health information technology. (http:/ / www. cbo. gov/ doc. cfm?index=9168) Congressional Budget Office, May 2008. [14] Information Technology: Not a Cure for the High Cost of Health Care. Knowledge@Wharton, June 10, 2009. (http:/ / knowledge. wharton. upenn. edu/ article. cfm?articleID=2260) [15] Abraham Verghese. The Myth of Prevention. The Wall Street Journal, June 20, 2009. (http:/ / online. wsj. com/ article/ SB10001424052970204005504574235751720822322. html) [16] Defining and Testing EMR Usability. Healthcare Information and Management Systems Society (HIMSS), June 2009. (http:/ / www. himss. org/ content/ files/ HIMSS_DefiningandTestingEMRUsability. pdf) [17] NISTIR 7804: Technical Evaluation, Testing and Validation of the Usability of Electronic Health Records, p. 9-10. National Institute of Standards and Technology, Sept. 2011. (http:/ / www. nist. gov/ healthcare/ usability/ upload/ Draft_EUP_09_28_11. pdf) [18] U.S. Medicine - The Voice of Federal Medicine, May 2009. (http:/ / www. usmedicine. com/ articles/ electronic-records-system-unreliable-difficult-to-use-service-officials-tell-congress. html) [19] "EHRs Go Mobile" (http:/ / www. physicianspractice. com/ mobile-health/ content/ article/ 1462168/ 2087697) Marisa Torrieri, Physicians Practice, July/August 2012. [20] Kling, Rosenbaum, Sawyer, Indiana University. Understanding And Communicating Social Informatics: A Framework For Studying And Teaching The Human Contexts Of Information And Communication Technologies, pg. 23. Information Today Inc (September 15, 2005), ISBN 978-1-57387-228-7, (http:/ / www. amazon. com/ Understanding-Communicating-Social-Informatics-Communication/ dp/ 1573872288) [21] Sawyer and Rosenbaum. Social Informatics in the Information Sciences: Current Activities and Emerging Directions, p. 94.Informing Science: Special Issue on Information Science Research, Vol. 3 No. 2, 2000. (http:/ / www. inform. nu/ Articles/ Vol3/ v3n2p89-96r. pdf) [22] Tenner, Edward. Why Things Bite Back: Technology and the Revenge of Unintended Consequences. ISBN 978-0-679-74756-7, 1997 (http:/ / www. amazon. com/ Why-Things-Bite-Back-Consequences/ dp/ 0679747567). [23] Safely implementing health information and converging technologies, The Joint Commission, Issue 42, December 11, 2008 (http:/ / www. jointcommission. org/ assets/ 1/ 18/ SEA_42. PDF) [24] MEDMARX Adverse Drug Event Reporting database (https:/ / www. medmarx. com/ docs/ about. pdf) [25] Health informatics - Guidance on the management of clinical risk relating to the deployment and use of health software (formerly ISO/TR 29322:2008(E)). DSCN18/2009, Examples of potential harm presented by health software, Annex A, p. 38 (http:/ / www. isb. nhs. uk/ documents/ isb-0160/ dscn-18-2009/ 0160182009specification. pdf). [26] FDA memo. H-IT Safety Issues, table 4, page 3, Appendix B, p. 7-8 (with examples), and p. 5, summary. Memo obtained and released by Fred Schulte and Emma Schwartz at the Huffington Post Investigative Fund, now part of the Center for Public Integrity, in an Aug. 3, 2010 article FDA, Obama digital medical records team at odds over safety oversight (http:/ / www. iwatchnews. org/ 2010/ 08/ 03/ 7096/ fda-obama-digital-medical-records-team-odds-over-safety-oversight), memo itself (http:/ / www. scribd. com/ huffpostfund/ d/ 33754943-Internal-FDA-Report-on-Adverse-Events-Involving-Health-Information-Technology) [27] Goodman KW, Berner ES, Dente MA, et al. Challenges in ethics, safety, best practices, and oversight regarding HIT vendors, their customers, and patients: a report of an AMIA special task force. J Am Med Inform Assoc (2010). (http:/ / jamia. bmj. com/ site/ icons/ amiajnl8946. pdf) [28] Rowe JC. Doctors Go Digital. The New Atlantis (2011). (http:/ / www. thenewatlantis. com/ publications/ doctors-go-digital) [29] US Code of Federal Regulations, Title45, Volume 1 (Revised October 1, 2005): of Individually Identifiable Health Information (45CFR164.501) (http:/ / frwebgate. access. gpo. gov/ cgi-bin/ get-cfr. cgi?YEAR=current& TITLE=45& PART=164& SECTION=501& SUBPART=& TYPE=TEXTPrivacy) Retrieved July 30, 2006 [30] Summary of the HIPAA Privacy Rule (http:/ / www. hhs. gov/ ocr/ privacy/ hipaa/ understanding/ summary/ index. html) [31] Privacy Rights Clearinghouse's Chronology of Data Security Breaches (https:/ / www. privacyrights. org/ data-breach/ new) [32] European Parliament and Council (24 October 1995): EU Directive 95/46/EC - The Data Protection Directive (http:/ / www. dataprivacy. ie/ viewdoc. asp?m=& fn=/ documents/ LEGAL/ 6aii. htm) Retrieved July 30, 2006 [33] Medical Manager History (http:/ / docs. mirrormed. org/ index. php/ Medical_Manager_History) [34] "Can Technology Get You Sued?" (http:/ / www. physicianspractice. com/ risk-management/ content/ article/ 1462168/ 2042414) Shelly K. Schwartz, Physicians Practice, March 2012. [35] European Patient Smart Open Services Work Plan: epSOS: Legal and Regulatory Issues (http:/ / www. epsos. eu/ about-epsos/ work-plan-new. html#c501) Retrieved May 4, 2008 [36] Privacy Rights Clearinghouse's Chronology of Data Security Breaches involving Medical Information (https:/ / www. privacyrights. org/ data-breach/ new) [37] Kierkegaard, P. (2012) Medical data breaches: Notification delayed is notification denied, Computer Law & Security Report , 28 (2), p.163–183. (http:/ / www. sciencedirect. com/ science/ article/ pii/ S0267364912000209) [38] HIPAA Basics: Medical Privacy in the Electronic Age from the Privacy Rights Clearinghouse www.privacyrights.org (https:/ / www. privacyrights. org/ fs/ fs8a-hipaa. htm) [39] DEPARTMENT OF HEALTH AND HUMAN SERVICES Breach Notification for Unsecured Protected Health Information (http:/ / www. gpo. gov/ fdsys/ pkg/ FR-2009-08-24/ pdf/ E9-20169. pdf) [40] Clayton L. Reynolds MD, FACP, FACPE (March 2006): Paper on Concept Processing (http:/ / www. infor-med. com/ downloads/ why_praxis_downloads/ Charting_Bass_Ackward. pdf) Retrieved July 27, 2006
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Electronic health record [41] Maekawa Y, Majima Y.; "Issues to be improved after introduction of a non-customized Electronic Medical Record system (EMR) in a Private General Hospital and efforts toward improvement"; Studies in Health Technology and Informatics 2006 [42] Tüttelmann F, Luetjens CM, Nieschlag E.; "Optimising workflow in andrology: a new electronic patient record and database"; Asian Journal of Andrology March 2006 [43] The Digital Office, September 2007, vol 2, no.9. HIMSS [44] Gina Rollins."The Perils of Customization." Journal of AHIMA 77, no.6 (2006):24-28. [45] The Master Child Index consolidated 4,610,585 records that were contained in both databases into 2,977,290 records through a match and merge system. [46] Pohjonen H. Images can now cross borders, but what about the legislation? (http:/ / www. diagnosticimaging. com/ imaging-trends-advances/ ultrasound-source/ archive/ article/ 113619/ 1584986) Diagnostic Imaging Europe. June/July 2010;26(4):16. [47] Patrick Kierkegaard (2011) Electronic health record: Wiring Europe's healthcare (http:/ / www. sciencedirect. com/ science/ article/ pii/ S0267364911001257), Computer Law & Security Review, Volume 27, Issue 5, September 2011, Pages 503-515, ISSN 0267-3649, 10.1016/j.clsr.2011.07.013. Retrieved Dec 15, 2011 [48] Smaltz, Detlev and Eta Berner. The Executive's Guide to Electronic Health Records.' (2007, Health Administration Press) p.03 [49] Are More Doctors Adopting EHRs? (http:/ / www. nuesoft. com/ blog/ are-more-doctors-adopting-ehrs/ ) Retrieved March 31, 2011 [50] National Center for Health : United States, 2008] Retrieved December 15, 2009 [51] National Archives and Records Administration (NARA): Long-Term Usability of Optical Media (http:/ / palimpsest. stanford. edu/ bytopic/ electronic-records/ electronic-storage-media/ critiss. html) Retrieved July 30, 2006 [52] Medical Board of California: Medical Records - Frequently Asked Questions (http:/ / medbd. ca. gov/ consumer/ complaint_info_questions_records. html) Retrieved July 30, 2006 [53] http:/ / www. law. cornell. edu/ jureeka/ index. php?doc=USPubLaws& cong=111& no=5 [54] Cohen GR, Grossman JM, O'Malley AS (2010). "Electronic Medical Records and Communication with Patients and Other Clinicians: Are We Talking Less?". Center for Studying Health System Change, Issue Brief No. 131 ( full text (http:/ / www. hschange. com/ CONTENT/ 1125/ #ib1)) [55] Torrieri, Marisa "Dealing with Meaningful Use Attestation Aggravation" (http:/ / www. physicianspractice. com/ meaningful-use/ content/ article/ 1462168/ 2009082). Physicians Practice. January 2012. [56] "Meaningful Use: Stage 2 Regulations Overview" (http:/ / www. physicianspractice. com/ meaningful-use/ content/ article/ 1462168/ 2098253) Robert Anthony, CMS, August 30, 2012. [57] "EHR Incentive Program: A Progress Report" (http:/ / www. physicianspractice. com/ meaningful-use/ content/ article/ 1462168/ 2099001) Marisa Torrieri, Physicians Practice, September 2012. [58] cited in [59] Parish, Colin (March 20, 2006). Edging towards a brave new IT world. Nursing Standard 27:15-16 [60] (http:/ / web. archive. org/ web/ 20091024042950/ http:/ / www1. va. gov/ vadodhealthitsharing/ page. cfm?pg=23) Retrieved March 4, 2010 [61] (http:/ / web. archive. org/ web/ 20091024045131/ http:/ / www1. va. gov/ vadodhealthitsharing/ page. cfm?pg=9) Retrieved March 4, 2010 [62] (http:/ / web. archive. org/ web/ 20091024044210/ http:/ / www1. va. gov/ vadodhealthitsharing/ page. cfm?pg=4) Retrieved March 4, 2010 [63] Retrieved March 4, 2010 (http:/ / www. connectopensource. org/ about/ what-is-CONNECT) [64] Retrieved March 4, 2010 (http:/ / connectopensource. osuosl. org/ sites/ connectopensource. osuosl. org/ files/ CONNECTOverview. pdf) [65] Greenhalgh T, Stramer K, Bratan T, Byrne E, Russell J, Potts HWW (2010). Adoption and non-adoption of a shared electronic summary care record in England: A mixed-method case study. BMJ, 340, c3111 [66] Bewley S, Perry H, Fawdry R, Cumming G (2011). NHS IT requires the wisdom of the crowd not the marketplace. (http:/ / www. bmj. com/ content/ 343/ bmj. d5317. full/ reply#bmj_el_270562) Accessed 16 April 2012 [67] "The government today announced an acceleration of the dismantling of the National Programme for IT, following the conclusions of a new review by the Cabinet Office's Major Projects Authority (MPA)... The MPA found that the National Programme for IT has not and cannot deliver to its original intent."(sic) [68] GP2GP Website (http:/ / www. connectingforhealth. nhs. uk/ delivery/ programmes/ gp2gp) [69] http:/ / www. ehealth. gov. au www.ehealth.gov.au [70] http:/ / www. nehta. gov. au/ [71] Electronic Health Records Act (EHR-Act) (http:/ / www. ilia. ch/ wordpress/ wp-content/ uploads/ 2013/ 02/ austrian_ehr-act_ILIA. pdf) [72] Data Protection Directive 95/46/EC (http:/ / eur-lex. europa. eu/ LexUriServ/ LexUriServ. do?uri=CELEX:31995L0046:en:HTML) [73] http:/ / www. cdsbc. org [74] http:/ / www. cdsbc. org/ ~ASSETS/ DOCUMENT/ Dental-Records-Mgt. pdf [75] http:/ / nhsvista. net/ jordan/ [76] https:/ / www. sundhed. dk/ borger/ min-side/ mine-sundhedsdata/ min-e-journal/ [77] http:/ / www. informationWeek. com/ healthcare/ electronic-medical-records/ ehr-adoption-us-remains-the-slow-poke/ 240142152, Ken Terry, Informationweek [78] Gill, M. (2007) Attitudes to clinical audit in veterinary practice, Royal Veterinary College elective project, unpublished work [79] Carruthers, H. (2009) Disease surveillance in small animal practice, In Pract, 31(7): 356–358 [80] http:/ / www. rvc. ac. uk/ VetCOMPASS [81] http:/ / www. rxworks. com
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[82] VEctAR (Veterinary Electronic Animal Record) (2010) from http:/ / www. rvc. ac. uk/ VEctAR/
External links • Can Electronic Health Record Systems Transform Health Care? (http://www.eecs.harvard.edu/cs199r/ readings/RAND_benefits.pdf) • Health Information Technology in the United States (http://www.rwjf.org/files/publications/other/ EHRReport0609.pdf) • How to Enable Standard-Compliant Streaming of Images in Electronic Health Records (http://www.aware. com/imaging/whitepapers/wp_jpipwado.htm) a white paper by Aware Inc. (http://www.aware.com/imaging/ whitepapers.htm) • Open-Source EHR Systems for Ambulatory Care: A Market Assessment (http://www.chcf.org/topics/view. cfm?itemID=133551)(California HealthCare Foundation, January 2008) • US Department of Health and Human Services (HHS), Office of the National Coordinator for Health Information Technology (ONC) (http://www.hhs.gov/healthit/) • US Department of Health and Human Services (HHS), Agency for Healthcare Research and Quality (AHRQ), National Resource Center for Health Information Technology (http://healthit.ahrq.gov/emr) • ICMCC portal: EHR info and blogs (http://recordaccess.icmcc.org/) • Security Aspects in Electronic Personal Health Record: Data Access and Preservation (http://www. digitalpreservationeurope.eu/publications/briefs/security_aspects.pdf) - a briefing paper at Digital Preservation Europe
Electronic medical record An electronic health record (EHR) is an evolving concept defined as a systematic collection of electronic health information about individual patients or populations. It is a record in digital format that is theoretically capable of being shared across different health care settings. In some cases this sharing can occur by way of network-connected, enterprise-wide information systems and other information networks or exchanges. EHRs may include a range of data, including demographics, medical history, medication and allergies, immunization status, laboratory test results, radiology images, vital signs, personal statistics like age and weight, and billing information.
Sample view of an electronic health record based on images
The system is designed to capture and re-present data that accurately capture the state of the patient at all times. It allows for
Electronic medical record
an entire patient history to be viewed without the need to track down the patient’s previous medical record volume and assists in ensuring data is accurate, appropriate and legible. It reduces the chances of data replication as there is only one modifiable file, which means the file is constantly up to date when viewed at a later date and eliminates the issue of lost forms or paperwork. Due to all the information being in a single file, it makes it much more effective when extracting medical data for the examination of possible trends and long term changes in the patient.
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Sample view of an electronic health record
Terminology The terms EHR, EPR (electronic patient record) and EMR (electronic medical record) are often used interchangeably, although differences between them can be defined. The EMR can, for example, be defined as the patient record created in hospitals and ambulatory environments, and which can serve as a data source for the EHR. It is important to note that an EHR is generated and maintained within an institution, such as a hospital, integrated delivery network, clinic, or physician office, to give patients, physicians and other health care providers, employers, and payers or insurers access to a patient's medical records across facilities. A personal health record (PHR) is, in modern parlance, generally defined as an EHR that the individual patient controls.
Comparison with paper-based records Paper-based records require a significant amount of storage space compared to digital records. In the United States, most states require physical records be held for a minimum of seven years. The costs of storage media, such as paper and film, per unit of information differ dramatically from that of electronic storage media. When paper records are stored in different locations, collating them to a single location for review by a health care provider is time consuming and complicated, whereas the process can be simplified with electronic records. This is particularly true in the case of person-centered records, which are impractical to maintain if not electronic (thus difficult to centralize or federate). When paper-based records are required in multiple locations, copying, faxing, and transporting costs are significant compared to duplication and transfer of digital records.[citation needed] Because of these many "after-entry" benefits, federal and state governments, insurance companies and other large medical institutions are heavily promoting the adoption of electronic medical records. The US Congress included a formula of both incentives (up to $44,000 per physician under Medicare or up to $65,000 over six years, under Medicaid) and penalties (i.e. decreased Medicare and Medicaid reimbursements for covered patients to doctors who fail to use EMRs by 2015) for EMR/EHR adoption versus continued use of paper records as part of the Health Information Technology for Economic and Clinical Health (HITECH) Act, enacted as part of the American Recovery and Reinvestment Act of 2009.[1] One study estimates electronic medical records improve overall efficiency by 6% per year, and the monthly cost of an EMR may (depending on the cost of the EMR) be offset by the cost of only a few "unnecessary" tests or admissions. Jerome Groopman disputed these results, publicly asking "how such dramatic claims of cost-saving and
Electronic medical record quality improvement could be true". However, the increased portability and accessibility of electronic medical records may also increase the ease with which they can be accessed and stolen by unauthorized persons or unscrupulous users versus paper medical records, as acknowledged by the increased security requirements for electronic medical records included in the Health Information and Accessibility Act and by large-scale breaches in confidential records reported by EMR users.[2][3] Concerns about security contribute to the resistance shown to their widespread adoption. Handwritten paper medical records can be associated with poor legibility, which can contribute to medical errors. Pre-printed forms, the standardization of abbreviations, and standards for penmanship were encouraged to improve reliability of paper medical records. Electronic records help with the standardization of forms, terminology and abbreviations, and data input. Digitization of forms facilitates the collection of data for epidemiology and clinical studies.[4] In contrast, EMRs can be continuously updated (within certain legal limitations – see below). The ability to exchange records between different EMR systems ("interoperability"[5]) would facilitate the co-ordination of health care delivery in non-affiliated health care facilities. In addition, data from an electronic system can be used anonymously for statistical reporting in matters such as quality improvement, resource management and public health communicable disease surveillance.
In ambulances Ambulance services in Australia have introduced the use of EMR systems [6] The benefits of EMR in ambulances include the following: better training for paramedics, review of clinical standards, better research options for pre-hospital care and design of future treatment options [7] Automated handwriting recognition of ambulance medical forms has also been successful. These systems allow paper-based medical documents to be converted to digital text with substantially less cost overhead. Patient identifying information would not be converted to comply with government privacy regulations. The data can then be efficiently used for epidemiological analysis.
Technical features • • • •
Digital formatting enables information to be used and shared over secure networks Track care (e.g. prescriptions) and outcomes (e.g. blood pressure) Trigger warnings and reminders Send and receive orders, reports, and results
Health Information Exchange • • • •
Technical and social framework that enables information to move electronically between organizations Reporting to public health ePrescribing Sharing laboratory results with providers
Using an EMR to read and write a patient's record is not only possible through a workstation but, depending on the type of system and health care settings, may also be possible through mobile devices that are handwriting capable.[8] Electronic Medical Records may include access to Personal Health Records (PHR) which makes individual notes from an EMR readily visible and accessible for consumers. Some EMR systems automatically monitor clinical events, by analyzing patient data from an electronic health record to predict, detect and potentially prevent adverse events. This can include discharge/transfer orders, pharmacy orders, radiology results, laboratory results and any other data from ancillary services or provider notes.[9] This type of event monitoring has been implemented using the Louisiana Public health information exchange linking state wide public health with electronic medical records. This system alerted medical providers when a patient with HIV/AIDS had not
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Electronic medical record received care in over twelve months. This system greatly reduced the number of missed critical opportunities.
Philosophical views of the EHR Within a meta-narrative systematic review of research in the field, Prof. Trish Greenhalgh and colleagues defined a number of different philosophical approaches to the EHR. The health information systems literature has seen the EHR as a container holding information about the patient, and a tool for aggregating clinical data for secondary uses (billing, audit etc.). However, other research traditions see the EHR as a contextualised artifact within a socio-technical system. For example, actor-network theory would see the EHR as an actant in a network,[10] while research in computer supported cooperative work (CSCW) sees the EHR as a tool supporting particular work. Several possible advantages to EHRs over paper records have been proposed, but there is debate about the degree to which these are achieved in practice.[11]
Implementation, end user and patient considerations Quality Several studies call into question whether EHRs improve the quality of care.[12] However, a recent multi-provider study in diabetes care, published in the New England Journal of Medicine, found evidence that practices with EHR provided better quality care. EHR do help improve care coordination. Since anyone with that EHR can view the patients chart it cuts down on guessing histories, seeing multiple specialists, smoothing transitions between care settings, and better care in emergency situations EHRs may also improve prevention by providing doctors and patients better access to test results, identifying missing patient information, and offering evidence-based recommendations for preventive services.
Costs The steep price of EHR and provider uncertainty regarding the value they will derive from adoption in the form of return on investment has a significant influence on EHR adoption. In a project initiated by the Office of the National Coordinator for Health Information (ONC), surveyors found that hospital administrators and physicians who had adopted EHR noted that any gains in efficiency were offset by reduced productivity as the technology was implemented, as well as the need to increase information technology staff to maintain the system. The U.S. Congressional Budget Office concluded that the cost savings may occur only in large integrated institutions like Kaiser Permanente, and not in small physician offices. They challenged the Rand Corp. estimates of savings. "Office-based physicians in particular may see no benefit if they purchase such a product—and may even suffer financial harm. Even though the use of health IT could generate cost savings for the health system at large that might offset the EHR's cost, many physicians might not be able to reduce their office expenses or increase their revenue sufficiently to pay for it. For example. the use of health IT could reduce the number of duplicated diagnostic tests. However, that improvement in efficiency would be unlikely to increase the income of many physicians."[13] One CEO of an EHR company has argued if a physician performs tests in the office, it might reduce his or her income. Doubts have been raised about cost saving from EHRs by researchers at Harvard University, the Wharton School of the University of Pennsylvania, Stanford University, and others.[14][15]
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Electronic medical record
Software quality and usability deficiencies The Healthcare Information and Management Systems Society (HIMSS), a very large U.S. healthcare IT industry trade group, observed that EHR adoption rates "have been slower than expected in the United States, especially in comparison to other industry sectors and other developed countries. A key reason, aside from initial costs and lost productivity during EMR implementation, is lack of efficiency and usability of EMRs currently available."[16] The U.S. National Institute of Standards and Technology of the Department of Commerce studied usability in 2011 and lists a number of specific issues that have been reported by health care workers.[17] The U.S. military's EHR, AHLTA, was reported to have significant usability issues.[18] However, physicians are embracing mobile technologies such as smartphones and tablets at a rapid pace. According to a 2012 survey by Physicians Practice, 62.6 percent of respondents (1,369 physicians, practice managers, and other healthcare providers) say they use mobile devices in the performance of their job. Mobile devices are increasingly able to synch up with electronic health record systems thus allowing physicians to access patient records from remote locations. Most devices are extensions of desk-top EHR systems, using a variety of software to communicate and access files remotely. The advantages of instant access to patient records at any time and any place are clear, but bring a host of security concerns. As mobile systems become more prevalent, practices will need comprehensive policies that govern security measures and patient privacy regulations.[19]
Unintended consequences Per empirical research in social informatics, information and communications technology (ICT) use can lead to both intended and unintended consequences.[20][21][22] A 2008 Sentinel Event Alert from the U.S. Joint Commission, the organization that accredits American hospitals to provide healthcare services, states that "As health information technology (HIT) and 'converging technologies'—the interrelationship between medical devices and HIT—are increasingly adopted by health care organizations, users must be mindful of the safety risks and preventable adverse events that these implementations can create or perpetuate. Technology-related adverse events can be associated with all components of a comprehensive technology system and may involve errors of either commission or omission. These unintended adverse events typically stem from human-machine interfaces or organization/system design."[23] The Joint Commission cites as an example the United States Pharmacopeia MEDMARX database[24] where of 176,409 medication error records for 2006, approximately 25 percent (43,372) involved some aspect of computer technology as at least one cause of the error. The National Health Service (NHS) in the UK reports specific examples of potential and actual EHR-caused unintended consequences in their 2009 document on the management of clinical risk relating to the deployment and use of health software.[25] In a Feb. 2010 U.S. Food and Drug Administration (FDA) memorandum, FDA notes EHR unintended consequences include EHR-related medical errors due to (1) errors of commission (EOC), (2) errors of omission or transmission (EOT), (3) errors in data analysis (EDA), and (4) incompatibility between multi-vendor software applications or systems (ISMA) and cites examples. In the memo FDA also notes the "absence of mandatory reporting enforcement of H-IT safety issues limits the numbers of medical device reports (MDRs) and impedes a more comprehensive understanding of the actual problems and implications."[26] A 2010 Board Position Paper by the American Medical Informatics Association (AMIA) contains recommendations on EHR-related patient safety, transparency, ethics education for purchasers and users, adoption of best practices, and re-examination of regulation of electronic health applications.[27] Beyond concrete issues such as conflicts of interest and privacy concerns, questions have been raised about the ways in which the physician-patient relationship would be affected by an electronic intermediary.[28]
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Privacy and confidentiality In the United States in 2011 there were 380 major data breaches involving 500 or more patients' records listed on the website kept by the United States Department of Health and Human Services (HHS) Office for Civil Rights. So far, from the first wall postings in September 2009 through the latest on December 8, 2012, there have been 18,059,831 "individuals affected," and even that massive number is an undercount of the breach problem. The civil rights office has not released the records of tens of thousands of breaches it has received under a federal reporting mandate on breaches affecting fewer than 500 patients per incident.
Governance, privacy and legal issues Privacy concerns In the United States, Great Britain, and Germany, the concept of a national centralized server model of healthcare data has been poorly received. Issues of privacy and security in such a model have been of concern. Privacy concerns in healthcare apply to both paper and electronic records. According to the Los Angeles Times, roughly 150 people (from doctors and nurses to technicians and billing clerks) have access to at least part of a patient's records during a hospitalization, and 600,000 payers, providers and other entities that handle providers' billing data have some access also. Recent revelations of "secure" data breaches at centralized data repositories, in banking and other financial institutions, in the retail industry, and from government databases, have caused concern about storing electronic medical records in a central location. Records that are exchanged over the Internet are subject to the same security concerns as any other type of data transaction over the Internet. The Health Insurance Portability and Accountability Act (HIPAA) was passed in the US in 1996 to establish rules for access, authentications, storage and auditing, and transmittal of electronic medical records. This standard made restrictions for electronic records more stringent than those for paper records. However, there are concerns as to the adequacy of these standards. In the United States, information in electronic medical records is referred to as Protected Health Information (PHI) and its management is addressed under the Health Insurance Portability and Accountability Act (HIPAA) as well as many local laws.[29] The HIPAA protects a patient's information; the information that is protected under this act are: information doctors and nurses input into the electronic medical record, conversations between a doctor and a patient that may have been recorded, as well as billing information. Under this act there is a limit as to how much information can be disclosed, and as well as who can see a patients information. Patients also get to have a copy of their records if they desire, and get a notified if their information is ever to be shared with third parties. Covered entities may disclose protected health information to law enforcement officials for law enforcement purposes as required by law (including court orders, court-ordered warrants, subpoenas) and administrative requests; or to identify or locate a suspect, fugitive, material witness, or missing person.[30] Medical and health care providers experienced 767 security breaches resulting in the compromised confidential health information of 23,625,933 patients during the period of 2006–2012.[31] In the European Union (EU), several directives of the European Parliament and of the Council protect the processing and free movement of personal data, including for purposes of health care.[32] Threats to health care information can be categorized under three headings: • Human threats, such as employees or hackers • Natural and environmental threats, such as earthquakes, hurricanes and fires. • Technology failures, such as a system crashing These threats can either be internal, external, intentional and unintentional. Therefore, one will find health information systems professionals having these particular threats in mind when discussing ways to protect the health information of patients. The Health Insurance Portability and Accountability Act (HIPAA) has developed a
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Electronic medical record framework to mitigate the harm of these threats that is comprehensive but not so specific as to limit the options of healthcare professionals who may have access to different technology. In the European Union (EU), several Directives of the European Parliament and of the Council protect the processing and free movement of personal data, including for purposes of health care. Personal Information Protection and Electronic Documents Act (PIPEDA) was given Royal Assent in Canada on April 13, 2000 to establish rules on the use, disclosure and collection of personal information. The personal information includes both non-digital and electronic form. In 2002, PIPEDA extended to the health sector in Stage 2 of the law's implementation. There are four provinces where this law does not apply because its privacy law was considered similar to PIPEDA: Alberta, British Columbia, Ontario and Quebec. One major issue that has risen on the privacy of the US network for electronic health records is the strategy to secure the privacy of patients. Former US president Bush called for the creation of networks, but federal investigators report that there is no clear strategy to protect the privacy of patients as the promotions of the electronic medical records expands throughout the United States. In 2007, the Government Accountability Office reports that there is a "jumble of studies and vague policy statements but no overall strategy to ensure that privacy protections would be built into computer networks linking insurers, doctors, hospitals and other health care providers." The privacy threat posed by the interoperability of a national network is a key concern. One of the most vocal critics of EMRs, New York University Professor Jacob M. Appel, has claimed that the number of people who will need to have access to such a truly interoperable national system, which he estimates to be 12 million, will inevitable lead to breaches of privacy on a massive scale. Appel has written that while "hospitals keep careful tabs on who accesses the charts of VIP patients," they are powerless to act against "a meddlesome pharmacist in Alaska" who "looks up the urine toxicology on his daughter's fiance in Florida, to check if the fellow has a cocaine habit." This is a significant barrier for the adoption of an EHR. Accountability among all the parties that are involved in the processing of electronic transactions including the patient, physician office staff, and insurance companies, is the key to successful advancement of the EHR in the US Supporters of EHRs have argued that there needs to be a fundamental shift in "attitudes, awareness, habits, and capabilities in the areas of privacy and security" of individual's health records if adoption of an EHR is to occur. According to the Wall Street Journal, the DHHS takes no action on complaints under HIPAA, and medical records are disclosed under court orders in legal actions such as claims arising from automobile accidents. HIPAA has special restrictions on psychotherapy records, but psychotherapy records can also be disclosed without the client's knowledge or permission, according to the Journal. For example, Patricia Galvin, a lawyer in San Francisco, saw a psychologist at Stanford Hospital & Clinics after her fiance committed suicide. Her therapist had assured her that her records would be confidential. But after she applied for disability benefits, Stanford gave the insurer her therapy notes, and the insurer denied her benefits based on what Galvin claims was a misinterpretation of the notes. Within the private sector, many companies are moving forward in the development, establishment and implementation of medical record banks and health information exchange. By law, companies are required to follow all HIPAA standards and adopt the same information-handling practices that have been in effect for the federal government for years. This includes two ideas, standardized formatting of data electronically exchanged and federalization of security and privacy practices among the private sector. Private companies have promised to have "stringent privacy policies and procedures." If protection and security are not part of the systems developed, people will not trust the technology nor will they participate in it.
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Legal issues Liability Legal liability in all aspects of healthcare was an increasing problem in the 1990s and 2000s. The surge in the per capita number of attorneys and changes in the tort system caused an increase in the cost of every aspect of healthcare, and healthcare technology was no exception. Failure or damages caused during installation or utilization of an EHR system has been feared as a threat in lawsuits. Similarly, it's important to recognize that the implementation of electronic health records carries with it significant legal risks. This liability concern was of special concern for small EHR system makers. Some smaller companies may be forced to abandon markets based on the regional liability climate.[33]Wikipedia:Identifying reliable sources Larger EHR providers (or government-sponsored providers of EHRs) are better able to withstand legal assaults. While there is no argument that electronic documentation of patient visits and data brings improved patient care, there is increasing concern that such documentation could open physicians to an increased incidence of malpractice suits. Disabling physician alerts, selecting from dropdown menus, and the use of templates can encourage physicians to skip a complete review of past patient history and medications, and thus miss important data. Another potential problem is electronic time stamps. Many physicians are unaware that EHR systems produce an electronic time stamp every time the patient record is updated. If a malpractice claim goes to court, through the process of discovery, the prosecution can request a detailed record of all entries made in a patient's electronic record. Waiting to chart patient notes until the end of the day and making addendums to records well after the patient visit can be problematic, in that this practice could result in less than accurate patient data or indicate possible intent to illegally alter the patient's record.[34] In some communities, hospitals attempt to standardize EHR systems by providing discounted versions of the hospital's software to local healthcare providers. A challenge to this practice has been raised as being a violation of Stark rules that prohibit hospitals from preferentially assisting community healthcare providers. In 2006, however, exceptions to the Stark rule were enacted to allow hospitals to furnish software and training to community providers, mostly removing this legal obstacle.Wikipedia:Identifying reliable sourcesWikipedia:Identifying reliable sources Legal interoperability In cross-border use cases of EHR implementations, the additional issue of legal interoperability arises. Different countries may have diverging legal requirements for the content or usage of electronic health records, which can require radical changes of the technical makeup of the EHR implementation in question. (especially when fundamental legal incompatibilities are involved) Exploring these issues is therefore often necessary when implementing cross-border EHR solutions.[35]
Regulatory compliance • Consumer Credit Act 2006 • HIPAA • Health Level 7
Contribution under UN administration and accredited organizations The United Nations World Health Organization (WHO) administration intentionally does not contribute to an internationally standardized view of medical records nor to personal health records. However, WHO contributes to minimum requirements definition for developing countries.
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Electronic medical record The United Nations accredited standardisation body International Organization for Standardization (ISO) however has settled thorough wordWikipedia:Please clarify for standards in the scope of the HL7 platform for health care informatics. Respective standards are available with ISO/HL7 10781:2009 Electronic Health Record-System Functional Model, Release 1.1 and subsequent set of detailing standards.
Medical Data Breach The Security Rule, according to Health and Human Services (HHS), establishes a security framework for small practices as well as large institutions. All covered entities must have a written security plan. The HHS identifies three components as necessary for the security plan: administrative safeguards, physical safeguards, and technical safeguards. However, medical and healthcare providers have experienced 767 security breaches resulting in the compromised confidential health information of 23,625,933 patients during the period of 2006-2012.[36] The majority of the counties in Europe have made a strategy for the development and implementation of the Electronic Health Record Systems. This would mean greater access to health records by numerous stakeholders, even from countries with lower levels of privacy protection. The forthcoming implementation of the Cross Border Health Directive and the EU Commission's plans to centralize all health records are of prime concern to the EU public who believe that the health care organizations and governments cannot be trusted to manage their data electronically and expose them to more threats. The idea of a centralized electronic health record system has been poorly received by the public who are wary that the governments may extend the use of the system beyond its purpose. There is also the risk for privacy breaches that could allow sensitive health care information to fall into the wrong hands. Some countries have enacted laws requiring safeguards to be put in place to protect the security and confidentiality of medical information as it is shared electronically and to give patients some important rights to monitor their medical records and receive notification for loss and unauthorized acquisition of health information. The United States and the EU have imposed mandatory medical data breach notifications.[37] The Health Insurance Portability and Accessibility Act (HIPAA) requires safeguards to limit the number of people who have access to personal information. However, given the number of people who may have access to your information as part of the operations and business of the health care provider or plan, there is no realistic way to estimate the number of people who may come across your records.[38] Additionally, law enforcement access is authorized under HIPAA. In some cases, medical information may be disclosed without a warrant or court order.
Breach notification The purpose of a personal data breach notification is to protect individuals so that they can take all the necessary actions to limit the undesirable effects of the breach and to motivate the organization to improve the security of the infrastructure to protect the confidentiality of the data. The US law requires the entities to inform the individuals in the event of breach while the EU Directive currently requires breach notification only when the breach is likely to adversely affect the privacy of the individual. Personal health data is valuable to individuals and is therefore difficult to make an assessment whether the breach will cause reputational or financial harm or cause adverse effects on one's privacy. The Security Rule that was adopted in 2005 did not require breach notification. However, notice might be required by state laws that apply to a variety of industries, including health care providers. In California, a law has been in place since 2003 requiring that a HIPAA covered organization's breach could have triggered a notice even though notice was not required by the HIPAA Security Rule. Since January 1, 2009, California residents are required to receive notice of a health information breach.
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Electronic medical record Federal law and regulations now provide rights to notice of a breach of health information. The Health Information Technology for Economic and Clinical Health (HITECH) Act requires HHS and the Federal Trade Commission (FTC) to jointly study and report on privacy and data security of personal health information. HITECH also requires the agencies to issue breach notification rules that apply to HIPAA covered entities and Web-based vendors that store health information electronically. The FTC has adopted rules regarding breach notification for internet-based vendors.[39] The Breach notification law in the EU provides better privacy safeguards with fewer exemptions, unlike the US law which exempts unintentional acquisition, access, or use of protected health information and inadvertent disclosure under a good faith belief.
Technical issues Standards • ANSI X12 (EDI) - transaction protocols used for transmitting patient data. Popular in the United States for transmission of billing data. • CEN's TC/251 provides EHR standards in Europe including: • EN 13606, communication standards for EHR information
• • • •
• CONTSYS (EN 13940), supports continuity of care record standardization. • HISA (EN 12967), a services standard for inter-system communication in a clinical information environment. Continuity of Care Record - ASTM International Continuity of Care Record standard DICOM - an international communications protocol standard for representing and transmitting radiology (and other) image-based data, sponsored by NEMA (National Electrical Manufacturers Association) HL7 - a standardized messaging and text communications protocol between hospital and physician record systems, and between practice management systems ISO - ISO TC 215 provides international technical specifications for EHRs. ISO 18308 describes EHR architectures
The U.S. federal government has issued new rules of electronic health records. Open Specifications • openEHR: an open community developed specification for a shared health record with web-based content developed online by experts. Strong multilingual capability. • Virtual Medical Record: HL7's proposed model for interfacing with clinical decision support systems. • SMART (Subsitutable Medical Apps, reusable technologies): an open platform specification to provide a standard base for healthcare applications. Customization Each healthcare environment functions differently, often in significant ways. It is difficult to create a "one-size-fits-all" EHR system. An ideal EHR system will have record standardization but interfaces that can be customized to each provider environment. Modularity in an EHR system facilitates this. Many EHR companies employ vendors to provide customization. This customization can often be done so that a physician's input interface closely mimics previously utilized paper forms.[40] At the same time they reported negative effects in communication, increased overtime, and missing records when a non-customized EMR system was utilized.[41] Customizing the software when it is released yields the highest benefits because it is adapted for the users and tailored to workflows specific to the institution.[42]
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Electronic medical record Customization can have its disadvantages. There is, of course, higher costs involved to implementation of a customized system initially. More time must be spent by both the implementation team and the healthcare provider to understand the workflow needs. Development and maintenance of these interfaces and customizations can also lead to higher software implementation and maintenance costs.[43]Wikipedia:Identifying reliable sources[44]Wikipedia:Identifying reliable sources
Long-term preservation and storage of records An important consideration in the process of developing electronic health records is to plan for the long-term preservation and storage of these records. The field will need to come to consensus on the length of time to store EHRs, methods to ensure the future accessibility and compatibility of archived data with yet-to-be developed retrieval systems, and how to ensure the physical and virtual security of the archives[citation needed]. Additionally, considerations about long-term storage of electronic health records are complicated by the possibility that the records might one day be used longitudinally and integrated across sites of care. Records have the potential to be created, used, edited, and viewed by multiple independent entities. These entities include, but are not limited to, primary care physicians, hospitals, insurance companies, and patients. Mandl et al. have noted that "choices about the structure and ownership of these records will have profound impact on the accessibility and privacy of patient information." The required length of storage of an individual electronic health record will depend on national and state regulations, which are subject to change over time. Ruotsalainen and Manning have found that the typical preservation time of patient data varies between 20 and 100 years. In one example of how an EHR archive might function, their research "describes a co-operative trusted notary archive (TNA) which receives health data from different EHR-systems, stores data together with associated meta-information for long periods and distributes EHR-data objects. TNA can store objects in XML-format and prove the integrity of stored data with the help of event records, timestamps and archive e-signatures." In addition to the TNA archive described by Ruotsalainen and Manning, other combinations of EHR systems and archive systems are possible. Again, overall requirements for the design and security of the system and its archive will vary and must function under ethical and legal principles specific to the time and place[citation needed]. While it is currently unknown precisely how long EHRs will be preserved, it is certain that length of time will exceed the average shelf-life of paper records. The evolution of technology is such that the programs and systems used to input information will likely not be available to a user who desires to examine archived data. One proposed solution to the challenge of long-term accessibility and usability of data by future systems is to standardize information fields in a time-invariant way, such as with XML language. Olhede and Peterson report that "the basic XML-format has undergone preliminary testing in Europe by a Spri project and been found suitable for EU purposes. Spri has advised the Swedish National Board of Health and Welfare and the Swedish National Archive to issue directives concerning the use of XML as the archive-format for EHCR (Electronic Health Care Record) information."
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Synchronization of records When care is provided at two different facilities, it may be difficult to update records at both locations in a co-ordinated fashion. Two models have been used to satisfy this problem: a centralized data server solution, and a peer-to-peer file synchronization program (as has been developed for other peer-to-peer networks). Synchronization programs for distributed storage models, however, are only useful once record standardization has occurred. Merging of already existing public healthcare databases is a common software challenge. The ability of electronic health record systems to provide this function is a key benefit and can improve healthcare delivery.[45]
eHealth and teleradiology The sharing of patient information between health care organizations and IT systems is changing from a "point to point" model to a "many to many" one. The European Commission is supporting moves to facilitate cross-border interoperability of e-health systems and to remove potential legal hurdles, as in the project www.epsos.eu/. To allow for global shared workflow, studies will be locked when they are being read and then unlocked and updated once reading is complete. Radiologists will be able to serve multiple health care facilities and read and report across large geographical areas, thus balancing workloads. The biggest challenges will relate to interoperability and legal clarity. In some countries it is almost forbidden to practice teleradiology. The variety of languages spoken is a problem and multilingual reporting templates for all anatomical regions are not yet available. However, the market for e-health and teleradiology is evolving more rapidly than any laws or regulations.[46]
European Union: Directive 2011/24/EU on patients' rights in cross-border healthcare The European Commission wants to boost the digital economy by enabling all Europeans to have access to online medical records anywhere in Europe by 2020. With the newly enacted Directive 2011/24/EU on patients' rights in cross-border healthcare due for implementation by 2013, it is inevitable that a centralised European health record system will become a reality even before 2020. However, the concept of a centralised supranational central server raises concern about storing electronic medical records in a central location. The privacy threat posed by a supranational network is a key concern. Cross-border and Interoperable electronic health record systems make confidential data more easily and rapidly accessible to a wider audience and increase the risk that personal data concerning health could be accidentally exposed or easily distributed to unauthorised parties by enabling greater access to a compilation of the personal data concerning health, from different sources, and throughout a lifetime.[47]
National contexts United States
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EHR adoption of all physicians in the US. Source: DesRoches et al. (2008). Fully functional EHR system (4%) Basic EHR system (13%) Bought but not implemented yet (13%) EHR purchase planned in 2 years (22%) No EHR system (48%) Usage Even though EMR systems with a computerized provider order entry (CPOE) have existed for more than 30 years, fewer than 10 percent of hospitals as of 2006 had a fully integrated system.[48] In a 2008 survey by DesRoches et al. of 4484 physicians (62% response rate), 83% of all physicians, 80% of primary care physicians, and 86% of non-primary care physicians had no EHRs. "Among the 83% of respondents who did not have electronic health records, 16%" had bought, but not implemented an EHR system yet. The 2009 National Ambulatory Medical Care Survey of 5200 physicians (70% response rate) by the National Center for Health Statistics showed that 51.7% of office-based physicians did not use any EMR/EHR system. In the United States, the CDC reported that the EMR adoption rate had steadily risen to 48.3 percent at the end of 2009.[49] This is an increase over 2008, when only 38.4% of office-based physicians reported using fully or partially electronic medical record systems (EMR) in 2008.[50] However, the same study found that only 20.4% of all physicians reported using a system described as minimally functional and including the following features: orders for prescriptions, orders for tests, viewing laboratory or imaging results, and clinical progress notes. As of 2012, 72 percent of office physicians are using basic electronic medical records. The healthcare industry spends only 2% of gross revenues on HIT, which is low compared to other information intensive industries such as finance, which spend upwards of 10%. The usage of electronic medical records can vary depending on who the user is and how they are using it. Electronic medical records can help improve the quality of medical care given to patients. Many doctors and office-based physicians refuse to get rid of the traditional paper records. Harvard University has conducted an experiment in which they tested how doctors and nurses use electronic medical records to keep their patients' information up to date. The studies found that electronic medical records were very useful; a doctor or a nurse was able to find a patient's information fast and easy just by typing their name; even if it was misspelled. The usage of electronic medical records increases in some work places due to the ease of use of the system; whereas the president of the Canadian Family Practice Nurses Association says that using electronic medical records can be time consuming, and it isn't very helpful due to the complexity of the system. Beth Israel Deaconess Medical Center reported that doctors and nurses prefer to use a much more friendly user software due to the difficulty and time it takes for a medical staff to input the information as well as to find a patients information. A study was done and the amount of information that was recorded in the EMRs was recorded; about 44% of the patients information was recorded in the EMRs. This
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Electronic medical record shows that EMRs are not very efficient most of the time. The cost of implementing an EMR system for smaller practices has also been criticized.Wikipedia:Manual of Style/Words to watch#Unsupported attributions Despite this, tighter regulations regarding meaningful use criteria have resulted in more physicians adopting EMR systems. Software, hardware and other services for EMR system implementation are provided for cost by various companies, including Dell. Open source EMR systems exist, but have not seen widespread adoption of open-source EMR system software. Beyond financial concerns there are a number of legal and ethical dilemmas created by increasing EMR use. Legal status Electronic medical records, like other medical records, must be kept in unaltered form and authenticated by the creator.[51] Under data protection legislation, responsibility for patient records (irrespective of the form they are kept in) is always on the creator and custodian of the record, usually a health care practice or facility. The physical medical records are the property of the medical provider (or facility) that prepares them. This includes films and tracings from diagnostic imaging procedures such as X-ray, CT, PET, MRI, ultrasound, etc. The patient, however, according to HIPAA, has a right to view the originals, and to obtain copies under law.[52] The Health Information Technology for Economic and Clinical Health Act (Pub.L. 111–5 [53],§2.A.III & B.4) (a part of the 2009 stimulus package) set meaningful use of interoperable EHR adoption in the health care system as a critical national goal and incentivized EHR adoption. The "goal is not adoption alone but 'meaningful use' of EHRs — that is, their use by providers to achieve significant improvements in care." Title IV of the act promises maximum incentive payments for Medicaid to those who adopt and use "certified EHRs" of $63,750 over 6 years beginning in 2011. Eligible professionals must begin receiving payments by 2016 to qualify for the program. For Medicare the maximum payments are $44,000 over 5 years. Doctors who do not adopt an EHR by 2015 will be penalized 1% of Medicare payments, increasing to 3% over 3 years. In order to receive the EHR stimulus money, the HITECH Act requires doctors to show "meaningful use" of an EHR system. As of June 2010, there are no penalty provisions for Medicaid. Health information exchange (HIE) has emerged as a core capability for hospitals and physicians to achieve "meaningful use" and receive stimulus funding. Healthcare vendors are pushing HIE as a way to allow EHR systems to pull disparate data and function on a more interoperable level[citation needed]. Starting in 2015, hospitals and doctors will be subject to financial penalties under Medicare if they are not using electronic health records. Goals And Objectives • Improve care quality, safety, efficiency, and reduce health disparities Quality and safety measurement Clinical decision support (automated advice) for providers Patient registries (e.g., “a directory of patients with diabetes”) • Improve care coordination • Engage patients and families in their care • Improve population and public health Electronic laboratory reporting for reportable conditions (hospitals) Immunization reporting to immunization registries Syndromic surveillance (health event awareness) • Ensure adequate privacy and security protections
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Electronic medical record Quality Studies call into question whether, in real life, EMRs improve the quality of care. 2009 produced several articles raising doubts about EMR benefits. A major concern is the reduction of physician-patient interaction due to formatting constraints. For example, some doctors have reported that the use of check-boxes has led to fewer open-ended questions.[53] Meaningful use The main components of Meaningful Use are: • The use of a certified EHR in a meaningful manner, such as e-prescribing. • The use of certified EHR technology for electronic exchange of health information to improve quality of health care. • The use of certified EHR technology to submit clinical quality and other measures. In other words, providers need to show they're using certified EHR technology in ways that can be measured significantly in quality and in quantity. The meaningful use of EHRs intended by the US government incentives is categorized as follows: • Improve care coordination • • • •
Reduce healthcare disparities Engage patients and their families Improve population and public health Ensure adequate privacy and security
The Obama Administration's Health IT program intends to use federal investments to stimulate the market of electronic health records: • Incentives: to providers who use IT • Strict and open standards: To ensure users and sellers of EHRs work towards the same goal • Certification of software: To provide assurance that the EHRs meet basic quality, safety, and efficiency standards The detailed definition of "meaningful use" is to be rolled out in 3 stages over a period of time until 2015. Details of each stage are hotly debated by various groups. Only stage 1 has been defined while the remaining stages will evolve over time. Meaningful use Stage 1 The first steps in achieving meaningful use are to have a certified electronic health record (EHR) and to be able to demonstrate that it is being used to meet the requirements. Stage 1 contains 25 objectives/measures for Eligible Providers (EPs) and 24 objectives/measures for eligible hospitals. The objectives/measures have been divided into a core set and menu set. EPs and eligible hospitals must meet all objectives/measures in the core set (15 for EPs and 14 for eligible hospitals). EPs must meet 5 of the 10 menu-set items during Stage 1, one of which must be a public health objective. Full list of the Core Requirements and a full list of the Menu Requirements. Core Requirements: 1. 2. 3. 4. 5.
Use computerized order entry for medication orders. Implement drug-drug, drug-allergy checks. Generate and transmit permissible prescriptions electronically. Record demographics. Maintain an up-to-date problem list of current and active diagnoses.
6. Maintain active medication list. 7. Maintain active medication allergy list. 8. Record and chart changes in vital signs.
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Electronic medical record 9. Record smoking status for patients 13 years old or older. 10. Implement one clinical decision support rule. 11. Report ambulatory quality measures to CMS or the States. 12. Provide patients with an electronic copy of their health information upon request. 13. Provide clinical summaries to patients for each office visit. 14. Capability to exchange key clinical information electronically among providers and patient authorized entities. 15. Protect electronic health information (privacy & security) Menu Requirements: 1. Implement drug-formulary checks. 2. Incorporate clinical lab-test results into certified EHR as structured data. 3. Generate lists of patients by specific conditions to use for quality improvement, reduction of disparities, research, and outreach. 4. Send reminders to patients per patient preference for preventive/ follow-up care 5. Provide patients with timely electronic access to their health information (including lab results, problem list, medication lists, allergies) 6. Use certified EHR to identify patient-specific education resources and provide to patient if appropriate. 7. Perform medication reconciliation as relevant 8. Provide summary care record for transitions in care or referrals. 9. Capability to submit electronic data to immunization registries and actual submission. 10. Capability to provide electronic syndromic surveillance data to public health agencies and actual transmission. To receive federal incentive money, CMS requires participants in the Medicare EHR Incentive Program to "attest" that during a 90-day reporting period, they used a certified EHR and met Stage 1 criteria for meaningful use objectives and clinical quality measures. For the Medicaid EHR Incentive Program, providers follow a similar process using their state's attestation system.[54] Meaningful use Stage 2 The government released its final ruling on achieving Stage 2 of meaningful use in August 2012. Eligible providers will need to meet 17 of 20 core objectives in Stage 2, and fulfill three out of six menu objectives. The required percentage of patient encounters that meet each objective has generally increased over the Stage 1 objectives. While Stage 2 focuses more on information exchange and patient engagement, many large EHR systems have this type of functionality built into their software, making it easier to achieve compliance. Also, for those eligible providers who have successfully attested to Stage 1, meeting Stage 2 should not be as difficult, as it builds incrementally on the requirements for the first stage.[55][56] Barriers to adoption Costs The steepWikipedia:Please clarify price of EMR and provider uncertainty regarding the value they will derive from adoption in the form of return on investment have a significant influence on EMR adoption.[] In a project initiated by the Office of the National Coordinator for Health Information (ONC), surveyors found that hospital administrators and physicians who had adopted EMR noted that any gains in efficiency were offset by reduced productivity as the technology was implemented, as well as the need to increase information technology staff to maintain the system. The U.S. Congressional Budget Office concluded that the cost savings may occur only in large integrated institutions like Kaiser Permanente, and not in small physician offices. They challenged the Rand Corp. estimates of savings. "Office-based physicians in particular may see no benefit if they purchase such a product—and may even suffer financial harm. Even though the use of health IT could generate cost savings for the health system at large that might offset the EMR's cost, many physicians might not be able to reduce their office expenses or increase their revenue
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Electronic medical record sufficiently to pay for it. For example. the use of health IT could reduce the number of duplicated diagnostic tests. However, that improvement in efficiency would be unlikely to increase the income of many physicians."[13] "Given the ease at which information can be exchanged between health IT systems, patients whose physicians use them may feel that their privacy is more at risk than if paper records were used." Doubts have been raised about cost saving from EMRs by researchers at Harvard University, the Wharton School of the University of Pennsylvania, Stanford University, and others. Start-up costs In a survey by DesRoches et al. (2008), 66% of physicians without EHRs cited capital costs as a barrier to adoption, while 50% were uncertain about the investment. Around 56% of physicians without EHRs stated that financial incentives to purchase and/or use EHRs would facilitate adoption. In 2002, initial costs were estimated to be $50,000–70,000 per physician in a 3-physician practice. Since then, costs have decreased with increasing adoption. A 2011 survey estimated a cost of $32,000 per physician in a 5-physician practice during the first 60 days of implementation.[57] One case study by Miller et al. (2005) of 14 small primary-care practices found that the average practice paid for the initial and ongoing costs within 2.5 years. A 2003 cost-benefit analysis found that using EMRs for 5 years created a net benefit of $86,000 per provider. Some physicians are skeptical of the positive claims and believe the data is skewed by vendors and others with an interest in EHR implementation.[citation needed] Brigham and Women's Hospital in Boston, Massachusetts, estimated it achieved net savings of $5 million to $10 million per year following installation of a computerized physician order entry system that reduced serious medication errors by 55 percent. Another large hospital generated about $8.6 million in annual savings by replacing paper medical charts with EHRs for outpatients and about $2.8 million annually by establishing electronic access to laboratory results and reports. Maintenance costs Maintenance costs can be high. Miller et al. found the average estimated maintenance cost was $8500 per FTE health-care provider per year. Furthermore, software technology advances at a rapid pace. Most software systems require frequent updates, often at a significant ongoing cost. Some types of software and operating systems require full-scale re-implementation periodically, which disrupts not only the budget but also workflow. Costs for upgrades and associated regression testing can be particularly high where the applications are governed by FDA regulations (e.g. Clinical Laboratory systems). Physicians desire modular upgrades and ability to continually customize, without large-scale reimplementation[citation needed]. Training costs Training of employees to use an EHR system is costly, just as for training in the use of any other hospital system. New employees, permanent or temporary, will also require training as they are hired.[58] In the United States, a substantial majority of healthcare providers train at a VA facility sometime during their career. With the widespread adoption of the Veterans Health Information Systems and Technology Architecture (VistA) electronic health record system at all VA facilities, few recently-trained medical professionals will be inexperienced in electronic health record systems. Older practitioners who are less experienced in the use of electronic health record systems will retire over time.[citation needed]
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Software quality and usability deficiencies The Healthcare Information and Management Systems Society (HIMSS), a very large U.S. health care IT industry trade group, observed that EMR adoption rates "have been slower than expected in the United States, especially in comparison to other industry sectors and other developed countries. A key reason, aside from initial costs and lost productivity during EMR implementation, is lack of efficiency and usability of EMRs currently available." The U.S. National Institute of Standards and Technology of the Department of Commerce studied usability in 2011 and lists a number of specific issues that have been reported by health care workers. The U.S. military's EMR "AHLTA" was reported to have significant usability issues. Lack of semantic interoperability In the United States, there are no standards for semantic interoperability of health care data; there are only syntactic standards. This means that while data may be packaged in a standard format (using the pipe notation of HL7, or the bracket notation of XML), it lacks definition, or linkage to a common shared dictionary. The addition of layers of complex information models (such as the HL7 v3 RIM) does not resolve this fundamental issue. Implementations In the United States, the Department of Veterans Affairs (VA) has the largest enterprise-wide health information system that includes an electronic medical record, known as the Veterans Health Information Systems and Technology Architecture (VistA). A key component in VistA is their VistA imaging System which provides a comprehensive multimedia data from many specialties, including cardiology, radiology and orthopedics. A graphical user interface known as the Computerized Patient Record System (CPRS) allows health care providers to review and update a patient's electronic medical record at any of the VA's over 1,000 healthcare facilities. CPRS includes the ability to place orders, including medications, special procedures, X-rays, patient care nursing orders, diets, and laboratory tests.[citation needed] The 2003 National Defense Authorization Act (NDAA) ensured that the VA and DoD would work together to establish a bidirectional exchange of reference quality medical images. Initially, demonstrations were only worked in El Paso, Texas, but capabilities have been expanded to six different locations of VA and DoD facilities. These facilities include VA polytrauma centers in Tampa and Richmond, Denver, North Chicago, Biloxi, and the National Capitol Area medical facilities. Radiological images such as CT scans, MRIs, and x-rays are being shared using the BHIE. Goals of the VA and DoD in the near future are to use several image sharing solutions (VistA Imaging and DoD Picture Archiving & Communications System (PACS) solutions).[59] Clinical Data Repository/Health Data Repository (CDHR)is a database that allows for sharing of patient records, especially allergy and pharmaceutical information, between the Department of Veteran Affairs (VA) and the Department of Defense (DoD) in the United States. The program shares data by translating the various vocabularies of the information being transmitted, allowing all of the VA facilities to access and interpret the patient records.[60] The Laboratory Data Sharing and Interoperability (LDSI) application is a new program being implemented to allow sharing at certain sites between the VA and DoD of "chemistry and hematology laboratory tests." Unlike the CHDR, the LDSI is currently limited in its scope.[61]
Electronic health records flow chart
One attribute for the start of implementing EHRs in the States is the development of the Nationwide Health Information Network which is a work in progress and still being developed. This started with the North Carolina Healthcare Information and Communication Alliance founded in 1994 and who received funding from Department of Health and Human Services.
Electronic medical record The Department of Veterans Affairs and Kaiser Permanente has a pilot program to share health records between their systems VistA and HealthConnect, respectively. This software called 'CONNECT' uses Nationwide Health Information Network standards and governance to make sure that health information exchanges are compatible with other exchanges being set up throughout the country. CONNECT is an open source software solution that supports electronic health information exchange.[62] The CONNECT initiative is a Federal Health Architecture project that was conceived in 2007 and initially built by 20 various federal agencies and now comprises more than 500 organizations including federal agencies, states, healthcare providers, insurers, and health IT vendors.[63] The US Indian Health Service uses an EHR similar to Vista called RPMS. VistA Imaging is also being used to integrate images and co-ordinate PACS into the EHR system. In Alaska, use of the EHR by the Kodiak Area Native Association has improved screening services and helped the organization reach all 21 clinical performance measures defined by the Indian Health Service as required by the Government Performance and Results Act.
UK In 2005 the National Health Service (NHS) in the United Kingdom began deployment of EHR systems. The goal was to have all patients with a centralized electronic health record by 2010. While many hospitals acquired electronic patient records systems in this process, there was no national healthcare information exchange. [64][65][66] Ultimately, the program was dismantled after a cost to the UK taxpayer was over $24 Billion (12 Billion GPB), and is considered one of the most expensive healthcare IT failures . The UK Government is now considering open-source healthcare platform from the United States Veterans Affairs following on the success of the VistA_EHR deployment in Jordan [citation needed]. GP2GP is an NHS Connecting for Health project in the United Kingdom. It enables GPs to transfer a patient's electronic medical record to another practice when the patient moves onto the list.[67]
Australia Australia is dedicated to the development of a lifetime electronic health record for all its citizens. PCEHR - the Personally Controlled Electronic Health Record - is the major national EHR initiative in Australia, being delivered through territory, state, and federal governments. This electronic health record was initially deployed in July 2012, and is under active development and extension.[68] MediConnect is an earlier program that provides an electronic medication record to keep track of patient prescriptions and provide stakeholders with drug alerts to avoid errors in prescribing. Within Australia, there is a not-for-profit organisation called Standards Australia, which has created a electronic health website relating to information not only about Australia and what is currently going on about EHRs but also globally. There is a large number of key stakeholders that contribute to the process of integrating EHRs within Australia,they range from each States Departments of Health to Universities around Australia and National E-Health Transition Authority [70] to name a few.
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Austria In December 2012 Austria introduced an Electronic Health Records Act (EHR-Act).[69] These provisions are the legal foundation for a national EHR system based upon a substantial public interest according to Art 8(4) of the Data Protection Directive 95/46/EC.[70] In compliance to the Data Protection Directive (DPD) national electronic health records could be based upon explicit consent (Art 8(2)(a) DPD), the necessity for healthcare purposes (Art8(3) DPD) or substantial public interests (Art 8(4) DPD). The Austrian EHR-Act pursues an opt-out approach in order to harmonize the interests of public health and privacy in the best possible manner.
Structure and basic components of the Austrian EHR (ELGA)
The 4th Part of the Austrian Health Telematics Act 2012 (HTA 2012) - these are the EHR provisions - are one of the most detailed data protection rules within Austrian legislation. Numerous safeguards according to Art 8(4) DPD guarantee a high level of data protection. For example: • personal health data needs to be encrypted prior to transmission (§ 6 HTA 2012), or • strict rules on data usage allow personal health data only to be used for treatment purposes or exercising patients' rights (§ 14 HTA 2012), or • patients may declare their right to opt out from the national EHR at any time (§ 15 HTA 2012), or • the implementation of an EHR-Ombudsman, to support the patients in exercising their rights (§ 17 HTA 2012), or • the Access Control Center provides EHR-participants with full control over their data (§ 21 HTA 2012), or • judicial penalties for privacy breaches (Art 7 of the EHR-Act).
Canada The Canadian province of Alberta started a large-scale operational EHR system project in 2005 called Alberta Netcare, which is expected to encompass all of Alberta by 2008. The College of Dental Surgeons of British Columbia [73] has compiled the Dental Records Management document which lays out the requirements, for records for their industry within the province of British Columbia.
[74]
Jordan In 2009, the Jordanian Government made a strategic decision to address quality and cost challenges in their healthcare system by investing in an effective, national e-health infrastructure. Following a period of detailed consultation and investigation, Jordan adopted the electronic health record system of the US Veterans Health Administration VistA_EHR because it was a proven, national-scale enterprise system capable of scaling to hundreds of hospitals and millions of patients. [75] In 2010 three of the country's largest hospitals went live with VistA_EHR. It is anticipated that all further hospital deployments based on this 'gold' version will require less than 20% effort and cost of the original hospitals, enabling rapid national coverage. The implementation of VistA EHR was estimated at 75% less cost than proprietary products, with the greatest savings related to reduced costs of configuration, customization, implementation and support. When completed, Jordan will be the largest country in the world with a single, comprehensive, national electronic health care delivery network to care for the country’s entire population in a single electronic network of over 850 hospitals and clinics.
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Denmark The five regions each use their own setup of electronic health record systems. However, all patient data is registered in the national e-journal [76].
Estonia Estonia is the first country in the world that has implemented a nationwide EHR system, registering virtually all residents' medical history from birth to death.
Netherlands The vast majority of GP's [71] and all pharmacies and hospitals use EHR's. In hospitals computerized ordermanagment and medical imaging systems (PACS) are widely accepted. Whereas healthcare institutions continue to upgrade their EHR's functionalties, the national infrastructure is still far from being generally accepted. In 2012 the national EHR restarted under the joined ownership of GP's, pharmacies and hospitals. A major change is that as of January 2013 patients have to give their explicit permission that their data may be exchanged over the national infrastrucure. The national EHR is a virtual EHR and basically is a reference server which knows in which local EHR what kind of patientdata is stored. EDIFACT still is the most common way to exchange patient information electroniaclly between hospitals and GP's.
UAE Abu Dhabi is leading the way in using national EHR data as a live longitudinal cohort in the assessment of risk of cardiovascular disease.
Saudi Arabia Arab Health Awards 2010 recognizes Saudi Arabia National Guard Health Affairs for greatest advancement in EHR development.
In veterinary medicine In UK veterinary practice, the replacement of paper recording systems with electronic methods of storing animal patient information escalated from the 1980s and the majority of clinics now use electronic medical records. In a sample of 129 veterinary practices, 89% used a Practice Management System (PMS) for data recording.[72] There are more than ten PMS providers currently in the UK. Collecting data directly from PMSs for epidemiological analysis abolishes the need for veterinarians to manually submit individual reports per animal visit and therefore increases the reporting rate.[73] Veterinary electronic medical record data are being used to investigate antimicrobial efficacy; risk factors for canine cancer; and inherited diseases in dogs and cats, in the small animal disease surveillance project 'VetCOMPASS' [80] (Veterinary Companion Animal Surveillance System) at the Royal Veterinary College, London, in collaboration with the University of Sydney and RxWorks [81] (the VetCOMPASS project was formerly known as VEctAR).[74]
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The Future of Electronic Health Records – Personally Controlled Electronic Health Records A Personally Controlled Electronic Health Record (PCEHR) is a system that proposes to store admission or event summaries in an electronic format over a large network accessible by doctors, nurses, GPs and chemists without the need for written scripts or requesting medical files from another hospital. The system proposes to record and store any health information provided by a health care professional that has agreed to be a part of the system. This allows the storage and retrieval of a lifetimes worth of clinical and demographic information of a patient that can be viewed as event summaries and reports with the appropriate authorization
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Electronic medical record [26] FDA memo. H-IT Safety Issues, table 4, page 3, Appendix B, p. 7-8 (with examples), and p. 5, summary. Memo obtained and released by Fred Schulte and Emma Schwartz at the Huffington Post Investigative Fund, now part of the Center for Public Integrity, in an Aug. 3, 2010 article FDA, Obama digital medical records team at odds over safety oversight (http:/ / www. iwatchnews. org/ 2010/ 08/ 03/ 7096/ fda-obama-digital-medical-records-team-odds-over-safety-oversight), memo itself (http:/ / www. scribd. com/ huffpostfund/ d/ 33754943-Internal-FDA-Report-on-Adverse-Events-Involving-Health-Information-Technology) [27] Goodman KW, Berner ES, Dente MA, et al. Challenges in ethics, safety, best practices, and oversight regarding HIT vendors, their customers, and patients: a report of an AMIA special task force. J Am Med Inform Assoc (2010). (http:/ / jamia. bmj. com/ site/ icons/ amiajnl8946. pdf) [28] Rowe JC. Doctors Go Digital. The New Atlantis (2011). (http:/ / www. thenewatlantis. com/ publications/ doctors-go-digital) [29] US Code of Federal Regulations, Title45, Volume 1 (Revised October 1, 2005): of Individually Identifiable Health Information (45CFR164.501) (http:/ / frwebgate. access. gpo. gov/ cgi-bin/ get-cfr. cgi?YEAR=current& TITLE=45& PART=164& SECTION=501& SUBPART=& TYPE=TEXTPrivacy) Retrieved July 30, 2006 [30] Summary of the HIPAA Privacy Rule (http:/ / www. hhs. gov/ ocr/ privacy/ hipaa/ understanding/ summary/ index. html) [31] Privacy Rights Clearinghouse's Chronology of Data Security Breaches (https:/ / www. privacyrights. org/ data-breach/ new) [32] European Parliament and Council (24 October 1995): EU Directive 95/46/EC - The Data Protection Directive (http:/ / www. dataprivacy. ie/ viewdoc. asp?m=& fn=/ documents/ LEGAL/ 6aii. htm) Retrieved July 30, 2006 [33] Medical Manager History (http:/ / docs. mirrormed. org/ index. php/ Medical_Manager_History) [34] "Can Technology Get You Sued?" (http:/ / www. physicianspractice. com/ risk-management/ content/ article/ 1462168/ 2042414) Shelly K. Schwartz, Physicians Practice, March 2012. [35] European Patient Smart Open Services Work Plan: epSOS: Legal and Regulatory Issues (http:/ / www. epsos. eu/ about-epsos/ work-plan-new. html#c501) Retrieved May 4, 2008 [36] Privacy Rights Clearinghouse's Chronology of Data Security Breaches involving Medical Information (https:/ / www. privacyrights. org/ data-breach/ new) [37] Kierkegaard, P. (2012) Medical data breaches: Notification delayed is notification denied, Computer Law & Security Report , 28 (2), p.163–183. (http:/ / www. sciencedirect. com/ science/ article/ pii/ S0267364912000209) [38] HIPAA Basics: Medical Privacy in the Electronic Age from the Privacy Rights Clearinghouse www.privacyrights.org (https:/ / www. privacyrights. org/ fs/ fs8a-hipaa. htm) [39] DEPARTMENT OF HEALTH AND HUMAN SERVICES Breach Notification for Unsecured Protected Health Information (http:/ / www. gpo. gov/ fdsys/ pkg/ FR-2009-08-24/ pdf/ E9-20169. pdf) [40] Clayton L. Reynolds MD, FACP, FACPE (March 2006): Paper on Concept Processing (http:/ / www. infor-med. com/ downloads/ why_praxis_downloads/ Charting_Bass_Ackward. pdf) Retrieved July 27, 2006 [41] Maekawa Y, Majima Y.; "Issues to be improved after introduction of a non-customized Electronic Medical Record system (EMR) in a Private General Hospital and efforts toward improvement"; Studies in Health Technology and Informatics 2006 [42] Tüttelmann F, Luetjens CM, Nieschlag E.; "Optimising workflow in andrology: a new electronic patient record and database"; Asian Journal of Andrology March 2006 [43] The Digital Office, September 2007, vol 2, no.9. HIMSS [44] Gina Rollins."The Perils of Customization." Journal of AHIMA 77, no.6 (2006):24-28. [45] The Master Child Index consolidated 4,610,585 records that were contained in both databases into 2,977,290 records through a match and merge system. [46] Pohjonen H. Images can now cross borders, but what about the legislation? (http:/ / www. diagnosticimaging. com/ imaging-trends-advances/ ultrasound-source/ archive/ article/ 113619/ 1584986) Diagnostic Imaging Europe. June/July 2010;26(4):16. [47] Patrick Kierkegaard (2011) Electronic health record: Wiring Europe's healthcare (http:/ / www. sciencedirect. com/ science/ article/ pii/ S0267364911001257), Computer Law & Security Review, Volume 27, Issue 5, September 2011, Pages 503-515, ISSN 0267-3649, 10.1016/j.clsr.2011.07.013. Retrieved Dec 15, 2011 [48] Smaltz, Detlev and Eta Berner. The Executive's Guide to Electronic Health Records.' (2007, Health Administration Press) p.03 [49] Are More Doctors Adopting EHRs? (http:/ / www. nuesoft. com/ blog/ are-more-doctors-adopting-ehrs/ ) Retrieved March 31, 2011 [50] National Center for Health : United States, 2008] Retrieved December 15, 2009 [51] National Archives and Records Administration (NARA): Long-Term Usability of Optical Media (http:/ / palimpsest. stanford. edu/ bytopic/ electronic-records/ electronic-storage-media/ critiss. html) Retrieved July 30, 2006 [52] Medical Board of California: Medical Records - Frequently Asked Questions (http:/ / medbd. ca. gov/ consumer/ complaint_info_questions_records. html) Retrieved July 30, 2006 [53] Cohen GR, Grossman JM, O'Malley AS (2010). "Electronic Medical Records and Communication with Patients and Other Clinicians: Are We Talking Less?". Center for Studying Health System Change, Issue Brief No. 131 ( full text (http:/ / www. hschange. com/ CONTENT/ 1125/ #ib1)) [54] Torrieri, Marisa "Dealing with Meaningful Use Attestation Aggravation" (http:/ / www. physicianspractice. com/ meaningful-use/ content/ article/ 1462168/ 2009082). Physicians Practice. January 2012. [55] "Meaningful Use: Stage 2 Regulations Overview" (http:/ / www. physicianspractice. com/ meaningful-use/ content/ article/ 1462168/ 2098253) Robert Anthony, CMS, August 30, 2012.
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Electronic medical record [56] "EHR Incentive Program: A Progress Report" (http:/ / www. physicianspractice. com/ meaningful-use/ content/ article/ 1462168/ 2099001) Marisa Torrieri, Physicians Practice, September 2012. [57] cited in [58] Parish, Colin (March 20, 2006). Edging towards a brave new IT world. Nursing Standard 27:15-16 [59] (http:/ / web. archive. org/ web/ 20091024042950/ http:/ / www1. va. gov/ vadodhealthitsharing/ page. cfm?pg=23) Retrieved March 4, 2010 [60] (http:/ / web. archive. org/ web/ 20091024045131/ http:/ / www1. va. gov/ vadodhealthitsharing/ page. cfm?pg=9) Retrieved March 4, 2010 [61] (http:/ / web. archive. org/ web/ 20091024044210/ http:/ / www1. va. gov/ vadodhealthitsharing/ page. cfm?pg=4) Retrieved March 4, 2010 [62] Retrieved March 4, 2010 (http:/ / www. connectopensource. org/ about/ what-is-CONNECT) [63] Retrieved March 4, 2010 (http:/ / connectopensource. osuosl. org/ sites/ connectopensource. osuosl. org/ files/ CONNECTOverview. pdf) [64] Greenhalgh T, Stramer K, Bratan T, Byrne E, Russell J, Potts HWW (2010). Adoption and non-adoption of a shared electronic summary care record in England: A mixed-method case study. BMJ, 340, c3111 [65] Bewley S, Perry H, Fawdry R, Cumming G (2011). NHS IT requires the wisdom of the crowd not the marketplace. (http:/ / www. bmj. com/ content/ 343/ bmj. d5317. full/ reply#bmj_el_270562) Accessed 16 April 2012 [66] "The government today announced an acceleration of the dismantling of the National Programme for IT, following the conclusions of a new review by the Cabinet Office's Major Projects Authority (MPA)... The MPA found that the National Programme for IT has not and cannot deliver to its original intent."(sic) [67] GP2GP Website (http:/ / www. connectingforhealth. nhs. uk/ delivery/ programmes/ gp2gp) [68] http:/ / www. ehealth. gov. au www.ehealth.gov.au [69] Electronic Health Records Act (EHR-Act) (http:/ / www. ilia. ch/ wordpress/ wp-content/ uploads/ 2013/ 02/ austrian_ehr-act_ILIA. pdf) [70] Data Protection Directive 95/46/EC (http:/ / eur-lex. europa. eu/ LexUriServ/ LexUriServ. do?uri=CELEX:31995L0046:en:HTML) [71] http:/ / www. informationWeek. com/ healthcare/ electronic-medical-records/ ehr-adoption-us-remains-the-slow-poke/ 240142152, Ken Terry, Informationweek [72] Gill, M. (2007) Attitudes to clinical audit in veterinary practice, Royal Veterinary College elective project, unpublished work [73] Carruthers, H. (2009) Disease surveillance in small animal practice, In Pract, 31(7): 356–358 [74] VEctAR (Veterinary Electronic Animal Record) (2010) from http:/ / www. rvc. ac. uk/ VEctAR/
External links • Can Electronic Health Record Systems Transform Health Care? (http://www.eecs.harvard.edu/cs199r/ readings/RAND_benefits.pdf) • Health Information Technology in the United States (http://www.rwjf.org/files/publications/other/ EHRReport0609.pdf) • How to Enable Standard-Compliant Streaming of Images in Electronic Health Records (http://www.aware. com/imaging/whitepapers/wp_jpipwado.htm) a white paper by Aware Inc. (http://www.aware.com/imaging/ whitepapers.htm) • Open-Source EHR Systems for Ambulatory Care: A Market Assessment (http://www.chcf.org/topics/view. cfm?itemID=133551)(California HealthCare Foundation, January 2008) • US Department of Health and Human Services (HHS), Office of the National Coordinator for Health Information Technology (ONC) (http://www.hhs.gov/healthit/) • US Department of Health and Human Services (HHS), Agency for Healthcare Research and Quality (AHRQ), National Resource Center for Health Information Technology (http://healthit.ahrq.gov/emr) • ICMCC portal: EHR info and blogs (http://recordaccess.icmcc.org/) • Security Aspects in Electronic Personal Health Record: Data Access and Preservation (http://www. digitalpreservationeurope.eu/publications/briefs/security_aspects.pdf) - a briefing paper at Digital Preservation Europe
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Personal health record A personal health record, or PHR, is a health record where health data and information related to the care of a patient is maintained by the patient. This stands in contrast to the more widely used electronic medical record, which is operated by institutions (such as hospitals) and contains data entered by clinicians or billing data to support insurance claims. The intention of a PHR is to provide a complete and accurate summary of an individual's medical history which is accessible online. The health data on a PHR might include patient-reported outcome data, lab results, data from devices such as wireless electronic weighing scales or collected passively from a smartphone.
Definition The term “personal health record” is not new. The earliest mention of the term was in an article indexed by PubMed dated June 1978, and even earlier in 1956 reference is made to a personal health log. However, most scientific articles written about PHRs have been published since 2000. The term "PHR" has been applied to both paper-based and computerized systems; current usage usually implies an electronic application used to collect and store health data. In recent years, several formal definitions of the term have been proposed by various organizations.[1][2][3] It is important to note that PHRs are not the same as electronic health records (EHRs). The latter are software systems designed for use by health care providers. Like the data recorded in paper-based medical records, the data in EHRs are legally mandated notes on the care provided by clinicians to patients. There is no legal mandate that compels a consumer or patient to store her personal health information in a PHR. PHRs can contain a diverse range of data, including but not limited to: • • • • • • • • • • •
allergies and adverse drug reactions chronic diseases family history illnesses and hospitalizations imaging reports (e.g. X-ray) laboratory test results medications and dosing prescription record surgeries and other procedures vaccinations and Observations of Daily Living (ODLs)
There are two methods by which data can arrive in a PHR. A patient may enter it directly, either by typing into fields or uploading/transmitting data from a file or another website. The second is when the PHR is tethered to an electronic health record, which automatically updates the PHR. Not all PHRs have the same capabilities, and individual PHRs may support one or all of these methods. In addition to storing an individual's personal health information, some PHRs provide added-value services such as drug-drug interaction checking, electronic messaging between patients and providers, managing appointments, and reminders.
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PHR benefits PHRs grant patients access to a wide range of health information sources, best medical practices and health knowledge. All of an individual’s medical records are stored in one place instead of paper-based files in various doctors’ offices. Upon encountering a medical condition, a patient’s health information is only a few clicks away. Moreover, PHRs can benefit clinicians. PHRs offer patients the opportunity to submit their data to their clinicians' EHRs. This helps clinicians make better treatment decisions by providing more continuous data. PHRs have the potential to help analyze an individual’s health profile and identify health threats and improvement opportunities based on an analysis of drug interaction, current best medical practices, gaps in current medical care plans, and identification of medical errors. Patient illnesses can be tracked in conjunction with healthcare providers and early interventions can be promoted upon encountering deviation of health status. PHRs also make it easier for clinicians to care for their patients by facilitating continuous communication as opposed to episodic. Eliminating communication barriers and allowing documentation flow between patients and clinicians in a timely fashion can save time consumed by face-to-face meetings and telephone communication. Improved communication can also ease the process for patients and caregivers to ask questions, to set up appointments, to request refills and referrals, and to report problems. Additionally, in the case of an emergency a PHR can quickly provide critical information to proper diagnosis or treatment.
PHR architecture PHR architecture consists of three primary components: Data, Infrastructure and Applications. • Data refers to the information that is collected, analyzed, exchanged and stored by different information technologies. Examples include medical history, laboratory and imaging results, list of medical problems, medication history, etc. • Infrastructure is the computing platform which processes or exchanges healthcare data, such as software packages and websites. • Applications include the data exchange, transactional, analytical and content delivery capabilities of the system, such as appointment scheduling, medication renewal, patient decision support system and disease education materials. Since no particular architecture has been unanimously agreed upon as being the most effective, researching the benefits of various architectural models is a high priority. Regardless of the PHR paradigm, interoperability of PHRs with other entities should be the key component of PHR architecture. If PHRs serve only as a repository for an individual’s health information, it is unlikely that individuals who are not highly motivated will maintain their health records and find PHRs to be useful.
PHR solution types One of the principal distinguishing features of a PHR is the platform by which it is delivered. The types of platforms include: paper, electronic device, and web.
Paper-based PHRs Personal health information is recorded and stored in paper format. Printed laboratory reports, copies of clinic notes, and health histories created by the individual may be parts of a paper-based PHR. This method is low cost, reliable, and accessible without the need for a computer or any other hardware. Probably the most successful paper PHR is the hand-held pregnancy record, developed in Milton Keynes in the mid-1980s[4] and now in use throughout the United Kingdom (see the Scottish Woman-Held Maternity Record [5], the All Wales Maternity Record (Cofnod Mamolaeth Cymru Gyfan) [6] and the Perinatal Institute notes [7] ).
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Personal health record Paper-based PHRs may be difficult to locate, update, and share with others. Paper-based PHRs are subject to physical loss and damage, such as can occur during a natural disaster. Paper records can also be printed from most electronic PHRs. However, Fawdry et al. have shown that paper records are extremely flexible and do have distinct advantages over rigid electronic systems.
Electronic device-based PHRs Personal health information is recorded and stored in personal computer-based software that may have the capability to print, backup, encrypt, and import data from other sources such as a hospital laboratory. The most basic form of a PC-based PHR would be a health history created in a word-processing program. The health history created in this way can be printed, copied, and shared with anyone with a compatible word processor. PHR software can provide more sophisticated features such as data encryption, data importation, and data sharing with health care providers. Some PHR products allow the copying of health records to a mass-storage device such as a CD-ROM, DVD, smart card, or USB flash drive. PC-based PHRs are subject to physical loss and damage of the personal computer and the data that it contains. Some other methods of device solution may entail cards with embedded chips containing health information that may or may not be linked to a personal computer application or a web solution.
Web-based PHR solutions Web-based PHR solutions are essentially the same as electronic device PHR solutions, however, web-based solutions have the advantage of being easily integrated with other services. For example, some solutions allow for import of medical data from external sources. Solutions including HealthVault, PatientsLikeMe, getHealtZ [8], onpatient [9], and Careplan [10] allow for data to be shared with other applications or specific people. Mobile solutions often integrate themselves with web solutions and use the web-based solution as the platform. A large number of companies have emerged to provide consumers the opportunity to develop online PHRs. Some have been developed by non-profit organizations, while others have been developed by commercial ventures. These web-based applications allow users to directly enter their information such as diagnosis, medications, laboratory tests, immunizations and other data associated with their health. They generate records that can be displayed for review or transmitted to authorized receivers. Despite the need for PHRs and the availability of various online PHR providers, there has not been wide adoption of PHR services. In fact, Google, being among the most innovative companies in the world, recently announced discontinuation of its PHR service called Google Health starting January 12, 2012. The reason cited for shutting down Google Health was that the service did not translate from its limited usage into widespread usage in the daily health routines of millions of people.
EHRs, PHRs, patient portals and UHRs The terms electronic health records, personal health records, and patient portals are not always used correctly. The generally agreed upon definition of these terms relates mainly to the ownership of the data. Once data is in a PHR it usually owned and controlled by the patient. Most EHRs, however, are the property of the provider, although the content can be co-created by both the provider and patient. A patient has a legal right in most states to request their healthcare data and under recent USA legislation those providers using a certified EHR will be required to provide an electronic copy as well. In the UK, according to the governments's information strategy for the NHS every primary care practice in England will have to offer patients online access to their care records by 2015. Currently only 1% do so., Electronic health records and electronic medical records contain clinical data created by and for health professionals in the course of providing care. The data is about the patient but the data resides in a health care provider's system. The patient portal is typically defined as a view into the electronic medical records. In addition,
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Personal health record ancillary functions that support a health care provider's interaction with a patient are also found in those systems e.g. prescription refill requests, appointment requests, electronic case management, etc. Finally, PHRs are data that resides with the patient, in a system of the patient's choosing. This data may have been exported directly from an EMR, but the point is it now resides in a location of the patient's choosing. Access to that information is controlled entirely by the patient. A new concept being discussed is the UHR or "universal health record",[11] which would be a patient-centered and patient-controlled body of information that could be shared in a granular way with particular health care providers at the patient's discretion in support of the patient's work with health care providers. This project would enlist open source contributions and enhancements from developers, with particular emphasis on supporting patient expectations of privacy and responsible patient control of private health information (PHI). It is anticipated that effective implementation of one or more "open source" approaches to the UHR would benefit both providers and patients, including providing more cost-effective solutions to currently difficult problems including entry/verification/update of personal health data, enabling responsible patient-controlled granular release of PHI, and supporting interoperability and effective collaboration of patients and physicians across disparate EHR/PHR platforms. While PHRs can help patients keep track of their personal health information, the value of PHRs to healthcare organizations is still unclear.
PHRs and public health PHRs have the ability to benefit the public health sector by providing health monitoring, outbreak monitoring, empowerment, linking to services, and research. PHRs can give consumers the potential to play a large role in protecting and promoting the public's health.
Barriers to adoption Despite the need to centralize patient information, PHR adoption has been very low. A study was carried out in an effort to assess the functionality and utility of online PHRs. An abstraction from real-life case of a patient suffering from a thyroid condition was utilized to create various online PHRs. The outputs generated were examined for accuracy and completeness of clinical information. A team of researchers identified 19 websites offering different versions of PHRs. To evaluate the PHRs, researchers identified criteria based on their promotional advertisements. Ideally, centralized PHRs should help patients relate accurate history during clinical encounters, check for drug interactions, eliminate unnecessary duplication of laboratory tests and diagnostic studies, and serve as an information hub for patients’ health management. An analysis of web-based PHR applications showed that most websites did provide access to personal medical information, however each demonstrated limited capacity in a different way: From the 19 sites examined, four were found to be specific to certain diseases only and were therefore excluded from the study. Another four were excluded for reasons such as recurrent technical problems or connections to a specific hospital’s information system. The remaining 11 sites did not provide patients with sufficient guidance as to how they should enter personal data. Some of the sites allowed patients to select medical conditions from categorized lists which did not cover the patients’ complete health condition while others allowed free text entry. To formulate medication history, sites that required patients to choose medication from lists requested them to enter a wide range of descriptive information for each medication such as prescribed dose, administration frequency, start date, name of pharmacy that issued the medication and name of provider that prescribed the medication. With respect to laboratory tests, only two allowed patients to import results from outside sources. From these two sites, only one was functional. Not every site allowed patients to enter insurance coverage information. Majority of the sites required patients to enter date and results of diagnostic tests. Most people do not keep record of minute details of their healthcare experiences and therefore find it difficult to make use of web-based PHRs. Overall, the sites selected for evaluation offered limited functionality to the general
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Personal health record public. Low adoption of web-based PHRs can be a direct result of limitations in these applications’ data entry, validation and information display methods. PHR development should be guided by ample patient-oriented research in future.
Promotion There are instances where the use of a PHR would be beneficial to patients and may, therefore, override privacy concerns. Stage 1 of meaningful use of certified EHR systems requires that practices provide at least 50 percent of their patients with a copy of their health records upon request. While this can be accomplished through a patient portal, this function can also be part of a larger system such as Kaiser Permanente's My Health Manager—a PHR that is integrated into the health system's patient portal. By June 2012, 3.9 million Kaiser members were enrolled in this program. For the first half of 2012, members viewed 2.5 million lab results, sent 1 million e-mails to physicians, and scheduled 230,000 appointments monthly, demonstrating ease of use and convenience.[12]
Privacy and ethical concerns One of the most controversial issues for PHRs is how the technology could threaten the privacy of patient information. Network computer break-ins are becoming more common,[citation needed] thus storing medical information online can cause fear of the exposure of health information to unauthorized individuals. In addition to height, weight, blood pressure and other quantitative information about a patient's physical body, medical records can reveal very sensitive information, including fertility, surgical procedures, emotional and psychological disorders, and diseases, etc. Various threats exist to patient information confidentiality, some of which are listed below: • Accidental disclosure: During multiple electronic transfers of data to various entities, medical personnel can make innocent mistakes to cause disclosure of data. • Insider curiosity: Medical personnel may misuse their access to patient information out of curiosity or for another purpose. • Insider subordination: Medical personnel may leak out personal medical information for spite, profit, revenge, or other purposes. • Uncontrolled secondary usage: Those who are granted access to patient information solely for the purpose of supporting primary care can exploit that permission for reasons not listed in the contract, such as research. • Outsider intrusion: Former employees, network intruders, hackers, or others may access information, damage systems or disrupt operations Unlike paper-based records that require manual control, digital health records are secured by technological tools. Rindfleisch (1997) identifies three general classes of technological interventions that can improve system security: • Deterrents – These depend on the ethical behaviour of people and include controls such as alerts, reminders and education of users. Another useful form of deterrents has been Audit Trails. The system records identity, times and circumstances of users accessing information. If system users are aware of such a record keeping system, it will discourage them from taking ethically inappropriate actions • Technological obstacles – These directly control the ability of a user to access information and ensure that users only access information they need to know according to their job requirements. Examples of technological obstacles include authorization, authentication, encryption, firewalls and more. • System management precautions – This involves proactively examining the information system to ensure that known sources of vulnerability are eliminated. An example of this would be installing antivirus software in the system The extent of information security concerns surrounding PHRs extends beyond technological issues. For some ethicist,Wikipedia:Avoid weasel words each transfer of information in the treatment process must be authorized by
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Personal health record the patient even if it is for patient’s betterment. As mentioned previously, no set of clearly defined architectural requirements and information use policies is available.
References [1] Connecting for Health. (http:/ / www. connectingforhealth. org/ resources/ final_phwg_report1. pdf) The Personal Health Working Group Final Report. July 1, 2003. [2] American Health Information Management Association. (http:/ / library. ahima. org/ xpedio/ groups/ public/ documents/ ahima/ bok1_027539. hcsp?dDocName=bok1_027539) The Role of the Personal Health Record in the EHR. July 25, 2005. [3] America's Health Insurance Plans. (http:/ / www. ahip. org/ content/ default. aspx?docid=18330) What are Personal Health Records (PHRs)? December 13, 2006. [4] UK Department of Health (1993). Changing Childbirth. Part II: Survey of good communications practice in maternity services, pp25-26. London: HMSO. ISBN 0-11-321623-8 [5] http:/ / www. healthcareimprovementscotland. org/ programmes/ reproductive,_maternal__child/ woman_held_maternity_record. aspx [6] http:/ / www. awcpp. org. uk/ 10460. file. dld [7] http:/ / www. perinatal. nhs. uk/ notes. htm [8] https:/ / www. gethealtz. com [9] https:/ / onpatient. com [10] http:/ / www. careplan. co [11] Moving Toward an Open Standard, Universal Health Record (http:/ / www. smart-publications. com/ health_commentaries/ Universal_Health_Record. php), by John Morgenthaler [12] "Personal Health Record Usage and Medical Practices" (http:/ / www. physicianspractice. com/ personal-health-records/ personal-health-record-usage-and-medical-practices) Robert Redling, Physicians Practice, October 2012.
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Personal health record 106. Zeng-Treitler Q, Kim H, Goryachev S, Keselman A, Slaughter L, Smith CA (2007). "Text characteristics of clinical reports and their implications for the readability of personal health records" (http://booksonline.iospress. nl/Extern/EnterMedLine.aspx?ISSN=0926-9630&Volume=129&SPage=1117). Stud Health Technol Inform. 129 (Pt 2): 1117–21. PMID 17911889 (http://www.ncbi.nlm.nih.gov/pubmed/17911889). Conference Proceedings 1. Angst CM, Agarwal, R, Downing, J. An empirical examination of the importance of defining the PHR for research and for practice,” Proceedings of the 41st Annual Hawaii International Conference on System Sciences. Jan 2008. 2. Cimino JJ, Li J, Mendoca EA, Sengupta S, Patel VL, Kuhniruk AW (2000). "An evaluation of patient access to their electronic medical records via the World Wide Web" (http://www.ncbi.nlm.nih.gov/pmc/articles/ PMC2244037/). AMIA Annu Symp Proc.: 151–155. PMC 2244037 (http://www.ncbi.nlm.nih.gov/pmc/ articles/PMC2244037). PMID 11079863 (http://www.ncbi.nlm.nih.gov/pubmed/11079863). 3. Dorr D, Bonner LM, Cohen AN et al. (2007). "Informatics Systems to Promote Improved Care for Chronic Illness: A Literature Review" (http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2213468/). J Am Med Inform Assoc. 14 (2): 156–63. doi: 10.1197/jamia.M2255 (http://dx.doi.org/10.1197/jamia.M2255). PMC 2213468 (http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2213468). PMID 17213491 (http://www.ncbi.nlm.nih. gov/pubmed/17213491). 4. Iakovidis I. From electronic medical record to personal health records: present situation and trends in European Union in the area of electronic healthcare records. Medinfo. Sep 1998. 9(1 suppl); 18-22. 5. Ross S, Lin CT (2003). "A Randomized Controlled Trial of a Patient Accessible Medical Record". AMIA Annu Symp Proc. 2003: 990. PMC 1480093 (http://www.ncbi.nlm.nih.gov/pmc/articles/PMC1480093). PMID 14728493 (http://www.ncbi.nlm.nih.gov/pubmed/14728493). 6. Stroetmann, KA; Pieper, M; Stroetmann, VN (Nov 2003), "Understanding patients: participatory approaches for the user evaluation of vital data presentation", Proceedings of the 2003 Conference on Universal Usability, ACM Conference on Universal Usability: 93–97, doi: 10.1145/957205.957222 (http://dx.doi.org/10.1145/957205. 957222) 7. Wuerdeman L, Volk L, Pizziferri L et al. (2005). "How Accurate is Information that Patients Contribute to their Electronic Health Record?". AMIA Annu Symp Proc. 2005: 834–8. PMC 1560697 (http://www.ncbi.nlm.nih. gov/pmc/articles/PMC1560697). PMID 16779157 (http://www.ncbi.nlm.nih.gov/pubmed/16779157). Other 1. America’s Health Insurance Plans. Consumer and provider focus groups on PHR. Unpublished. Jan 2005. 2. Angst CM, Agarwal R, Downing J. An empirical examination of the importance of defining the PHR for research and for practice. Robert H. Smith School Research Paper. May 2006. RHS-06-011. 3. California Health Care Foundation. National consumer health privacy survey 2005. Nov 2005. 4. Canedy JT. SimplyWell PHR. AHIC Consumer Empowerment Workgroup Meeting 7/23/06. Jul 2006. 5. Connecting for Health. Americans want benefits of personal health records Jun 2003. 6. Connecting for Health. The personal health working group. Jul 2003. 7. Connecting for Health. Connecting Americans to their Healthcare final report: working group on policies for electronic information sharing between doctors and patients. Jul 2004. 8. Connecting for Health. Connecting Americans to Their Health Care: A Common Framework for Networked Personal Health Information. Dec 2006. 9. Department of Health and Human Services. Standards for privacy of individually identifiable health information. Federal Register. Dec 2000. Billing Code 4150-05M; 82461-82829 (45 CFR Parts 160-164). 10. Detmer D, Steen E. Learning from abroad: lessons and questions on personal health records for national policy. AARP. Mar 2006.
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Personal health record 11. Haslmaier EF. Health care information technology – getting the policy right. Web Memo – Heritage Foundation. Jun 2006. No. 1131. 12. Markle Foundation. Attitudes of Americans regarding personal health records and nationwide electronic health information exchange: key findings from two surveys of Americans. Oct 2005. 13. Miller RH, Sim I. Physicians' use of electronic medical records: barriers and solutions. California HealthCare Foundation. Mar 2004. 116-126. 14. Skewes JL. Shared Health, Inc. AHIC Consumer Empowerment Workgroup Meeting 7/23/06. Jul 2006. 15. Taylor H. Two in five adults keep personal or family health records and almost everybody think this is a good idea. Harris Interactive. Aug 2004.
External links • Video Interview of MIT Professor Peter Szolovitz about early developments of the Personal Health Record (http:/ /blip.tv/file/1185844/) • MedlinePlus - Personal Health Records (http://www.nlm.nih.gov/medlineplus/personalhealthrecords.html) The U.S. National Library of Medicine & National Institutes of Health. • Personal Health Records: What Physicians Need to Know (http://www.texmed.org/WorkArea/linkit. aspx?LinkIdentifier=id&ItemID=13008&libID=10657)
COmputer STored Ambulatory Record COmputer STored Ambulatory Record (COSTAR) is an electronic medical record using the MUMPS programming language. It was developed by the Laboratory of Computer Science at Massachusetts General Hospital between 1968 and 1971 for Harvard Community Health Plan by Octo Barnett and Jerome Grossman.
References • Hattwick, Michael A. W. Computer Stored Ambulatory Record Systems in Real Life Practice [1]. Proc Annu Symp Comput Appl Med Care. 1979 October 17; 761–764. • Barnett, G. Octo. Computer-stored ambulatory record (COSTAR) [2]. 1976. • Agency for Healthcare Research and Quality (AHRQ). Medical Informatics for Better and Safer Health Care [3]. Research in Action, Issue 6. June 2002 • Kerlin, Barbara D (1986). Dissemination of COSTAR: Promises and Realities. Journal of Medical Systems doi:10.1007/BF00992821 [4] • Clinfowiki - COmputer STored Ambulatory Record (COSTAR) [5]
References [1] [2] [3] [4] [5]
http:/ / www. pubmedcentral. nih. gov/ pagerender. fcgi?artid=2231858& pageindex=1 http:/ / openlibrary. org/ b/ OL15209465M/ Computer-stored-ambulatory-record-(COSTAR) http:/ / www. ahrq. gov/ data/ informatics/ informatria. htm http:/ / dx. doi. org/ 10. 1007%2FBF00992821 http:/ / www. clinfowiki. org/ wiki/ index. php/ COmputer_STored_Ambulatory_Record_(COSTAR)
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ProRec
ProRec The ProRec initiative of 1996 is a network of national non-profit organisations (the "ProRec centres"). The initiative was a consequence of the conclusions of the Concerted Action MEDIREC (1994-1995) regarding the reasons why Electronic Health Record (EHR) systems were not used more widely in any of the European Union. As part of the Lisbon Declaration suggestions were made to remedy this situation. The ProRec initiative is supported by the DG Information Society of the European Union. The DG Information Society supported the ProRec initiative with the ProRec Support Action (1996-1998), and the WIDENET Accompanying Measure (2000-2003). The goal of the initiative is to build awareness of the limitations, shortcomings and obstacles on the way towards widespread development, implementation and use of quality Electronic Health Records (EHRs) and pointing them out. Especially significant for implementing Electronic Health Record systems is the ability to communicate and interoperate.
External links • ProRec-BE [1] • ProRec-RO [2]
References [1] http:/ / www. prorec. be/ [2] http:/ / www. prorec. ro/
Health record trust A ""health record trust"" (also independent health record trust or health record data bank) provides a secure and protected place for individuals to create, use, and maintain their lifetime electronic health record (EHR). The health record trust takes personal health records one step further by combining an individual’s electronic health record with the personal health record. A health record trust protects patient privacy by establishing that the patient is the owner of his or her health care records. It gives patients authority to access and review the entire record at any time as well as the authority to allow health care professionals, facilities, and organizations to view the all of the records or a limited portion of the records. Currently a record is left at each facility a patient seeks care. The health record trust allows for all of the information to be in one central document. (Health Record Banking Alliance, n.d.) Patients cannot alter their health records but instead add notes and request corrections. They can also view every provider who downloads their EHR. Legislation was introduced in the 110th Congress to establish a regulatory framework for the establishment of health record trusts. The Independent Health Record Trust Act of 2007 (H.R. 2991) was introduced by Rep. Dennis Moore (D-KS) and Rep. Paul Ryan (R-WI) on July 11, 2007. The legislation seeks to give consumers control over their lifetime health records, with the broader goal of reducing health care costs that result from inefficiency, medical errors, inappropriate care, and incomplete information. This legislation provides standards for the use of health record trusts, including certifications and interoperability of independent health record trusts. HR 2991 was referred to the House Committee on Energy and Commerce and the House Committee on Ways and Means. The bill died in committee and has not been reintroduced. (govtrack.us, n.d.) With the availability of a longitudinal health record protected by a health record trust, patients receive better quality of care and are able to pass along their medical records to future generations. Health record trusts promote wellness and improve patient care through quick and easy access to critical health information.
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Health record trust References Kendall, D. B. (2009). Protecting patient privacy through health record trust. Health Affairs, 28, 444-446. govtrack.us. (n.d.). govtrack.us/congress/bills/110/hr2991 Health Record Banking Alliance. (n.d.). http:/ / www. healthbanking.org/
External links • • • •
HR 2991 [1] GovTrack Summary of H.R. 2991 [2] Health Banking information [3] Personal health record
References [1] http:/ / frwebgate. access. gpo. gov/ cgi-bin/ getdoc. cgi?dbname=110_cong_bills& docid=f:h2991ih. txt. pdf [2] http:/ / www. govtrack. us/ congress/ bill. xpd?bill=h110-2991 [3] http:/ / www. ehrbanks. com
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ClearHealth
148
ClearHealth ClearHealth Developer(s)
Open source community
Initial release
2003
Development status Current Written in
PHP
Operating system
Cross-platform
Available in
Multilingual
Type
Medical Software
License
GNU General Public License
Website
ClearHealth Website
[1]
[2]
ClearHealth is an Open Source practice management (PM) and electronic medical records (EMR/EHR/PHR) system available under the GNU General Public License. It has received attention as a possible open source option for FQHC and CHC sites.[3] It is currently deployed at approximately 600 sites worldwide including commercially supported and self-supported open source installations. There are number of high profile installations in non-profit health settings including the Primary Care Coalition network, powering the Community Healthlink System, in Maryland, USA, which includes approximately 50 sites and 1,500 users[4] and Operation Samahan,[5] a Federally Qualified Health Center (FQHC-Look alike)look alike facility in National City, CA with 5 locations. OsNews provides an introduction to the system.[6]
History The history of ClearHealth began with the core developers of several other Open Source healthcare software systems including OpenEMR and FreeMed. ClearHealth released its first version in 2003, supporting mainly scheduling capabilities. Its 1.0 release was in October 2005 and included additions to the original scheduling capabilities, including support for patient registration/demographics, and electronic billing. In July 2007, its 2.0 version was released which added electronic medical records capabilities and an integrated SQL based reporting system. In 2006, the Tides Foundation provided a grant which funded the development of a set of feature additions to support the specialized needs of Federally Qualified Health Centers (FQHC) and other CHC/RNC facilities. Written in the PHP language and capable of running on most server configurations, Windows, Linux or Mac OS X, under Apache and MySQL (LAMP), ClearHealth is compliant with the expectations of most Open Source web-based systems. Amongst several open source solutions for the healthcare industry, the California Healthcare Foundation identified ClearHealth specifically as a viable solution based on its evaluation of sites and support in its Open Source Primer on healthcare software.[7]
ClearHealth
Features ClearHealth is a comprehensive practice management and EMR system incorporating the key categories of functionality for scheduling, patient registration, electronic medical records and CPOE, electronic and paper billing, and SQL reporting. As an open source reference implementation of several interoperability protocols, ClearHealth has support for working with data in HL7[8] and Continuity of Care Record (CCR) formats. The ClearHealth system is fully compliant with HIPAA security provisions.[9]
References [1] [2] [3] [4] [5] [6] [7] [8] [9]
See (http:/ / www. clear-health. com/ forum/ index. php?t=msg& goto=3756) http:/ / www. clear-health. com/ CHCF Market Assessment California Healthcare Foundation (http:/ / www. chcf. org/ topics/ view. cfm?itemID=133551) VistA and Open Healthcare News May/June 2008 Operation Samahan LinuxMedNews Coverage of Operation Samahan (http:/ / linuxmednews. com/ 1142379128/ index_html) OsNews OsNews Introduction (http:/ / osnews. com/ story/ 10740) CHCF CHCF Open Source Primer (http:/ / www. chcf. org/ topics/ view. cfm?itemID=119091) Fred Trotter Interview HL7 Support (http:/ / linuxmednews. com/ 1113401258/ index_html) CHCF Open Source Healthcare Market Assessment California Healthcare Foundation (http:/ / www. chcf. org/ topics/ view. cfm?itemID=133551)
External links • "ClearHealth User Forums" (http://www.clear-health.com/forum). • "GitHub Site" (http://github.com/clearhealth/). (GitHub) • "Primary Care Coalition" (http://www.primarycarecoalition.org/).
Laika Laika is an open source Electronic Health Record (EHR) testing framework. Laika analyzes and reports on the interoperability capabilities of EHR systems. This includes the testing for certification of EHR software products and networks. Laika is designed to verify the input and output of EHR data against the standards and criteria identified by the Certification Commission for Healthcare Information Technology (CCHIT). Since June 2008, Laika has been used by CCHIT to perform the machine-automated testing of EHR systems for interoperability.
Laika Interoperability Packages • Laika C32: Laika C32 was the first tool to be created in the Laika framework suite, and supports testing of the HL7/ASTM Continuity of Care Document (CCD) constrained by the HITSP C32 version 2.1 specification. • Laika ORU: was released in September 2008 to test the interoperability of HL7 2.5.1 lab messages. Laika ORU can be used with Mirth, an open source health informatics messaging package, to manage the routing of HL7 2.5.1 lab messages with Laika. • Laika XDS: is scheduled to be released in March 2009 to test EHR systems and Health Information Exchange systems with XDS registries and repositories.
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Laika
150
CCHIT and MITRE Collaboration Laika is an active collaborative effort between CCHIT and The MITRE Corporation. CCHIT is leading the functional requirements definition of the Laika testing framework. MITRE is leading the technical software design and is prototyping the software service. The Certification Commission is a private, not-for-profit organization whose mission is to accelerate adoption of health information technology in the United States. MITRE is a 501(c)(3) not-for-profit corporation that manages three Federally Funded Research and Development Centers (FFRDCs) and works in partnership with the US government applying systems engineering and advanced technology to address issues of critical national importance in the USA.
Deployment in Virtual Environments Laika has been deployed in virtual environments using, for example, the Amazon cloud environment. This allows centralized testing of multiple EHRs in segmented environments. It also allows portable implementations, so that field testing can be achieved.
Technical Details Laika is licensed under an Apache 2.0 open source license. Laika uses the Ruby on Rails framework, the Java programming language, the open source PostgreSQL database, and several Web 2.0 JavaScript libraries including Scriptaculous and Prototype.
Laika and popHealth In 2010, the core Laika software infrastructure, consisting of the Laika database and Rails controllers, was forked to support the open source popHealth [1] project. The popHealth project was developed from resources provided by the Federal Health Architecture (FHA [2]) within the Office of the National Coordinator (ONC [3]). popHealth integrates with a healthcare provider's electronic health record (EHR) system to produce summary quality measures on the provider's patient population. The MITRE Corporation was also tasked as the technical lead team for the popHealth activity.
Background of Project Name Laika Laika is named after the dog and first living animal to enter earth orbit, paving the way for human space flight.
External links • The Laika open source project website [4] • The Certification Commission for Healthcare Information Technology website [9] • The MITRE Corporation website [5]
References [1] [2] [3] [4] [5]
http:/ / projectpophealth. org http:/ / www. hhs. gov/ fedhealtharch/ http:/ / healthit. hhs. gov/ portal/ server. pt?open=512& objID=1263& mode=2 http:/ / projectlaika. org/ http:/ / www. mitre. org/
OpenEHR
OpenEHR openEHR is an open standard specification in health informatics that describes the management and storage, retrieval and exchange of health data in electronic health records (EHRs). In openEHR, all health data for a person is stored in a "one lifetime", vendor-independent, person-centred EHR. The openEHR specification is not concerned with the exchange of data between EHR-systems as this is the primary focus of other standards such as EN 13606 and HL7. The openEHR specifications are maintained by the openEHR Foundation [1], a not for profit foundation supporting the open research, development, and implementation of openEHR EHRs. The openEHR specifications are based on a combination of 15 years of European and Australian research and development into EHRs and new paradigms, including what has become known as the archetype methodology[2][3] for specification of content. The openEHR specifications include information and service models for the EHR, demographics, clinical workflow and archetypes. They are designed to be the basis of a medico-legally sound, distributed, versioned EHR infrastructure.
Multi-level Modelling Methodology The key innovation in the openEHR framework is to leave all specification of clinical information out of the information model (also commonly known as "reference model" in health informatics) but, most importantly, to provide a powerful means of expressing what clinicians and patients report that they need to record so that the information can be understood and processed wherever there is a need[citation needed]. Clinical content is specified in terms of two types of artefact which exist outside the information model. The first, known as "archetypes" provides a place to formally define re-usable data point and data group definitions, i.e. content items that will be re-used in numerous contexts. Typical examples include "systemic arterial blood pressure measurement" and "serum sodium". Many such data points occur in logical groups, e.g. the group of data items to document an allergic reaction, or the analytes in a liver function test result. Some archetypes contain numerous data points, e.g. 50, although a more common number is 10-20. A collection of archetypes can be understood as a "library" of re-usable domain content definitions, with each archetype functioning as a "governance unit", whose contents are co-designed, reviewed and published. The second kind of artefact is known in openEHR as a "template", and is used to logically represent a use case-specific data-set, such as the data items making up a patient discharge summary, or a radiology report. A template is constructed by referencing relevant items from a number of archetypes. A template might only require one or two data points or groups from each archetype. In terms of the technical representation, openEHR templates cannot violate the semantics of the archetypes from which they are constructed. Templates are almost always developed for local use by software developers and clinical analysts. Templates are typically defined for GUI screen forms, message definitions and document definitions, and as such, correspond to "operational" content definitions. The justification for the two layers of models over and above the information model is that if data set definitions consist of pre-defined data points from a library of such definitions, then all recorded data (i.e. instances of templates) will ultimately just be instances of the standard content definitions. This provides a basis for standardised querying to work. Without the archetype "library" level, every data set (i.e. chunk of operational content) is uniquely defined and a standard approach to querying is difficult. Accordingly, openEHR defines a method of querying based on archetypes, known as AQL (Archetype Querying Language). While individual health records may be vastly different in content, the core information in openEHR data instances always complies to archetypes. The way this works is by creating archetypes which express clinical information in a way that is highly reusable, even universal in some cases.
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OpenEHR
Formalisms openEHR archetypes are expressed in "Archetype Definition Language", an openEHR public specification. Two versions are available: ADL 1.4 [4], and ADL 1.5 [5], a forthcoming release with better support for specialisation, redefinition and annotations, among other improvements. The 1.4 release of ADL and its "object model" counterpart Archetype Object Model (AOM) are the basis for the CEN and ISO "Archetype Definition Language" standard (ISO standard 13606-2). Templates have historically been developed in a simple, de facto industry-developed XML format, known as ".oet", after the file extension. In future, templates may be expressed in ADL 1.5, enabling them to be processed seamlessly with archetypes.
Quality Assurance of Archetypes Various principles for developing archetypes have been identified.[6] For example, a set of openEHR archetypes needs to be quality managed to conform to a number of axioms such as being mutually exclusive. The archetypes can be managed independently from software implementations and infrastructure, in the hands of clinician groups to ensure they meet the real needs on the ground. Archetypes are designed to allow the specification of clinical knowledge to evolve and develop over time. Challenges in implementation of information designs expressed in openEHR centre on the extent to which actual system constraints are in harmony with the information design.[citation needed]
In the field of Electronic health records there are a number of existing information models with overlaps in their scope which are difficult to manage, such as between HL7 V3 and SNOMED CT. The openEHR approach faces harmonisation challenges unless used in isolation.[citation needed]
International collaboration Following the openEHR approach, the use of shared and governed archetypes globally would ensure openEHR health data could be consistently manipulated and viewed, regardless of the technical, organisational and cultural context. This approach also means the actual data models used by any EHR are flexible, given that new archetypes may be defined to meet future needs of clinical record keeping. Recently work in Australia has demonstrated how archetypes and templates may be used to facilitate the use of legacy health record and message data in an openEHR health record system, and output standardised messages and CDA documents. The prospect of gaining agreement on design and on forms of governance at the international level remains speculative, with influences ranging from the diverse medico-legal environments to cultural variations, to technical variations such as the extent to which a reference clinical terminology is to be integral. The openEHR Framework is consistent with the new Electronic Health Record Communication Standard (EN 13606). It is being used in parts of the UK NHS Connecting for Health Programme and has been selected as the basis for the national program in Sweden. It is also under evaluation in a number of countries including Denmark, Slovakia, Chile and Brazil. It is beginning to be utilised in commercial systems throughout the world.
152
OpenEHR
Clinical Knowledge Manager One of the key features of openEHR is the development of structures and terminologies to represent health data. Due to the open nature of openEHR, these structures are publicly available to be used and implemented in health information systems. Commonly, community users share, discuss and approve these structures in a repository known as the Clinical Knowledge Manager (CKM). Some currently used openEHR CKMs: • openEHR Clinical Knowledge Manager [7] • NEHTA Clinical Knowledge Manager [8] • UK Clinical Knowledge Manager [9]
References [1] [2] [3] [4] [5] [6] [7] [8]
http:/ / www. openehr. org (PDF) (PDF) https:/ / github. com/ openEHR/ specifications/ blob/ master/ publishing/ architecture/ am/ adl1. 4. pdf?raw=true https:/ / github. com/ openEHR/ specifications/ blob/ master/ publishing/ architecture/ am/ adl1. 5. pdf?raw=true (PDF) http:/ / openehr. org/ knowledge http:/ / dcm. nehta. org. au/ ckm/
[9] http:/ / www. clinicalmodels. org. uk/ ckm/
External links • openEHR Foundation website (http://www.openEHR.org) • openEHR specifications (http://www.openehr.org/releases/1.0.2/roadmap.html)
153
OpenEMR
154
OpenEMR OpenEMR
Stable release
4.1.2 / August 17, 2013
Operating system Any Unix-like, Mac OS, Windows Platform
Cross-platform
Type
Free and Open Source, Practice management, Electronic Medical Records
License
GNU General Public License
Website
OpenEMR
[1]
OpenEMR is a Free and Open Source, Practice management and Electronic Medical Records software application. It is ONC Complete Ambulatory EHR certified and it features fully integrated electronic medical records, practice management, scheduling, electronic billing, internationalization, and free support. It can run on Microsoft systems, Unix-like systems (Linux, UNIX, and BSD systems), Mac OS X and other platforms. OpenEMR is one of the most popular free electronic medical records in use today with over 3,700 downloads per month. The community maintains the official OpenEMR web site at open-emr.org.
History OpenEMR was originally developed by Synitech and version 1.0 was released in June 2001 as MP Pro (MedicalPractice Professional). Much of the code was then reworked for HIPAA compliance and improved security, and the product was reintroduced as OpenEMR version 1.3 a year later, in July 2002. It became an open source project and was registered on SourceForge.net on August 13, 2002. The project evolved through version 2.0 and the Pennington Firm (Pennfirm) took over as its primary maintainer in 2003. Walt Pennington transferred the OpenEMR software repository to SourceForge in March 2005, where it remains today. Mr. Pennington also established Rod Roark, Andres Paglayan and James Perry, Jr. as administrators of the project. Walt Pennington, Andres Paglayan and James Perry eventually took other directions and were replaced by Brady Miller in August 2009. So at this time Rod Roark and Brady Miller are the project's co-administrators.
Features • • • • • • •
Free ONC Complete Ambulatory EHR Certified Patient Demographics Patient Scheduling Electronic Medical Records Prescriptions Medical Billing
• Clinical Decision Rules • Patient Portal • Reports
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• Multilanguage Support • Security • Free Support
Development The official OpenEMR code repository was migrated from cvs to git on 10/20/2010. Currently, the project's official code repository is on Sourceforge. There are also official mirrored code repositories on Github, Google Code, Gitorious, Bitbucket, Assembla, CodePlex and Repo.or.cz. OpenEMR has a vibrant open-source development community with over 83 developers having contributed to the project. There are 205 developers with personal OpenEMR code repositories on Github. Ohloh considers OpenEMR to have "one of the largest open-source teams in the world, and is in the top 2% of all project teams on Ohloh".
Releases Version
Release Date
Feature Highlight(s)
1.0
June, 2001
1.3
July 5, 2002
2.0.0
June 30, 2003
2.5.2
March 25, 2004
Support for HL7
2.7.2
June 1, 2005
Calendar overhaul, Added access controls
2.8.0
November 11, 2005 Support UB-92 claims
2.8.1
February 24, 2006
Language translation support
2.8.2
January 14, 2007
PHP5/MySQL5 Compatible, CAMOS Module
2.8.3
October 4, 2007
Support HCFA 1500
2.9.0
August 7, 2008
Customizable layouts
3.0.1
April 8, 2009
Fully integrated php-Gacl access controls
3.1.0
August 28, 2009
Full UTF-8 compliance
3.2.0
February 16, 2010
Layout based form builder module
4.0.0
March 26, 2011
ONC Modular Ambulatory EHR certification
4.1.0
September 23, 2011 ONC Complete Ambulatory EHR certification
4.1.1
August 31, 2012
Translated in 19 languages, ICD10 support
4.1.2
August 17, 2013
Interoperability
Development/Project Highlight(s) Released as MP Pro (MedicalPractice Professional)
Support for HIPAA compliance
Released as OpenEMR, open sourced with GPL license
Code moved to Sourceforge
Internationalization project started
Code repository changed from cvs to git
Deployments Because this is an open source project that does not register its users, it is very difficult to estimate the number of practitioners that are using this software. In the US, it has been estimated that there are more than 5,000 installations of OpenEMR in physician offices and other small healthcare facilities serving more than 30 million patients. Internationally, it has been estimated that OpenEMR is installed in over 15,000 healthcare facilities, translating into more than 45,000 practitioners using the system which are serving greater than 90 million patients. The Peace Corps plan to incorporate OpenEMR into their EHR system in 2013. Siaya District Hospital, a 220-bed hospital in rural Kenya, is using OpenEMR. HP India is planning to utilize OpenEMR for their Mobile Health Centre Project. There are also articles describing single clinician deployments and a free clinic deployment. Internationally, it is known
OpenEMR that there are practitioners in Pakistan, Puerto Rico, Australia, Sweden, Holland, Israel, India, Malaysia, Nepal, Indonesia, Bermuda, Armenia, Kenya, and Greece that are either testing or actively using OpenEMR for use as a free electronic medical records program.
Architecture OpenEMR is a "LAMP" type of web based software application that uses a web server such as Apache, MySQL as the database and PHP as its programming language. As with most "LAMP" architecture, OpenEMR can run on Linux, Unix, BSD, Mac OS X, and Microsoft architectures.
Certification Both OpenEMR versions 4.1.0 (released on 9/23/2011), 4.1.1 (released on 8/31/2012) and 4.1.2 (released on 8/17/2013) have ONC Complete Ambulatory EHR Certification. OpenEMR was certified by ICSA Labs and the OEMR organization is a non-profit entity that manages/provides this certification.
Awards OpenEMR has received a Bossie Award in the "The Best Open Source Applications" category in both 2012 and 2013.
OEMR OEMR is a 501(c)(3) tax exempt entity that was organized in July, 2010 to support the OpenEMR project. OEMR is the entity that holds the ONC Complete Ambulatory EHR Certification with ICSA Labs.
References [1] http:/ / www. open-emr. org/
• Wallen, Jack (2011-10-11). " DIY: OpenEMR, free software for medical practices (http://www.techrepublic. com/blog/doityourself-it-guy/diy-openemr-free-software-for-medical-practices/861)". Retrieved 2011-10-11. • Krishna, Sreevidya (2010-11-30). " Taking medical records into the digital age - Solving traditional system challenges with OpenEMR (http://www.ibm.com/developerworks/industry/library/ind-openemr/index. html?cmp=dw&cpb=dwope&ct=dwgra&cr=twitter&ccy=zz&csr=openemr)". Retrieved 2012-02-25. • AG (2010-02-12). " OpenEMR - At a glance (http://bkaeg.org/blog/archives/2010/02/openemr---at-a.html)". Retrieved 2010-02-16. • Lewis, Hans (2010-01-18). " Electronic Medical and Health Records Usage Increases in U.S.: Report (http:// healthcare.tmcnet.com/topics/healthcare/articles/ 72903-electronic-medical-health-records-usage-increases-us-report.htm)". Retrieved 2010-01-23. • Shah, Shahid (2007-01-07). " Open Source EMR and Practice Management Software Appliance (http://www. healthcareguy.com/2007/01/07/open-source-emr-and-practice-management-software-appliance/341/)". Retrieved 2009-12-07.
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External links • Official website (http://www.open-emr.org/) • Source code repository (http://sourceforge.net/projects/openemr/) • OEMR.org: A non-profit organization that supports OpenEMR (http://oemr.org/)
OpenMRS OpenMRS is a collaborative open source project to develop software to support the delivery of health care in developing countries. It grew out of the critical need to scale up the treatment of HIV in Africa but from the start was conceived as a general purpose electronic medical record system that could support the full range of medical treatments. The first ideas and prototype of OpenMRS were conceived by Paul Biondich and Burke Mamlin from the Regenstrief Institute, Indiana on a visit to the AMPATH [1] project in Eldoret, Kenya in February 2004. Around the same time the EMR team at Partners In Health led by Hamish Fraser and Darius Jazayeri were looking at ways to scale up the PIH-EMR [2] web-based medical record system developed to manage drug resistant tuberculosis in Peru, and HIV in rural Haiti. Paul, Burke and Hamish met in September 2004 at the Medinfo conference in San Francisco, and recognized they had a common approach to medical information systems and a similar philosophy for healthcare and development and OpenMRS was born. Later, Chris Seebregts of the South African Medical Research Council [3] (MRC) became the fourth founding member. OpenMRS is founded on the principles of openness and sharing of ideas, software and strategies for deployment and use. The system is designed to be usable in very resource poor environments and can be modified with the addition of new data items, forms and reports without programming. It is intended as a platform that many organizations can adopt and modify avoiding the need to develop a system from scratch. The software is licensed under the "OpenMRS Public License", based on the Mozilla Public License. It requires that recipients are entitled to freely access the source code, but allows binary distribution, modification of the code (under the same license) and bundling into larger products that are under different licenses.
Design OpenMRS is based on a "concept dictionary" that describes all the data items that can be stored in the system such as clinical findings, laboratory test results or socio-economic data. This approach avoids the need to modify the database structure to add new diseases for example, and facilitates sharing of data dictionaries between projects and sites. An important feature of OpenMRS is its modular construction which allows the programming of new functions without modifying the core code. OpenMRS is web based but can be deployed on a single laptop or on a large server and runs on Linux, Windows or Mac OS X. Other key features of OpenMRS: • • • • •
Built on the MySQL database (but uses Hibernate allowing it to be ported to other databases) Programmed in Java Includes tools for data export and reporting Versions currently exist for HIV/AIDS, Drug resistant TB, primary care and oncology Supports open standards for medical data exchange including HL7, LOINC and IXF
New features: • New open source form based tools, OpenXdata, and HTMLForm entry • Tools to link to hand held devices and cell phones (JavaROSA [4] project) • Research data collection tools for clinical trials and community data collection projects • A "logic service" that allows clinical alerts and reminders to be created in a medical standard Arden Syntax
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OpenMRS • Support for report generation and transmission to national reporting systems like DHIS and TRACnet using the SDMX-HD standard Currently being tested: • • • •
Tools for data synchronization between systems connected by slow or unreliable internet User interface improvements Links to cell phone based data collection and messaging tools using SMS Touch screen patient registration systems supporting bar coded patient IDs, in collaboration with Baobab Health Systems [5]
Deployments The first deployment was in Eldoret, Kenya in February 2006 followed by the PIH-supported hospital in Rwinkwavu [6] , Rwanda in August 2006 and Richmond Hospital in the KwaZulu-Natal province of South Africa later that year. As of March 2010, OpenMRS is in use in at least 23 developing countries (mostly in Africa) and it has been used to record over 1 million patient records around the world. Most deployments are run by independent groups who carry out the work on the ground with technical support and training provided by the core team of OpenMRS developers, and other implementers. There have been four annual OpenMRS meetings in South Africa, organized by Chris Seebregts, who also leads the OpenMRS implementers community. Shorter meetings were held in Boston in May 2009, and a developer training in Indianapolis in February 2010. There are five known deployments supporting clinical care in the US - three in Indianapolis, one in Los Angeles, and one in Maryland. OpenMRS use will be expanded in Haiti to assist with the patients recovering from the January 2010 earthquake.
Support OpenMRS is supported by core teams from Partners In Health, Regenstrief Institute, and the South African Medical Research Council. Other organizations that collaborate on OpenMRS are the Millennium Villages Project, based at Columbia University, and Baobab Health Systems [5] in Malawi. There are several groups of programmers working on OpenMRS in developing countries including Kenya, Rwanda, Uganda, South Africa, Pakistan, Chile, and India. In Rwanda, a training program for software developers to learn advanced Java skills and OpenMRS development, graduated 10 students in October 2009 after an intensive 11-month training program. Nine graduates are currently working on OpenMRS and related eHealth technologies.
Community The OpenMRS community includes developers, implementers, and users from multiple countries who collaborate through mailing lists [7], #openmrs IRC [8], and annual conferences. OpenMRS has participated in Google Summer of Code in 2007, 2008, 2009, 2010, 2011 and 2012; according to that program's manager, it receives more student applications [9] than the Apache Software Foundation.
References [1] [2] [3] [4] [5] [6] [7]
http:/ / www. iukenya. org/ http:/ / model. pih. org/ electronic_medical_records/ pih_emr_overview http:/ / www. mrc. ac. za/ http:/ / code. javarosa. org/ http:/ / www. baobabhealth. org/ http:/ / pih. org/ where/ Rwanda/ Rwanda. html http:/ / openmrs. org/ wiki/ Community
[8] irc:/ / irc. freenode. net [9] http:/ / twitter. com/ HFOSS/ status/ 10280000103
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External links • • • •
OpenMRS (http://openmrs.org) Regenstrief Institute (http://regenstrief.org) Partners In Health (http://www.pih.org) Podcast/MP3 of an episode of the BBC radioprogramme Digital Planet. (http://downloads.bbc.co.uk/podcasts/ worldservice/digitalp/digitalp_20080623-1726.mp3) Described OpenMRS on 2008-06-23; 07m 02s - 14 m 23s. • OpenMRS presentation for Google Tech Talks. August 23, 2007 (http://video.google.com/ videoplay?docid=5181254373166129293) • Lecture slides on OpenMRS from the May 2009 meeting, Boston, MA, USA (http://www.slideshare.net/ hamishfraser/presentations)
Kind Messages for Electronic Healthcare Record KMEHR or Kind Messages for Electronic Healthcare Record is a proposed Belgian medical data standard introduced in 2002, in order to enable the exchange of structured clinical information. It is funded by the Belgian federal Ministry of public health and assessed in collaboration with Belgian industry. The initiative lead to the specification of about 20 specific XML messages (the Kind Messages for Electronic Healthcare Records - Belgian implementation standard or KMEHR-bis).
Structure The KMEHR standard consists of the following elements: • An XML (eXtensible Markup Language) message format defined by the KMEHR XML Schema • A set of reference tables
Message structure A KMEHR XML message is composed of two components a header and at least one folder. The header of the message describes the sender, the recipient(s) and a few technical statuses. The folder itself gathers the information about a patient, where each folder identifies the subject of care (patient) and contains at least one medical transaction. The medical transaction item gathers the information reported by one healthcare professional at a given instance. Its attributes are type, author, date and time.
Coding The KMEHR-bis standard comprises a set of dictionaries which define the transaction types, heading types, item types, severity levels and administration routes.
External links The KMEHR-bis standard supports links to either internal or external objects, e.g. an image or another KMEHR-message.
Services The KMEHR-Bis specification is extended with web services, based on SOAP, which define request and response elements to offer standard web services.
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Source • KMEHR: Kind Messages for Electronic Healthcare Record, Belgian Implementation Standard [1] • Belgian Implementation Standard Guided Tour [2] • Advice no. 4 of the Telematics Commission [3]
References [1] https:/ / www. ehealth. fgov. be/ standards/ kmehr/ [2] https:/ / www. ehealth. fgov. be/ standards/ kmehr/ content/ page/ 75/ guided-tour [3] https:/ / www. ehealth. fgov. be/ standards/ kmehr/ sites/ default/ files/ assets/ home/ fourthrecommendation. pdf
Summarized Electronic Health Record SumEHR or Summarised Electronic Health Record is a KMEHR message, used for the exchange of medical information. It summarizes the minimal set of data that a physician needs in order to understand the medical status of the patient in a few minutes and to ensure the continuity of care. The SumEHR standard was introduced by the Belgian government in 2005 and an EMD software package used by a physician (GP) should be capable of exporting a SumEHR message (KMEHR message level 4) for any given patient.
Layout • Date of creation • Author • Patient Identification • Health Number (mandatory item – empty if no available number) • Patient Presentation • Family name • Forenames • Sex • Birth date • Usual language • Contact person • Risks • Allergies • Adverse drug reactions • Social factors • Other factors • Relevant personal antecedents • IBUI (French: Identificateur Belge Unique, Dutch: Belgische Unieke Identificator) and ICPC-2 and ICD-10 (empty IBUI allowed) • Begin date • End date • Text • Actual problems list • IBUI and ICPC-2 and ICD-10 (empty IBUI allowed) • Begin date
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Summarized Electronic Health Record • Text • Relevant medications • CNK (Code National(e) Kode) or other ID (if no available CNK) • Administration information • Instructions for patient • Begin date • End date • Text • Vaccination status • Administrated • CNK and/or ATC (Anatomical Therapeutic Chemical-code) • Date • To be administered • CNK and/or ATC • Date • Contextual comment
Source • Sumehr [1] • Transaction: Summarised Electronic Healthcare Record [2]
References [1] https:/ / portal. health. fgov. be/ pls/ portal/ docs/ PAGE/ INTERNET_PG/ HOMEPAGE_MENU/ GEZONDHEIDZORG1_MENU/ AUTOMATISERING1_MENU/ HOPITAUX9_MENU/ SUMEHRPHOTOSANTE1_MENU/ SUMEHRPHOTOSANTE1_DOCS/ SUMEHR-CONTENTS. PDF [2] https:/ / www. ehealth. fgov. be/ standards/ kmehr/ en/ transaction_detail/ home/ transactions/ transaction_detail/ Sumehr-1-1. xml
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VistA The Veterans Health Information Systems and Technology Architecture (VistA) is an enterprise-wide information system built around an Electronic Health Record (EHR), used throughout the United States Department of Veterans Affairs (VA) medical system, known as the Veterans Health Administration (VHA). It consists of nearly 160 integrated software modules for clinical care, financial functions, and infrastructure. The VHA manages the largest medical system in the United States, providing care to over 8 million veterans, employing 180,000 medical personnel and operating 163 hospitals, over 800 clinics, and 135 nursing homes throughout the continental U.S., Alaska, and Hawaii on a single electronic healthcare information network. Nearly 25% of the nation's population is potentially eligible for VA benefits and services because they are veterans, family members, or survivors of veterans. Over 60% of all physicians trained in the U.S. rotate through the VHA on clinical electives, making VistA the most familiar and widely used EHR in the U.S. Nearly half of all U.S. hospitals that have a complete (inpatient/outpatient) enterprise-wide implementation of an EHR are VA hospitals using VistA.
The VistA Computerized Patient Record System (CPRS) cover sheet view
Sample patient record view from VistA Imaging
Features The Department of Veterans Affairs (VA) has had automated data processing systems, including extensive clinical and administrative capabilities, within its medical facilities since before 1985. Initially called the Decentralized Hospital Computer Program (DHCP) information system, DHCP was enshrined as a recipient of the Computerworld Smithsonian Award for best use of Information Technology in Medicine in 1995. VistA supports both ambulatory and inpatient care, and includes several significant enhancements to the original DHCP system. The most significant is a graphical user interface for clinicians known as the Computerized Patient Record System (CPRS), which was released in 1997. In addition, VistA includes computerized order entry, bar code medication administration, electronic prescribing, and clinical guidelines. CPRS provides a client–server interface that allows health care providers to review and update a patient's electronic medical record. This includes the ability to place orders, including those for medications, special procedures, X-rays,
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nursing interventions, diets, and laboratory tests. CPRS provides flexibility in a wide variety of settings so that a consistent, event-driven, Windows-style interface is presented to a broad spectrum of health care workers.
Clinical Functions • • • • • • • • • • • • •
Admission Discharge Transfer (ADT) Ambulatory Care Reporting Anticoagulation Management Tool (AMT) Automated Service Connected Designation (ASCD) Beneficiary Travel Blind Rehabilitation Care Management Clinical Case Registries Clinical Procedures Clinical/Health Data Repository (CHDR) Computerized Patient Record System (CPRS) CPRS: Adverse Reaction Tracking (ART) CPRS: Authorization Subscription Utility (ASU)
• • • • • • • • • • • • • • • • • • • • • • • • • • • •
CPRS: Clinical Reminders CPRS: Consult/Request Tracking CPRS: Health Summary CPRS: Problem List CPRS: Text Integration Utility (TIU) Dentistry Electronic Wait List Pharm: National Drug File (NDF) Emergency Department Integration Software (EDIS) Functional Independence Measurement (FIM) Group Notes Primary Care Management Module (PCMM) HDR – Historical (HDR-Hx) Home Based Primary Care (HBPC) Home Telehealth Immunology Case Registry (ICR) Incomplete Records Tracking (IRT) Intake and Output Scheduling Laboratory Shift Handoff Tool Laboratory: Anatomic Pathology Laboratory: Blood Bank Laboratory: Blood Bank Workarounds Laboratory: Electronic Data Interchange (LEDI) Laboratory: Emerging Pathogens Initiative (EPI) Laboratory: Howdy Computerized Phlebotomy Login Process Laboratory: National Laboratory Tests (NLT) Documents and LOINC Request Form Laboratory: Point of Care (POC) Laboratory: Universal Interface Laboratory: VistA Blood Establishment Computer Software (VBECS) Lexicon Utility
• Medicine • Mental Health
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• • • • • • • • • • • • • • • • •
Methicillin Resistant Staph Aurerus (MRSA) Mobile Electronic Documentation (MED) Nationwide Health Information Network Adapter (NHIN) Nursing Nutrition and Food Service (NFS) Oncology Patient Appointment Info. Transmission (PAIT) Patient Assessment Documentation Package (PADP) Patient Care Encounter (PCE) Patient Record Flags Pharm: Automatic Replenish / Ward Stock (AR/WS) Pharm: Bar Code Medication Administration (BCMA) Pharm: Benefits Management (PBM) Pharm: Consolidated Mail Outpatient Pharmacy Pharm: Consolidated Mail Outpatient Pharmacy Pharm: Controlled Substances Pharm: Data Management (PDM)
• • • • • • • • • • • • • • • • • • • •
Pharm: Drug Accountability Pharm: Inpatient Medications Pharm: Outpatient Pharmacy Pharm: Prescription Practices (PPP) Prosthetics Quality Audiology and Speech Analysis and Reporting (QUASAR) Radiology / Nuclear Medicine RAI/MDS Remote Order Entry System (ROES) Social Work Spinal Cord Dysfunction Standards & Terminology Services (STS) Surgery Traumatic Brain Injury (TBI) Virtual Patient Record VistA Imaging System VistAWeb Visual Impairment Service Team (VIST) Vitals / Measurements Women's Health
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Financial-Administrative Functions • • • • • • • • • • • • • • • •
Accounts Receivable (AR) Auto Safety Incident Surv Track System (ASISTS) Automated Information Collection System (AICS) Automated Medical Information Exchange (AMIE) Clinical Monitoring System Integrated Billing (IB) Compensation Pension Record Interchange (CAPRI) Current Procedural Terminology (CPT) Library Decision Support System (DSS) Extracts Diagnostic Related Group (DRG) Grouper Electronic Claims Management Engine (ECME) Engineering (AEMS / MERS) Police and Security Enrollment Application System Quality Management Integration Module Equipment / Turn-In Request Event Capture Release of Information (ROI) Manager Fee Basis Fugitive Felon Program (FFP)
• • • • • • • • • • • • • • •
Generic Code Sheet (GCS) Health Eligibility Center (HEC) Hospital Inquiry (HINQ) ICD-9-CM Incident Reporting Income Verification Match (IVM) Integrated Patient Funds Occurrence Screen Patient Representative Personnel and Accounting Integrated Data (PAID) Record Tracking Veterans Identification Card (VIC/PICS) Voluntary Service System (VSS) WebHR Wounded Injured and Ill Warriors
Infrastructure Functions • • • • • • • • • •
Capacity Management Tools Duplicate Record Merge: Patient Merge Name Standardization Electronic Error and Enhancement Reporting (E3R) Enterprise Exception Log Service (EELS) FatKAAT FileMan FileMan Delphi Components (FMDC) Health Data Informatics HL7 (VistA Messaging) Institution File Redesign (IFR)
• KAAJEE • Kernel • Kernel Delphi Components (KDC)
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• • • • • • • • • • • • • • • • •
Kernel Toolkit Kernel Unwinder List Manager M-to-M Broker MailMan Master Patient Index (MPI) Medical Domain Web Services (MDWS) (MWVS*2) National Online Information Sharing (NOIS) National Patch Module Network Health Exchange (NHE) Patient Data Exchange (PDX) Remote Procedure Call (RPC) Broker Resource Usage Monitor Single Signon/User Context (SSO/UC) SlotMaster (Kernel ZSLOT) SQL Interface (SQLI) Standard Files and Tables
• • • • • •
Statistical Analysis of Global Growth (SAGG) Survey Generator System Toolkit (STK) VistA Data Extraction Framework (VDEF) VistALink XML Parser (VistA)
Patient Web Portal Functions • • • • • • • • •
Clinical Information Support System (CISS) Electronic Signature (ESig) Person Services HealtheVet Web Services Client (HWSC) Registries My HealtheVet Spinal Cord Injury and Disorders Outcomes (SCIDO) National Utilization Management Integration (NUMI) Occupational Health Record-keeping System (OHRS) Patient Advocate Tracking System (PATS) VA Enrollment System (VES) Veterans Personal Finance System (VPFS)
Achievements For its development of VistA, the United States Department of Veterans Affairs (VA) / Veterans Health Administration (VHA) was named the recipient of the prestigious Innovations in American Government Award presented by the Ash Institute of the John F. Kennedy School of Government at Harvard University in July, 2006. The VistA electronic medical records system is estimated to improve efficiency by 6% per year, and the monthly cost of the EHR is offset by eliminating the cost of even a few unnecessary tests or admissions. The adoption of VistA has allowed the VA to achieve a pharmacy prescription accuracy rate of 99.997%, and the VA outperforms most public sector hospitals on a variety of criteria, enabled by the implementation of VistA. VA hospitals using VistA are one of only thirteen hospital systems that have achieved the qualifications for HIMSS stage 7, the highest level of electronic health record integration,[1][2] while a non-VA hospital using VistA is one of only 42 US hospitals that has achieved HIMSS stage 6.
VistA
Licensing and dissemination The VistA system is public domain software, available through the Freedom Of Information Act directly from the VA website or through a growing network of distributors.
VistA modules and projects Database backend VistA was developed using the M or MUMPS language/database. The VA currently runs a majority of VistA systems on the proprietary InterSystems Caché version of MUMPS, but an open source MUMPS database engine, called GT.M, for Linux and Unix computers has also been developed. Although initially separate releases, publicly available VistA distributions are now often bundled with the GT.M database in an integrated package. This has considerably eased installation. The free, open source nature of GT.M allows redundant and cost-effective failsafe database implementations, increasing reliability for complex installations of VistA.
Database projections An open source project called EsiObjects [3] has also allowed the (ANSI- Standard) MUMPS language and database technology to evolve into a modern object-oriented language (and persistent-object store) that can be integrated into mainstream, state-of-the-art technologies. For the Caché MUMPS database, a similar object-oriented extension to MUMPS called Caché ObjectScript has been developed. Both of these have allowed development of the MUMPS database environment (by programmers) using modern object-oriented tools. M2Web [4] is an open source web gateway to MUMPS for use with VistA. A free open source module from M/Gateway [5] called MGWSI has been developed to act as a gateway between GT.M, Cache, or M21 MUMPS databases and programming tools such as PHP, ASP.NET, or Java, in order to create a web-based interface.
Patient Web Portal MyHealtheVet [6] is a web portal that allows veterans to access and update their personal health record, refill prescriptions, and schedule appointments. This also allows veterans to port their health records to institutions outside the VA health system or keep a personal copy of their health records, a Personal Health Record (PHR).
VistA Imaging The Veterans Administration has also developed VistA Imaging, a coordinated system for communicating with PACS (radiology imaging) systems and for integrating others types of image-based information, such as EKGs, pathology slides, and scanned documents, into the VistA electronic medical records system. This type of integration of information into a medical record is critical to efficient utilization. VistA Imaging has been made freely available in the public domain for private/public hospital use through the Freedom of Information Act. It is available through the Department of Veteran's Affairs software request [7] office. (Licensing of several proprietary modules are required for it to function correctly.) It can be used independently or integrated into the VistA electronic health record system (as is done in VA health facilities).
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Deployments and uses Role in development of a national healthcare network The VistA electronic healthcare record has been widely credited for reforming the VA healthcare system, improving safety and efficiency substantially. The results have spurred a national impetus to adopt electronic medical records similar to VistA nationwide. VistA Web collectively describes a set of protocols that in 2007 was being developed and used by the VHA to transfer data (from VistA) between hospitals and clinics within the pilot project. This is the first effort to view a single patient record so that VistA becomes truly interoperable among the more than 128 sites running VistA today. BHIE enables real-time sharing of electronic health information between DoD and VA for shared patients of allergy, outpatient pharmacy, demographic, laboratory, and radiology data. This became a priority during the Second Iraq War, when a concern for the transition of healthcare for soldiers as they transferred from active military status to veteran status became a national focus of attention. A Clinical Data Repository/Health Data Repository (CHDR [8]) allows interoperability between the DoD's Clinical Data Repository (CDR) & the VA's Health Data Repository (HDR). Bidirectional real time exchange of computable pharmacy, allergy, demographic and laboratory data occurred in phase 1. Phase 2 involved additional drug–drug interaction and allergy checking. Initial deployment of the system was completed in March 2007 at the El Paso, Augusta, Pensacola, Puget Sound, Chicago, San Diego, and Las Vegas facilities. The combination of VistA and the interoperable projects listed above in the VA/DoD systems will continue to expand to meet the objectives that all citizens will have an electronic record by 2014. Because of the success of these programs, a national move to standardize healthcare data transmission across the country was started. Text based information exchange is standardized using a protocol called HL7 (Health Level 7), which is approved by the American National Standards Institute. DICOM is an international image communications protocol standard. VistA is compliant with both. VistA has been interfaced with commercial off-the-shelf products, as well. Standards and protocols used by VA are consistent with current industry standards and include HL7, DICOM, and other protocols. Tools for CCR/CCD support have been developed for VistA, allowing VistA to communicate with other EHRs using these standardized information exchange protocols. This includes the Mirth open source cross platform HL7 interface and NHIN Connect, the open source health information exchange adaptor. In 2009, a project was undertaken to facilitate EHR communication between the VA (using VistA) and Kaiser Permanente (using Epic) using NHIN Connect. (Both VistA and the commercial EHR Epic use a derivative of the MUMPS database, thereby facilitating data exchange.) When completed, two of the largest medical record systems in the US will be able to exchange health data. Public-domain VistA derivatives are also expected to be able to use NHIN Connect. The VistA EHR has been used by the VA in combination with Telemedicine to provide surgical care to rural areas in Nebraska and Western Iowa over a 400,000-square-mile (1,000,000km2) area.
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Usage in non-governmental hospitals Under the Freedom of Information Act (FOIA), the VistA system, the CPRS graphical interface, and unlimited ongoing updates (500–600 per year) are provided as public domain software. This was done by the US government in an effort to make VistA available as a low cost Electronic Health Record (EHR) for non-governmental hospitals and other healthcare entities. With funding from The Pacific Telehealth & Technology Hui, the Hui 7 produced a version of VistA that ran on GT.M in a Linux operating system, and that was suitable for use in private settings. VistA has since been adapted by companies such as Blue Cliff [9], DSS, Inc. [10], Medsphere [11], and Sequence Managers Software [12] to a variety of environments, from individual practices to clinics to hospitals, to regional healthcare co-ordination between far-flung islands. In addition, VistA has been adopted within similar provider environments worldwide. Universities, such as UC Davis and Texas Tech implemented these systems. A non-profit organization, WorldVistA, has also been established to extend and collaboratively improve the VistA electronic health record and health information system for use outside of its original setting. VistA (and other derivative EMR/EHR systems) can be interfaced with healthcare databases not initially used by the VA system, including billing software, lab databases, and image databases (radiology, for example). VistA implementations have been deployed (or are currently being deployed) in non-VA healthcare facilities in Texas, Arizona, Florida, Hawaii, New Jersey, Oklahoma, West Virginia, California, New York, and Washington, D.C. In one state, the cost of a multiple hospital VistA-based EHR network was implemented for one tenth the price of a commercial EHR network in another hospital network in the same state ($9 million versus $90 million for 7–8 hospitals each). (Both VistA and the commercial system used the MUMPS database). VistA has even been adapted into a Health Information System (VMACS) at the veterinary medical teaching hospital at UC Davis.
International deployments VistA software modules have been installed around the world, or are being considered for installation, in healthcare institutions such as the World Health Organization, and in countries such as Mexico, Samoa, Finland, Jordan, Germany,[13] Kenya, Nigeria, Egypt, Malaysia, India, Brazil, Pakistan, and Denmark. In September 2009, Dell Computer bought Perot Systems, the company installing VistA in Jordan (the Hakeem project).
History The concept that eventually became VistA was initiated and planned at the beginning of the 1970s by the National Center for Health Services Research and Development of the U.S. Public Health Service (NCHSR&D/PHS). (The NCHSR&D is now known as the Agency for Healthcare Research and Quality (AHRQ).) As a proof of concept, the U.S. Navy's clinic at the Brunswick Naval Air Station had used an early version of the system software to develop an operational, automated, clinic-management and medical-record system that was "paperless". The National Center for Health Sciences Research and Development then turned to an agency of the U.S. Department of Commerce, the National Bureau of Standards (NBS, reorganized in 1988 as the National Institute of Standards and Technology), to turn the systems-technology strategy into a systems-architecture design. Under the farsighted leadership of the VA's Chief Medical Director, Dr. John Chase, the VA's Department of Medicine and Surgery (now known as the Veterans Health Administration (VHA)), then agreed to deploy the system at the largest medical system of that time, the VA hospitals.
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VistA Both Dr. Robert Kolodner (National Health Information Technology Coordinator)[14] and George Timson (an architect of VistA who has been involved with it since the early years) date VistA's actual architecture genesis, then, to 1977. The program was launched in 1978 with the deployment of the initial modules in about twenty VA Medical Centers. The program was named the Decentralized Hospital Computer Program (DHCP) in 1981. The physicians in VA Medical Centers, with leadership from the National Association of VA Physicians (NAVAP, renamed NAVAPD [15] in 1989 when Dentists were added) and its Executive Director, Dr. Paul Shafer, made sure that the VA understood the importance of clinician-directed development and refinement of this new clinical-information system. In December 1981, Congressman Sonny Montgomery of Mississippi arranged for the Decentralized Hospital Computer Program (DHCP) to be written into law as the medical-information systems development program of the VA. VA Administrator Robert P. Nimmo signed an Executive Order in February 1982 describing how the DHCP was to be organized and managed within the VA's Department of Medicine and Surgery. In consultation with F. Whitten Peters and Vincent Fuller of the Williams and Connolly law firm, it was established at the beginning of the 1980s that the software existing in the VA (derived from the PHS projects) was legally in the public domain and must be made available without proprietary or other restrictions to other government and private-sector organizations for their use. In conjunction with the VA's DHCP development, the (PHS) Indian Health Service deployed a system built on and augmenting DHCP throughout its Federal and Tribal facilities as the Resource and Patient Management System (RPMS [16]). This implementation emphasized the integration of outpatient clinics into the system, and many of its elements were soon re-incorporated into the VA system (through a system of technology sharing). Subsequent VistA systems therefore included elements from both RPMS and DHCP. Health IT sharing between VA and IHS continues to the present day. The U.S. Department of Defense (DoD) then contracted with Science Applications International Corporation (SAIC) for a heavily modified and extended form of the DHCP system for use in DoD healthcare facilities, naming it the Composite Health Care System (CHCS). Meanwhile, in the early 1980s, major hospitals in Finland were the first institutions outside of the United States to adopt and adapt the VistA system to their language and institutional processes, creating a suite of applications called MUSTI and Multilab. (Since then, institutions in Germany, Egypt, Nigeria, and other nations abroad have adopted and adapted this system for their use, as well.) The name "VistA" (Veterans Health Information System and Technology Architecture) was adopted by the VA in 1994, when the Under Secretary for Health of the U.S. Department of Veterans Affairs (VA), Dr. Ken Kizer, renamed what had previously been known as the Decentralized Hospital Computer Program (DHCP). The four major adopters of VistA – VA (VistA), DoD (CHCS), IHS (RPMS), and the Finnish Musti consortium [17] – each took VistA in a different direction, creating related but distinct "dialects" of VistA. VA VistA and RPMS exchanged ideas and software repeatedly over the years, and RPMS periodically folded back into its code base new versions of the VA VistA packages. These two dialects are therefore the most closely related. The Musti software drifted further away from these two but retained compatibility with the infrastructure of RPMS and VA VistA (while adding additional GUI and web capabilities to improve function). Meanwhile, the CHCS code base diverged from that of the VA's VistA in the mid-eighties and has never been reintegrated. The VA and the DoD had been instructed for years to improve the sharing of medical information between the two systems, but for political reasons made little progress toward bringing the two dialects back together. More recently, CHCS's development was brought to a complete stop by continued political opposition within the DoD, and it has now been supplanted by a related, but different, system called AHLTA. While AHLTA is the new system for DoD, the core systems beneath AHLTA (for Computerized Physician Order Entry, appointing, referral management, and creation of new patient registrations) remain those of the underlying CHCS system. (While some ongoing development has occurred for CHCS, the majority of funds are consumed by the AHLTA project.) Thus, the VistA code base was split four ways.
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VistA Many VistA professionals then informally banded together as the "Hardhats" (a name the original VistA programmers used for themselves) to promote that the FOIA (Freedom of Information Act) release of VA VistA (that allows it to be in the public domain) be standardized for universal usage. WorldVistA was formed from this group and was incorporated in March 2003 as a non-profit corporation. This allowed the WorldVistA board of directors to pursue certain activities (obtaining grants, creating contracts, and making formal alliances) that they otherwise could not pursue as an informal organization. It is, however, an organization independent of the VA system and its version of VistA therefore differs from that of the VA's. Nevertheless, it maintains as an objective that its public version be compatible (interoperable) with the VA's official version. It has developed packages of WorldVistA [18] for multiple operating systems, including Linux (Debian/Ubuntu and Red Hat) -based and Microsoft Windows-based operating systems. Co-operation with the maintainers and vendors of OpenVistA [19], another widely deployed open source public version of VistA, helps maintain interoperability and a standardized framework. In 2011 the Open Source Electronic Health Record Agent (OSEHRA) project was started (in cooperation with the Department of Veterans Affairs) to provide a common code repository for VistA (and other EHR and health IT) software. Therefore, it is through the joint achievement of thousands of clinicians and professional systems experts from the United States and other nations that the VistA system has developed, many of them volunteers.
Supporters of VistA There have been many champions of VistA as the electronic healthcare record system for a universal healthcare plan. VistA can act as a standalone system, allowing self-contained management and retention of healthcare data within an institution. Combined with BHIE (or other data exchange protocol) it can be part of a peer-to-peer model of universal healthcare. It is also scalable to be used as a centralized system (allowing regional or even national centralization of healthcare records). It is, therefore, the electronic records system most adaptable to a variety of healthcare models. In addition to the unwavering support of congressional representatives such as Congressman Sonny Montgomery of Mississippi, numerous IT specialists, physicians, and other healthcare professionals have donated significant amounts of time in adapting the VistA system for use in non-governmental healthcare settings. The ranking member of the House Veterans Affairs Committee's Oversight and Investigation Subcommittee, Rep. Ginny Brown-Waite of Florida, recommended that the Department of Defense (DOD) adopt VA's VistA system following accusations of inefficiencies in the DOD healthcare system. The DOD hospitals use Armed Forces Health Longitudinal Technology Application (AHLTA) which has not been as successful as VistA and has not been adapted to non-military environments (as has been done with VistA). In November 2005, the U.S. Senate passed the Wired for Health Care Quality Act, introduced by Sen. Enzi of Wyoming with 38 co-sponsors, that would require the government to use the VA's technology standards as a basis for national standards allowing all health care providers to communicate with each other as part of a nationwide health information exchange. The legislation would also authorize $280 million in grants, which would help persuade reluctant providers to invest in the new technology. There has been no action on the bill since December 2005. Two similar House bills were introduced in late 2005 and early 2006; no action has been taken on either of them, either. In late 2008, House Ways and Means Health Subcommittee Chair Congressman Pete Stark (D-CA) introduced the Health-e Information Technology Act of 2008 (H.R. 6898) that calls for the creation of a low-cost public IT system for those providers who do not want to invest in a proprietary one. In April 2009, Sen. John D. Rockefeller of West Virginia introduced the Health Information Technology Public Utility Act of 2009 calling for the government to create an open source electronic health records solution and offer it
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at little or no cost to safety-net hospitals and small rural providers.
VistA Derivatives • Astronaut VistA [20] – an installer suite for different versions of VistA, with multiple enhancements and bug fixes. • WorldVistA or WorldVistA EHR [21] • OpenVistA [22] (Medsphere) • vxVistA [23] (Document Storage Systems, Inc.) An effort has been made by the Astronaut team, WorldVistA team, members of the VistA Software Alliance, and the OSEHRA to standardize structure between the platform derivatives to allow for future interoperability, as part of the vision for a national healthcare network record system.[citation needed]
References [1] [2] [3] [4]
VA Testimony of Roger Baker before Congress on July 14, 2009 – Congressional and Legislative Affairs HIMSS Analytics Stage 7 Hospitals http:/ / www. esiobjects. org/ http:/ / vistapedia. net/ index. php?title=M2Web_Overview
[5] http:/ / gradvs1. mgateway. com/ main/ index. html [6] http:/ / www. myhealth. va. gov/ [7] http:/ / www. ehealth. va. gov/ EHEALTH/ Requesting_VistA_Software. asp [8] http:/ / www1. va. gov/ vadodhealthitsharing/ page. cfm?pg=9 [9] http:/ / www. bluecliffinc. com [10] http:/ / www. docstorsys. com [11] http:/ / www. medsphere. com/ [12] http:/ / sequencemanagers. com [13] Implemented in 1992 at the Berlin Heart Institute, VistA is still in use there. [14] http:/ / www. hhs. gov/ healthit/ onc/ mission/ [15] http:/ / www. navapd. org/ [16] http:/ / www. ihs. gov/ CIO/ EHR/ index. cfm [17] http:/ / messi. uku. fi/ tike/ his/ english/ musti. html [18] http:/ / sourceforge. net/ projects/ worldvista/ files/ [19] http:/ / medsphere. com [20] http:/ / astronautvista. com/ [21] http:/ / www. worldvista. org [22] http:/ / medsphere. com/ [23] http:/ / www. docstorsys. com/ index. htm
External links • Vistapedia: the WorldVistA Wiki (http://vistapedia.net/index.php?title=Main_Page) • Hardhats (http://www.hardhats.org) – a VistA user community • Hardhats Google Group (http://groups.google.com/group/Hardhats/) – a forum to discuss installation of WorldVistA • VISTA Monograph (http://www.va.gov/vista_monograph/) (Veterans Administration) • VistA Monograph wiki (http://wiki.laptop.org/go/VistA_Monograph_Wiki) (OLPC project) • VistA Software Alliance (http://www.vistasoftware.org) (VistA Software Vendor Trade Organization) • OSEHRA (http://www.osehra.org) – Open Source Electronic Health Record Agent, a code repository for VistA • VistA / CPRS Demo site (http://www.va.gov/cprsdemo/) (Department of Veterans Affairs) • VA VistA software FTP site (http://www1.va.gov/CPRSdemo/page.cfm?pg=1) (Department of Veterans Affairs) • The Pacific Telehealth & Technology Hui (http://www.pacifichui.org/)
VistA • VistA Imaging overview (http://www1.va.gov/imaging/page.cfm?pg=3) (Department of Veterans Affairs) • BHIE (http://www1.va.gov/VADODHEALTHITSHARING/Bidirectional_Health_Information_Exchange. asp) – Bidirectional Health Information Exchange protocols of the Department of Veterans Affairs • Why is VistA good? (http://www.fredtrotter.com/2007/11/10/ why-is-vista-good-the-vista-open-source-development-model/) • "Innovations Award" (http://www.ashinstitute.harvard.edu/Ash/pdfs/VAVistAreleasefinale.pdf) (PDF). Retrieved July 25, 2006. – Ash Institute News Release • VistA Glossary (http://liutiu.narod.ru/VistA_Glossary.htm) LiuTiu Medical Administrative Lexicon (Brokenly translated into English from Russian) • Ubuntu Doctors Guild (http://ubuntudoctorsguild.dyndns.org/public/index.php/ Main_Page#Electronic_Medical_Records_.28EMRs.2FEHRs.29) Information about implementing VistA and other open source medical applications in Ubuntu Linux Videos about VistA: • VistA at the VA (http://www1.va.gov/vha_oi/docs/vha_intel_chime.asx) • History of Vista Architecture (http://blip.tv/file/405389) Interview with Tom Munnecke • Interview with Rob Kolodner (http://blip.tv/file/1187881) regarding VistA's potential for the National Health Information Network • • • • •
Impact of VistA (http://blip.tv/file/1187881) Interview with Dr. Ross Fletcher Interview with Philip Longman (http://blip.tv/file/1188770) Events leading up to the development of VistA (http://blip.tv/file/1187514/) Interview with Henry Heffernan History of Vista (http://blip.tv/file/1187241/) Interview with Ruth Dayhoff Early development of the Decentralized Hospital Computer Program (http://blip.tv/file/1186772/) Interview with Marty Johnson • Early days of the VA "Underground Railroad" (http://blip.tv/file/1188922/) Interview with Tom Munnecke
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VistA imaging
VistA imaging VistA Imaging is an FDA-approved Image Management system used in the Department of Veterans Affairs healthcare facilities nationwide. It is one of the most widely used image management systems in routine healthcare use, and is used to manage many different varieties of images associated with a patient's medical record.
Hardware requirements The VistA Imaging System uses hardware components to provide short- and long-term storage. It takes advantage of network servers for storage. It uses a DICOM Sample patient record view from VistA Imaging gateway system to communicate with commercial Picture Archiving and Communication Systems (PACS) and modalities such as CT, MR, and Computed Radiography (x-ray) devices for image capture. It utilizes a background processor for moving the images to the proper storage device and for managing storage space.
Types of data managed The system not only manages radiologic images, but also is able to capture and manage EKGs, pathology images, gastroenterology (endoscopic) images, laparoscopic images, scanned paperwork, or essentially any type of health care image.
Integration with Electronic Health Record systems VistA Imaging is currently integrated into the VistA EMR (electronic medical record) system used nationwide in Department of Veterans Affairs hospitals. This integration is able to provide increased efficiency of retrieval of images. It has also been used as a separate software package and can be used with EHRs other than VistA. VistA Imaging now connects to a nationwide backbone that allows clinicians to access the 350 million images stored in the VA system via Remote Image View software. The VA has developed interfaces for more than 250 medical devices in VistA Imaging, the images from which can be accessed through the desktop VistA Imaging Viewer. The Department of Defense will use the VistA Imaging Viewer to enhance its own EHR.
Usage in a National Network of Healthcare Records As part of the US national mandate to co-ordinate care between Department of Defense and the VA, VistA Imaging is forming a cornerstone of the effort to exchange medical imagery between the two systems. “When soldiers come back from Iraq and Afghanistan and eventually enter the VA system, images will be able to move from DOD to VA seamlessly." Eventually, DOD and VA should be able to share all image file types from all sites. Additional enhancements to VistA Imaging include development of a central archive for all VA images (whether acquired through VistA or a commercial system) and new indexing and search capabilities.
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Availability The software for VistA Imaging has been made available through the Freedom of Information Act so that it is in the public domain. Due to its designation as a medical device, however, it can not be designated as free open source software and therefore can not be altered or implemented without FDA approval. Although it can be used in healthcare facilities that are outside the Department of Veterans Affairs, this is possible only if the proprietary modules that have been integrated into it are also licensed and implementation is registered with the FDA. This has effectively limited its use to government institutions who have licensed the proprietary modules. It is nominally available through the Department of Veterans Affairs software request $15.
[1]
office for a fee of around
Proprietary modules required VistA Imaging uses proprietary modules not in the public domain. This makes its public domain use limited.
Open Source Alternative compatible with VistA A multi-platform open source product O3-DPACS [2] (“Open Three ( O3 ) - Data & Picture Archiving and Communication System”) is a Java-based alternative for the open-source VistA implementations that is likely to fulfill the same functions provided by VistA Imaging within the VA.
Information retrieval after a natural disaster The VistA Imaging system was robust enough to be restored after Hurricane Katrina damaged the data facility at the New Orleans VA. This type of backup proved superior to a paper record system.
References [1] http:/ / www1. va. gov/ VHA_OI/ page. cfm?pg=37 [2] http:/ / sourceforge. net/ projects/ o3-dpacs
External links • "VistA Imaging overview" (http://www.va.gov/IMAGING/overview.asp). Department of Veterans Affairs. Retrieved July 16, 2010. • "Capture devices approved for use with VistA Imaging (IHS intranet)" (http://vhacollaboration.ihs.gov/ VistAImaging/Shared Documents/VistA Imaging Training Documentation/ Approved_Clinical_Capture_Devices.pdf). March 2007. • "DICOM devices approved for use with VistA Imaging" (http://www1.va.gov/imaging/docs/ VistA_Imaging_DICOM_Modality_Interfaces.pdf). • "VistA Imaging in the IHS RPMS EHR" (http://www.ihs.gov/vistaimaging/). Nov 2009.
VistA Web
VistA Web VistAWeb is a portal accessible through CPRS (Computerized Patient Recordkeeping System), the graphical user interface for the Veterans Health Information Systems and Technology Architecture (VistA), the electronic health record used throughout the United States Department of Veterans Affairs (VA) medical system (known as the Veterans Health Administration (VHA)). This portal has been implemented throughout the VA system, allowing healthcare providers at remote VA facilities to view records contained within the VistA electronic health record system at the patient's primary facility. As originally created, the VA health systems has 21 regional data systems (VISN), each with some differences in data collection and storage. The usage of VistA throughout the VA has helped to standardize records, but there has until recently not been an easy way for accessing records from a different VISN service area. If a patient travels or is injured or sick and visits a VA facility far from their home, VistAWeb will allow the physician or other healthcare provider access to the records at that patient's home institution.
Introduction Veterans Health Information Systems and Technology Architecture (VistA) VistAWeb is an intranet web application used to review remote patient information found in VistA, the Federal Health Information Exchange (FHIE) system, and the Health Data Repository (HDR) databases. To a large extent, VistAWeb mirrors the reports behavior of the Computerized Patient Record System (CPRS) and Remote Data View (RDV). However, by permitting a more robust and timely retrieval of remote-site patient data, VistAWeb is also an enhancement to CPRS/RDV. There are three ways to access VistAWeb. VistAWeb can be made available by adding it to the CPRS Tools Menu, and it can be selected as the default method of retrieving data from the Remote Data Available button in CPRS. These two methods are referred to as CPRS-spawned versions of VistAWeb. They are compliant with the Health Level 7 (HL7) Clinical Context Object Workgroup (CCOW) standards and therefore maintain context with the patient selected in CPRS. As a third option, VistAWeb can be accessed in a standalone mode from its website at https://vistaweb.med.va.gov/ [1]. The standalone version of VistAWeb is connected to neither CPRS nor the clinical context management application. Standalone VistAWeb serves an important function for users who have been granted special access to multiple sites, such as for National Programs, Veterans Administration (VA) researchers, and others.
Usage for record access following natural disaster VistAWeb was also made available broadly, though temporarily, to assist clinical staff with the retrieval of patient information from the sites affected by damage caused by hurricane Katrina.
Usage for record access from DoD VistAWeb was also expanded for to access patient records from the DoD AHLTA patient record system in 2009. This relied on mapping data from AHLTA via a bidirectional data exchange system to VistA. Due to errors in the bidirectional exchange system, erroneous data was transmitted to the VA, causing this remote viewer function from DoD sites to be closed.
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Availability The VistAWeb package is distributed in the public domain as a module of the VistA software package under the Freedom of Information Act (FOIA). It is therefore available from the VA FTP site, from the VA software distribution office, and in bundled packages. Its capabilities, therefore, can be achieved by a healthcare institution that installs the VistA electronic health record.
References [1] https:/ / vistaweb. med. va. gov/
External links • "VISTA Monograph" (http://www.va.gov/vista_monograph/). Veterans Administration. • "VistA / CPRS Demo site" (http://www1.va.gov/cprsdemo/). (Department of Veterans Affairs) • "VA VistA software FTP site" (http://www1.va.gov/CPRSdemo/page.cfm?pg=1). (Department of Veterans Affairs) • "VistA Software Alliance" (http://www.vistasoftware.org). (VistA Software Vendor Trade Organization) • "The Pacific Telehealth & Technology Hui" (http://www.pacifichui.org/). • • • •
"WorldVistA" (http://www.worldvista.org). - Home of the WorldVistA EHR "WorldVistA Wiki" (http://openforum.worldvista.org/~forum/index.php?title=Main_Page). "Hardhats" (http://www.hardhats.org). - a VistA user community "Innovations Award" (http://www.ashinstitute.harvard.edu/Ash/pdfs/VAVistAreleasefinale.pdf). Retrieved July 25, 2006. - Ash Institute News Release • "Sequence Managers Software" (http://www.sequencemanagers.com/). Retrieved June 11, 2007.—provides a VistA-based EMR package, based in Raleigh, North Carolina • "VistA Imaging overview" (http://www1.va.gov/imaging/page.cfm?pg=3). Department of Veterans Affairs. • "BHIE" (http://www1.va.gov/vadodhealthitsharing/page.cfm?pg=14). Bidirectional Health Information Exchange protocols of the Department of Veterans Affairs
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WorldVistA
WorldVistA WorldVistA is an open source implementation of the Veteran Administration's Electronic Health Record system intended for use in health care facilities outside the VA.
Background The US Veterans Administration developed the most widely distributed electronic health record used in the US, the Veterans Health Information Systems and Technology Architecture (VistA). In an effort to make the system widely available to institutions outside the Veterans Administration health system, the software code was placed in the Public Domain under the Freedom of Information Act. The foundation for the WorldVistA EHR was formed to extend and collaboratively improve the VistA electronic health record and health information system for use outside of its original setting. It was originally developed as part of the VistA-Office project, a collaborative effort funded by the United States Centers for Medicare and Medicaid Services (CMS), an agency of the US Department of Health and Human Services (DHHS). WorldVistA EHR VOE/ 1.0 is based on and compatible with the U.S. Department of Veterans Affairs (VA) world renowned EHR, Veterans Health Information Systems and Technology Architecture (VistA). A fully open-source (GPL v2 licensed) project, WorldVistA has also developed software modules (such as pediatrics, obstetrics, and other functions) not used in the veterans' healthcare setting. In 2006, WorldVistA EHR VOE/ 1.0 was the only open source EHR that met Certification Commission for Healthcare Information Technology (CCHITSM) ambulatory electronic health record (EHR) criteria, and in January 2008, it was released with full CCHITSM EHR. As a free product developed in co-operation with the US government, WorldVistA is not marketed in a similar fashion to commercial EHRs.
Core VistA functions • • • • •
patient registration clinical reminders for chronic disease management clinical order entry progress note templates results reporting
Customizable functions The structure of WorldVistA is modular, and a wide variety of customization is possible. Because it is fully open source, this can be done without restriction (although CCHIT certification is granted only to the officially maintained package). • • • • • •
ability to interface to existing practice management / billing systems, lab services and other applications scanning and inclusion of scanned documents into the medical record prescription finishing and faxing clinical quality measure reporting capabilities support for disease management, using clinical reminders templates for obstetrics/gynecology (OB/GYN) and pediatrics care
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Server platforms For Linux-based servers, WorldVistA server uses the (free open source) Fidelity GT.M MUMPS database, available as an integrated package along with WorldVistA Server. This software is part of the VistA Public Domain software, and does not require licensing. For Windows-based servers, WorldVistA can be implemented used the commercial Caché MUMPS database, which requires a database license and software from Intersystems Corporation. For Mac OS-X-based servers, a development effort to port GT.M (and the Server software) to that platform has begun.
Client platforms The Client software is an implementation of CPRS, which is Windows-based. This allows Windows terminals to access the central server database. This software is part of the VistA Public Domain software, and does not require licensing. For Linux terminals, CPRS can be run as a Wine package or from within a virtual machine. A separate (Windows-based) module is available to capture and view vital signs as well as graphing of other clinical data. This is meant to be used on client terminals. A separate (Windows-based) module allows the scanning, capture and integration of paper documents as part of an individuals medical record. It can also be used to add a variety of non-diagnostic quality images to the medical record. This is meant to be used on client terminals.
Development history WorldVistA is developed by a series of physicians (and other medical professionals) and software professionals that donate their efforts as volunteers. This group loosely referred to themselves as Hardhats (and continues to do so) before the name of the project was officially changed to WorldVistA. WorldVistA has developed and distributes a "toaster" version of VistA, which is a self-contained software package [1] that integrates both the MUMPS database (GT.M version) and the VistA software. In 2009, the self-installing Linux toaster version was enhanced with a GUI-based patient registration module, web interface, and other enhancements, and incorporated into a self-installing package for both Debian/Ubuntu and Red Hat Linux. This freely available version of WorldVistA is known as Astronaut VistA [20]. This version is packaged with both an enhanced GUI as well as a web interface (which allows connection through an intranet or through the Internet). An introduction to this package is here in a PDF slide presentation [2]. A similar package for Windows-based servers is in alpha (early development) stage.
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Grants opportunities to help further development • NIH grants [3] -- Supported by the American Recovery & Reinvestment Act of 2009 (ARRA), NIH grants can be used efforts to further technology that advances the goals of the NIH • Robert Woods Johnson Foundation [4] -- This foundation has a goal of helping integrate personal access to electronic health records nationwide, similar to the VA's HealtheVet [5] project.
Adoption • Clinica Adelante [6] implemented the open source version of VistA for its 30,000 member community health clinic, and has testified before the US Congress [7] regarding its success. • Oroville Hospital [8] is installing the WorldVistA version into a co-ordinated hospital and clinic system.
References [1] http:/ / sourceforge. net/ projects/ worldvista [2] http:/ / worldvista. org/ conference_presentations/ 19th-vista-community-meeting/ AstroVistAInstaller_VCM_06_18_2009. pdf/ at_download/ file [3] http:/ / grants. nih. gov/ recovery/ [4] http:/ / www. rwjf. org/ applications/ solicited/ cfp. jsp?ID=20762 [5] [6] [7] [8]
http:/ / www. health-evet. va. gov/ http:/ / www. clinicaadelante. com/ http:/ / waysandmeans. house. gov/ hearings. asp?formmode=printfriendly& id=7233 http:/ / www. orovillehospital. com
External links • • • •
"WorldVistA" (http://www.worldvista.org). - Home of the WorldVistA EHR "Vistapedia: the WorldVistA Wiki" (http://vistapedia.net/index.php?title=Main_Page). "Hardhats" (http://www.hardhats.org). - a WorldVistA user community "Astronaut WorldVistA on Ubuntu" (http://ubuntudoctorsguild.dyndns.org/public/index.php/ Astronaut_VistA). - Tips for installation of the Astronaut WorldVistA server on Ubuntu/Kubuntu Linux (from Ubuntu Doctors Guild)
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ZEPRS The Zambia Electronic Perinatal Record System (ZEPRS) is an Electronic Medical Record (EMR) system used by public obstetric clinics and a hospital (the University Teaching Hospital) in Lusaka, Zambia.
Background In early 2001 a team of physicians at the University of Alabama at Birmingham (UAB) and visiting Zambian physician Dr. Moses Sinkala conceived the idea based on a successful electronic perinatal record system that the UAB had developed and proven in clinics in Birmingham. in July 2001 the Bill & Melinda Gates Foundation awarded a grant to the UAB to develop a similar system to serve public obstetric clinics in Lusaka, Zambia.[1] The UAB team solicited proposals for the technical design and implementation of the system from several private sector information technology firms, including Electronic Data Systems, before awarding a contract to RTI International [2] (RTI), a large 501(c)3 not-for-profit research organization based in Research Triangle Park, North Carolina. Other project partners included the Center for Infectious Disease Research in Zambia (CIDRZ) and the Lusaka District Health Management Team of the Zambian Central Board of Health [3].
Objectives ZEPRS was designed to improve maternal and perinatal outcomes by: 1. Improving perinatal care for women and postnatal care for neonates by: 1. Promoting adherence to good standard of care practices 2. Identifying and document potential medical/ obstetrical problems so that effective therapies can be administered 3. Improving communication and referrals among providers 4. Enhancing monitoring and evaluation of outcomes, clinics, and providers 2. Improving efficiency, completeness, accuracy of documentation and reporting
Major components ZEPRS achieves these objectives by providing the following: • • • • • •
An electronic patient record system with patient record database shared among facilities A system that guides clinicians through the Zambian standard of care Intelligent rules that alert clinicians to problems and recommend patient referral when appropriate Standard & ad hoc reporting for supportive supervision, surveillance, and analysis An electronic-first system used by clinicians during the course of patient care An electronic referral system to improve the efficiency and effectiveness of referrals
ZEPRS components include a high-speed point-to-point voice and data wireless network [4] that interconnects facilities; wired and wireless networks within facilities; a data center managed by CIDRZ; and an electronic perinatal record system with integrated patient referral system. ZEPRS uses a tabbed user interface and role-based access control system to enable several clinicians to share the same computer to retrieve and enter data for different patients.
ZEPRS
Software architecture ZEPRS has been developed using the Java programming language. Other technologies used include AJAX, Quartz [5] , MySQL, and others. ZEPRS has its own content management system called DynaSite, that makes it easy to add forms, fields, form flows, and business rules without programming. RTI has developed a locally installable version of the software using the Eclipse Rich Client Platform (RCP) and the Apache Derby embedded database. This version uses the free and open source zcore [6] platform, which uses an implementation of RSS to transmit data over intermittent networks, such as mobile phone networks.
Development history RTI worked with South African firm Communications Solutions [7] (Comsol) to conduct a wireless site survey of all facilities, and to design a line-of-sight wireless network to connect them. By March 2003 this network was operational. Before the end of 2003 RTI had worked with Comsol to add Voice over Internet Protocol (VoIP) to provide voice communication between facilities, and had extended the network to connect multiple buildings in clinic compounds and to multiple wards of the University Teaching Hospital. Three to nine networked PCs and one laser printer were installed in each clinic, as well as additional PCs and printers in the University Teaching Hospital and administrative offices. The ZEPRS data center was installed at CIDRZ, which housed the combined ZEPRS technical support team. Approximately 800 clinicians, many of whom had never used a computer, were trained in basic computer literacy. Standard clinical protocol reference works and email were made available to clinicians electronically over the ZEPRS network. By early May 2004 RTI had completed an open source electronic patient referral system. This was launched into production use in clinics on 22 June 2004. Clinicians adopted this component readily and the system was soon rolled out to all 24 clinics and the University Teaching Hospital. Also in May 2004, CIDRZ asked RTI whether the ZEPRS software could be adapted to help in managing antiretroviral therapy (ART) to treat patients infected with the Human immunodeficiency virus (HIV). Version 1.0 of this software, the ART Patient Tracking System (ART/PTS), was launched into use at Kalingalinga Clinic on 1 June 2004. RTI reviewed the completed Beta version of the fully integrated perinatal record system with the UAB team in July 2005, and released Version 1.0 on 21 November 2005. This version was pilot tested in a limited number of clinics in Lusaka from December 2005 through January 2006. ZEPRS was launched into production use in two clinics in February 2006. The system was rolled out to all clinics at the average rate of one clinic each month until the roll out was completed in August 2007.
Current status Since roll out was completed in August 2007, ZEPRS has been used by 24 public obstetrics clinics, as well as six wards (blocks A-D, adult ARV ward, and pediatric ARV ward) within the University Teaching Hospital in Lusaka. The ZEPRS patient record database has been used since February 2006 for routine health surveillance, monitoring, and supervision of health care. A 2006 study cited the use of a ZEPRS-based patient record system as one of four key factors in the successful rapid scale-up of antiretroviral therapy at primary care sites in Zambia. From 1 June 2007, to 31 January 2010, 115 552 pregnant women had prenatal and delivery information recorded in ZEPRS. By September 2011 ZEPRS contained medical records for more than 510,000 patients.[8] In 2011 an average of more than 8,000 new antenatal patients are being registered in the system each month. Clinicians in 25 facilities have used ZEPRS during more than 5,000,000 patient encounters to manage perinatal care.
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ZEPRS ZEPRS links multiple pregnancies of the same mother to the mother's record. ZEPRS had been used to manage multiple pregnancies for more than 20,000 mothers. In 2011 ZEPRS has been used by 278 individual users to help care for perinatal patients and analyze related health care data in Lusaka. Information regarding the impact of ZEPRS on the quality of health care and health outcomes can be obtained by contacting the Center for Infectious Disease Research in Zambia or the Zambian Central Board of Health.
Ongoing development Since ZEPRS was launched in February 2006 it has been continually refined and enhanced based on input from clinicians and emerging needs. In 2008 RTI worked with CIDRZ to implement an interface with the Laboratory Information Management System (LMIS) at Kalingalinga Clinic, enabling lab test results such as CD4 counts to be transferred to patient records automatically twice daily. RTI has developed a version of the software that can be installed locally, be used within a facility in the absence of network connectivity, and can synchronize records automatically when connectivity is detected. This version is based on the free and open source Zcore [9] platform, which can transmit data automatically over intermittent networks, such as mobile phone networks.
Adaptability The Zcore software platform that emerged from ZEPRS has been used for managing pharmaceutical supplies [10] in health centers in Nairobi, Kenya, managing malaria indoor residual spraying operations [11] in Kenya, and managing medical and legal care for rape survivors [12] in South Africa.
Availability ZEPRS has been released by RTI [13] as free and open source software under the Apache Software License (Version 2.0). RTI has released ZEPRS documentation under a Creative Commons Attribution-NonCommercial-ShareAlike 2.5 License.
Footnotes [1] 1 (http:/ / www. gatesfoundation. org/ GlobalHealth/ Pri_Diseases/ ReproductiveMaternalHealth/ Announcements/ Announce-403. htm) US-Zambian Collaboration Receives $4 Million To Establish Electronic Obstetric And Newborn Medical Record In Lusaka, Zambia [2] http:/ / www. rti. org [3] http:/ / www. cboh. gov. zm/ [4] http:/ / www. rtidemo. org/ front/ node/ 160 [5] http:/ / www. opensymphony. com/ quartz/ [6] http:/ / www. rtidemo. org/ front/ zcore [7] http:/ / www. comsol. co. za/ [8] 3 (http:/ / www. ictedge. org) Data based on queries of the ZEPRS database performed on 9 September 2011. [9] http:/ / www. ictedge. org/ zcore [10] http:/ / www. ictedge. org/ projects/ wrKenya [11] http:/ / www. ictedge. org/ projects/ irsa [12] http:/ / www. ictedge. org/ projects/ rape_case_mgmt [13] http:/ / www. ictedge. org/ projects/ zeprs
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References • Chi, Benjamin H.; Vwalika, Bellington; Killam, William P.; Wamalume, Chibesa; Giganti, Mark J.; Mbewe, Reuben; Stringer, Elizabeth M.; Chintu, Namwinga T. et al. (2011). "Implementation of the Zambia Electronic Perinatal Record System for comprehensive prenatal and delivery care". International Journal of Gynecology & Obstetrics 113 (2): 131. doi: 10.1016/j.ijgo.2010.11.013 (http://dx.doi.org/10.1016/j.ijgo.2010.11.013). |displayauthors= suggested (help) • Darcy N, Kelley C, Reynolds E, and Cressman G. (2010) An Electronic Patient Referral Application:A Case Study from Zambia (http://www.rti.org/pubs/rr-0011-1003-darcy.pdf), RTI Press, Research Report Series, RTI International. • Morris, Mary B; Chapula, Bushimbwa; Chi, Benjamin H; Mwango, Albert; Chi, Harmony F; Mwanza, Joyce; Manda, Handson; Bolton, Carolyn et al. (2009). "Use of task-shifting to rapidly scale-up HIV treatment services: Experiences from Lusaka, Zambia". BMC Health Services Research 9: 5. doi: 10.1186/1472-6963-9-5 (http://dx. doi.org/10.1186/1472-6963-9-5). PMC 2628658 (http://www.ncbi.nlm.nih.gov/pmc/articles/ PMC2628658). PMID 19134202 (http://www.ncbi.nlm.nih.gov/pubmed/19134202). |displayauthors= suggested (help) • Cressman G, Darcy N, Destefanis P, Kelley C, Reynolds E. (2008) "Can an Electronic Medical Record System Improve Health Care in Lusaka, Zambia?" (http://www.gpphi.org/conferences/PHI2008/images-and-docs/ PHI2008 Abstract List.pdf), Poster Presentation, PHI2008 Conference, Seattle, Washington, USA. • Stergachis A, Keene D, Somani S. (2008) "Informatics for Medicines Management Systems in Resource-Limited Settings" (http://www.ehealth-connection.org/files/conf-materials/Informatics for Medicines Mngmt Systems_0.pdf) Making the eHealth Connection Conference, Bellagio, Italy. • RTI International (2008) ICT Improves Patient Care in Africa: ZEPRS – An Electronic Perinatal Record System in Zambia (http://www.rti.org/page.cfm?nav=370&objectid=09C970A2-AB6E-504A-37FCAF624F0E9927), RTI International Website. • RTI International (2008) "ZEPRS wins Computerworld's Mobile & Wireless World 'Best Practices in Mobile and Wireless' Award for 2008" (http://www.rti.org/newsroom/news.cfm?nav=370& objectid=5A57BC36-50C1-4A2F-A8402DC050533D32), RTI International Press Release. • Computerworld (2008) "2008 'Best Practices in Mobile & Wireless' Awards" (http://www.mwwusa.com/ awards.aspx), Computerworld, Mobile & Wireless World. • Cressman G, Darcy N, Destefanis P, Kelley C, Reynolds E. (2007) "Can an Electronic Medical Record System Improve Health Care in Lusaka, Zambia?" (http://www.aidsimpact.com/2007/Programme/abstract/?id=411), Presentation, AIDSIMPACT 2007, Marseilles, France. • Ajita S, Dipsikha S, Sahajad A. (2006) " e-Health Perspective: Healing touch to health (http://www.i4donline. net/articles/current-article.asp?articleid=629&typ=Features)", i4D Magazine.
External links • ZEPRS project website (http://www.ictedge.org/projects/zeprs) • ZEPRS demonstration website (http://www.ictedge.org:8080/zeprs/home. do;jsessionid=8995E0B989D028BB97973F62671D2197) (Username: demo Password: demo11) • ZEPRS wireless network on Google Earth Community (http://bbs.keyhole.com/ubb/showflat.php/Cat/0/ Number/1238034/an/0/page/0) • ZEPRS overview presentation (http://www.slideboom.com/presentations/19659/ ZEPRS-Electronic-Perinatal-Record-System:-Results-and-Impact)
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Decision Support Applications Clinical decision support system Clinical decision support system (CDSS) is an interactive decision support system (DSS) Computer Software, which is designed to assist physicians and other health professionals with decision making tasks, such as determining diagnosis of patient data. A working definition has been proposed by Robert Hayward of the Centre for Health Evidence; "Clinical Decision Support systems link health observations with health knowledge to influence health choices by clinicians for improved health care". This definition has the advantage of simplifying Clinical Decision Support to a functional concept. It is a major topic of artificial intelligence in medicine.
Role & Characteristics A clinical decision support system has been coined as an “active knowledge systems, which use two or more items of patient data to generate case-specific advice.”[1] This implies that a CDSS is simply a DSS that is focused on using knowledge management in such a way to achieve clinical advice for patient care based on some number of items of patient data.
Purpose/Goal The main purpose of modern CDSS is to assist clinicians at the point of care.[2] This means that a clinician would interact with a CDSS to help determine diagnosis, analysis, etc. of patient data. Previous theories of CDSS were to use the CDSS to literally make decisions for the clinician. The clinician would input the information and wait for the CDSS to output the “right” choice and the clinician would simply act on that output. The new methodology of using CDSS to assist forces the clinician to interact with the CDSS utilizing both the clinician’s knowledge and the CDSS to make a better analysis of the patients data than either human or CDSS could make on their own. Typically the CDSS would make suggestions of outputs or a set of outputs for the clinician to look through and the clinician officially picks useful information and removes erroneous CDSS suggestions. There are two main types of CDSS: • Knowledge-Based • NonKnowledge-Based An example of how a CDSS might be used by a clinician comes from the subset of CDSS (Clinical Decision Support System), DDSS (Diagnosis Decision Support Systems). A DDSS would take the patients data and propose a set of appropriate diagnoses. The doctor then takes the output of the DDSS and figures out which diagnoses are relevant and which are not. Another important classification of a CDSS is based on the timing of its use. Doctors use these systems at point of care to help them as they are dealing with a patient, with the timing of use as either pre-diagnoses, during diagnoses, or post diagnoses.[citation needed] Pre-diagnoses CDSS systems are used to help the physician prepare the diagnoses. CDSS used during diagnoses help review and filter the physician’s preliminary diagnostic choices to improve their final results. And post-diagnoses CDSS systems are used to mine data to derive connections between patients and their past medical history and clinical research to predict future events. It has been claimed that decision support will begin to replace clinicians in common tasks in the future.
Clinical decision support system
Features of a Knowledge-Based CDSS Most CDSS consist of three parts, the knowledge base, inference engine, and mechanism to communicate. The knowledge base contains the rules and associations of compiled data which most often take the form of IF-THEN rules. If this was a system for determining drug interactions, then a rule might be that IF drug X is taken AND drug Y is taken THEN alert user. Using another interface, an advanced user could edit the knowledge base to keep it up to date with new drugs. The inference engine combines the rules from the knowledge base with the patient’s data. The communication mechanism will allow the system to show the results to the user as well as have input into the system.
Features of a non-Knowledge-Based CDSS CDSS’s that do not use a knowledge base use a form of artificial intelligence called machine learning, which allow computers to learn from past experiences and/or find patterns in clinical data. Two types of non-knowledge-based systems are artificial neural networks and genetic algorithms. Artificial neural networks or more generally neural networks use nodes and weighted connections between them to analyze the patterns found in the patient data to derive the associations between the symptoms and a diagnosis. This eliminates the need for writing rules and for expert input. However since the system cannot explain the reason it uses the data the way it does, most clinicians don’t use them for reliability and accountability reasons. Genetic Algorithms are based on simplified evolutionary processes using directed selection to achieve optimal CDSS results. The selection algorithms evaluate components of random sets of solutions to a problem. The solutions that come out on top are then recombined and mutated and run through the process again. This happens over and over until the proper solution is discovered. They are the same as neural networks in that they derive their knowledge from patient data. Non-knowledge-based networks often focus on a narrow list of symptoms like ones for a single disease as opposed to the knowledge based approach which cover many different diseases to diagnosis.
Effectiveness A 2005 systematic review by Garg et al. of 100 studies concluded that CDSs improved practitioner performance in 64% of the studies. The CDSs improved patient outcomes in 13% of the studies. Sustainable CDSs features associated with improved practitioner performance include the following: • automatic electronic prompts rather than requiring user activation of the system Garg et al. concluded that the number and methodologic quality of studies have improved from 1973 through 2004. Another 2005 systematic review (quantitative analysis) of 70 studies by Kawamoto et al. found... "Decision support systems significantly improved clinical practice in 68% of trials." The CDS features associated with success include the following: • the CDSs is integrated into the clinical workflow rather than as a separate log-in or screen. • the CDSs is electronic rather than paper-based templates. • the CDSs provides decision support at the time and location of care rather than prior to or after the patient encounter. • the CDSs provides (active voice) recommendations for care, not just assessments.
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Current U.S. Regulations With the enactment of the American Recovery and Reinvestment Act of 2009 (ARRA), there is a push for widespread adoption of health information technology through the Health Information Technology for Economic and Clinical Health Act (HITECH). Through these initiatives, more hospitals and clinics are integrating Electronic Medical Records (EMRs) and Computerized physician order entry (CPOE) within their health information processing and storage. Consequently, the Institute of Medicine (IOM) promoted usage of health information technology including Clinical Decision Support Systems to advance quality of patient care.[citation needed] The IOM had published a startling report which focused on patient safety crisis in the United States pointing to the incredibly high number of deaths. This statistic gained great attention to the quality of patient care.[citation needed] With the recent enactment of the HITECH Act included in the ARRA, encouraging the adoption of health IT, more detailed case laws for CDSS and EMRs are still being defined by the Office of National Coordinator for Health Information Technology (ONC) and approved by Department of Health and Human Services (HHS). “Meaningful use” definition is yet to be polished. Despite the absence of laws, the CDSS vendors would almost certainly be viewed as having a legal duty of care to both the patients who may adversely be affected due to CDSS usage and the clinicians who may use the technology for patient care. Therefore, the duties of care legal regulations are not explicitly defined yet. With recent effective legislations related to performance shift payment incentives, CDSS are appealing as more attractive.
Challenges to Adoption Clinical Challenges Much effort has been put forth by medical institutions and software companies to produce viable CDSSs to cover all aspects of clinical tasks. However, with the complexity of clinical workflows and the demands on staff time high, care must be taken by the institution deploying the support system to ensure that the system becomes a fluid and integral part of the workflow. To this end CDSSs have met with varying amounts of success, while others suffer from common problems preventing or reducing successful adoption and acceptance. Two sectors of the healthcare domain in which CDSSs have had a large impact are the pharmacy and billing sectors. Pharmacy and prescription ordering systems now do batch-based checking of orders for negative drug interactions and report warnings to the ordering professional. Such systems commonly exist both in clinical settings as well as in more commercial settings, such as in the software used by local or chain pharmacy stores. Another sector of success for CDSS is in billing and claims filing. Since many hospitals rely on Medicare reimbursements to maintain their operational status, systems have been created to help examine both a proposed treatment plan and the current rules of Medicare in order to suggest a plan that attempts to maximize both the care of the patient and the financial needs of the institution. Other CDSSs that are aimed at the diagnostic tasks have found success but are often very limited in deployment and scope. The Leeds Abdominal Pain System went operational in 1971 for the University of Leeds hospital, and found fantastic levels of success where the CDSS produced a correct diagnosis 91.8% of cases compared to the clinicians’ rating of 79.6%. Despite the wide range of efforts by institutions to produce and use these systems, widespread adoption and acceptance has still not yet been achieved for most offerings. One large roadblock to acceptance is workflow integration. A tendency to focus only on the functional decision making core of the CDSS exists, causing a deficiency in planning for how the clinician will actually use the product in situ. Often these systems are stand-alone applications, requiring the clinician to cease working on their current report system, switch to the CDSS, input the necessary data, and receive the information. These additional steps break the flow from the clinician’s perspective
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Clinical decision support system and cost precious time.
Technical Challenges & Barriers to Implementation Clinical decision support systems face steep technical challenges in a number of areas. Biological systems are profoundly complicated, and a clinical decision may utilize an enormous range of potentially relevant data. For example, an electronic evidence-based medicine system may potentially consider a patient’s symptoms, medical history, family history and genetics, as well as historical and geographical trends of disease occurrence, and published clinical data on medicinal effectiveness when recommending a patient’s course of treatment. Clinically, a large deterrent to CDSS acceptance is workflow integration. Inclination to focus only on functional decision making core of the CDSS causes a deficient plan on how the clinician will actually utilize the system in situations. Generally extra steps are required of the clinician which then causes a disruption in workflow affecting efficiency. Generally these systems are stand-alone applications which are not integrated with existing healthcare systems, the clinical user must stop work on the current system, switch to the CDSS, and reenter data necessary into the CDSS that may already exist in another electronic system. Another source of contention with many medical support systems produces mass amounts of alert. When systems produce high volume of warnings (especially those that do not require escalation), aside from the annoyance, clinicians may pay less attention to warnings, causing potentially critical alerts to be missed. Maintenance One of the core challenges facing CDSS is difficulty in incorporating the extensive quantity of clinical research being published on an ongoing basis. In a given year, tens of thousands of clinical trials are published. Currently, each one of these studies must be manually read, evaluated for scientific legitimacy, and incorporated into the CDSS in an accurate way. In addition to being laborious, integration of new data can sometimes be difficult to quantify or incorporate into the existing decision support schema, particularly in instances where different clinical papers may appear conflicting. Properly resolving these sorts of discrepancies is often the subject of clinical papers itself (see meta-analysis), which often take months to complete. Evaluation In order for a CDSS to offer value, it must demonstrably improve clinical workflow or outcome. Evaluation of CDSS is the process of quantifying its value to improve a system’s quality and measure its effectiveness. Because different CDSSs serve different purposes, there is no generic metric which applies to all such systems; however, attributes such as consistency (with itself, and with experts) often apply across a wide spectrum of systems. The evaluation benchmark for a CDSS depends on the system’s goal: for example, a diagnostic decision support system may be rated based upon the consistency and accuracy of its classification of disease (as compared to physicians or other decision support systems). An evidence-based medicine system might be rated based upon a high incidence of patient improvement, or higher financial reimbursement for care providers.
Electronic Health Records and CDSS Implementing Electronic Health Records (EHR) was always going to be an inevitable challenge. The reasons behind this challenge is that it is a relatively uncharted area as it is something that has never been done before, thus there is; and will be many issues and complications during the implementation phase of an EHR. This can be seen throughout the numerous studies that have been undertaken. Challenges in implementing electronic health records (EHRs) have received some attention, but less is known about the process of transitioning from legacy EHRs to newer systems. With all of this said, electronic health records are the way of the future for healthcare industry. It is a way to capture and utilise real-time data to provide high-quality patient care, ensuring efficiency and effective use of time and
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Clinical decision support system resources. By incorporating EHR and CDSS it has the potential to change the way medicine has been taught and practiced. As it is said that, “the highest level of the EHR is a CDSS”. Since “clinical decision support systems (CDSS) are computer systems designed to impact clinician decision making about individual patients at the point in time that these decisions are made”, the reasons can be seen why it would be beneficial to have a fully integrated CDSS and EHR. Even though the benefits can be seen, to fully implement a CDSS within an EHR, it will require significant planning by the healthcare facility/organisation, in order for the purpose of the CDSS to be successful and effective. The success and effectiveness can be measured by the increase in patient care being delivered and reduced adverse events occurring. In addition to this, there would be a saving of time, resources, autonomy and financial benefits to the healthcare facility/organisation
Benefits of CDSS and EHR There has always been errors that occur within the healthcare industry, thus trying to minimise them as much as possible in order to provide quality patient care. Three areas that can be addressed with the implementation of CDSS and Electronic Health Records (EHRs), are: 1. Medical error 2. Medication error 3. Adverse drug events CDSS will be most beneficial once the healthcare facility is 100% electronic thus simplifying the number of modifications that have to occur to ensure that all the systems are up to date.
Barriers to CDSS and EHR Implementing electronic health records (EHR) in healthcare settings incurs challenges; none more important than maintaining efficiency and safety during rollout but in order for the implementation process to occur effectively, an understanding of the EHR users’ perspectives is key to the success of EHR implementation projects. In addition to, adoption needs to be actively fostered through a bottom-up, clinical- needs-first approach. This can be said for CDSS too. The main barriers associated with CDSS and EHRs consist of feasibility (cost), poor usability/ integration, uniformity, clinician non-acceptance, alert desensitisation, as well as the key fields of data entry that need to be addressed when implementing a CDSS to avoid potential adverse events from occurring. These include: → Correct data is being used → All the data has been implemented → Current best practice → Evidence based The main areas of concern with moving into a fully integrated EHR system are: 1. Privacy 2. Confidentiality 3. User-friendliness 4. Document accuracy and completeness 5. Integration 6. Uniformity 7. Acceptance 8. Alert desensitisation
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Australia’s Status Current stage of progress with EHR especially in Australia, majority of the healthcare facilities is still completely paper-based form, and some are in the transition phase of a form of EHR with either already implemented scanned-EHR or are in the process of converting to the scanned EHRs. The process of gathering clinical data and medical knowledge and putting them into a form that computers can manipulate to assist in clinical decision-support is still in its infancy. Nonetheless there is great potential once EHRs are implemented, taking on board the key areas of concern and the associated barriers, it will allow for successful integration of CDSS and EHR to provide best practice, high quality care to the patient, which is the ultimate goal of healthcare. In saying this, Victoria has attempted to implement EHR across the state with the HealthSMART program, but due to financial costs it has cancelled the project. South Australia (SA) however is slightly more successful then Victoria in the implementation of an EHR. This maybe due to all public healthcare organisations being centrally run. SA is in the process of implementing “Enterprise Patient Administration System (EPAS)”. This system is the foundation for all public hospitals and health care sites for an EHR within SA and it is expected that the end of 2014 will have all facilities connected. This will allow for successful integration of CDSS into SA and increase the benefits of the EHR.
Methodological Basis of CDSS There are many different methodologies that can be used by a CDSS in order to provide support to the health care professional. The basic components of a CDSS include a dynamic (medical) knowledge base and an inferencing mechanism (usually a set of rules derived from the experts and evidence-based medicine) and implemented through medical logic modules based on a language such as Arden syntax. It could be based on Expert systems or artificial neural networks or both (connectionist expert systems).
Bayesian Network The Bayesian network is a knowledge-based graphical representation that shows a set of variables and their probabilistic relationships between diseases and symptoms. They are based on conditional probabilities, the probability of an event given the occurrence of another event, such as the interpretation of diagnostic tests. Bayes’ rule helps us compute the probability of an event with the help of some more readily available information and it consistently processes options as new evidence is presented. In the context of CDSS, the Bayesian network can be used to compute the probabilities of the presence of the possible diseases given their symptoms. Some of the advantages of Bayesian Network include the knowledge and conclusions of experts in the form of probabilities, assistance in decision making as new information is available and are based on unbiased probabilities that are applicable to many models. Some of the disadvantages of Bayesian Network include the difficulty to get the probability knowledge for possible diagnosis and not being practical for large complex systems given multiple symptoms. The Bayesian calculations on multiple simultaneous symptoms could be overwhelming for users. Example of a Bayesian network in the CDSS context is the Iliad system which makes use of Bayesian reasoning to calculate posterior probabilities of possible diagnoses depending on the symptoms provided. The system now covers about 1500 diagnoses based on thousands of findings. Another example is the DXplain system that uses a modified form of the Bayesian logic. This CDSS produces a list of ranked diagnoses associated with the symptoms. A third example is SimulConsult [3], which began in the area of neurogenetics. By the end of 2010 it covered ~2,600 diseases in neurology and genetics, or roughly 25% of known diagnoses. It addresses the core issue of Bayesian systems, that of a scalable way to input data and calculate probabilities, by focusing specialty by specialty and
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Clinical decision support system achieving completeness. Such completeness allows the system to calculate the relative probabilities, rather than the person inputting the data. Using the peer-reviewed medical literature as its source, and applying two levels of peer-review to the data entries, SimulConsult can add a disease with less than a total of four hours of clinician time. It is widely used by pediatric neurologists today in the US and in 85 countries around the world.
Neural Network Artificial Neural Networks (ANN) is a nonknowledge-based adaptive CDSS that uses a form of artificial intelligence, also known as machine learning, that allows the systems to learn from past experiences / examples and recognizes patterns in clinical information. It consists of nodes called neuron and weighted connections that transmit signals between the neurons in a forward or looped fashion. An ANN consists of 3 main layers: Input (data receiver or findings), Output (communicates results or possible diseases) and Hidden (processes data). The system becomes more efficient with known results for large amounts of data. The advantages of ANN include the elimination of needing to program the systems and providing input from experts. The ANN CDSS can process incomplete data by making educated guesses about missing data and improves with every use due to its adaptive system learning. Additionally, ANN systems do not require large databases to store outcome data with its associated probabilities. Some of the disadvantages are that the training process may be time consuming leading users to not make use of the systems effectively. The ANN systems derive their own formulas for weighting and combining data based on the statistical recognition patterns over time which may be difficult to interpret and doubt the system’s reliability. Examples include the diagnosis of appendicitis, back pain, myocardial infarction, psychiatric emergencies and skin disorders. The ANN’s diagnostic predictions of pulmonary embolisms were in some cases even better than physician’s predictions.[citation needed] Additionally, ANN based applications have been useful in the analysis of ECG (A.K.A. EKG) waveforms.
Genetic Algorithms A Genetic Algorithm (GA) is a nonknowledge-based method developed in the 1940s at the Massachusetts Institute of Technology based on Darwin’s evolutionary theories that dealt with the survival of the fittest. These algorithms rearrange to form different re-combinations that are better than the previous solutions. Similar to neural networks, the genetic algorithms derive their information from patient data. An advantage of genetic algorithms is these systems go through an iterative process to produce an optimal solution. The fitness function determines the good solutions and the solutions that can be eliminated. A disadvantage is the lack of transparency in the reasoning involved for the decision support systems making it undesirable for physicians. The main challenge in using genetic algorithms is in defining the fitness criteria. In order to use a genetic algorithm, there must be many components such as multiple drugs, symptoms, treatment therapy and so on available in order to solve a problem. Genetic algorithms have proved to be useful in the diagnosis of female urinary incontinence.[citation needed]
Rule-Based System A rule-based expert system attempts to capture knowledge of domain experts into expressions that can be evaluated known as rules; an example rule might read, "If the patient has high blood pressure, he or she is at risk for a stroke." Once enough of these rules have been compiled into a rule base, the current working knowledge will be evaluated against the rule base by chaining rules together until a conclusion is reached. Some of the advantages of a rule-based expert system are the fact that it makes it easy to store a large amount of information, and coming up with the rules will help to clarify the logic used in the decision-making process. However, it can be difficult for an expert to transfer their knowledge into distinct rules, and many rules can be required for a system to be effective.
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Clinical decision support system Rule-based systems can aid physicians in many different areas, including diagnosis and treatment. An example of a rule-based expert system in the clinical setting is MYCIN. Developed at Stanford University by Edward Shortliffe in the 1970s, MYCIN was based on around 600 rules and was used to help identify the type of bacteria causing an infection. While useful, MYCIN can help to demonstrate the magnitude of these types of systems by comparing the size of the rule base (600) to the narrow scope of the problem space. The Stanford AI group subsequently developed ONCOCIN, another rules-based expert system coded in Lisp in the early 1980s.[4] The system was intended to reduce the number of clinical trial protocol violations, and reduce the time required to make decisions about the timing and dosing of chemotherapy in late phase clinical trials. As with MYCIN, the domain of medical knowledge addressed by ONCOCIN was limited in scope and consisted of a series of eligibility criteria, laboratory values, and diagnostic testing and chemotherapy treatment protocols that could be translated into unambiguous rules. Oncocin was put into production in the Stanford Oncology Clinic.
Logical Condition The methodology behind logical condition is fairly simplistic; given a variable and a bound, check to see if the variable is within or outside of the bounds and take action based on the result. An example statement might be "Is the patient's heart rate less than 50 BPM?" It is possible to link multiple statements together to form more complex conditions. Technology such as a decision table can be used to provide an easy to analyze representation of these statements. In the clinical setting, logical conditions are primarily used to provide alerts and reminders to individuals across the care domain. For example, an alert may warn an anesthesiologist that their patient's heart rate is too low; a reminder could tell a nurse to isolate a patient based on their health condition; finally, another reminder could tell a doctor to make sure he discusses smoking cessation with his patient. Alerts and reminders have been shown to help increase physician compliance with many different guidelines; however, the risk exists that creating too many alerts and reminders could overwhelm doctors, nurses, and other staff and cause them to ignore the alerts altogether.
Causal Probabilistic Network The primary basis behind the causal network methodology is cause and effect. In a clinical causal probabilistic network, nodes are used to represent items such as symptoms, patient states or disease categories. Connections between nodes indicate a cause and effect relationship. A system based on this logic will attempt to trace a path from symptom nodes all the way to disease classification nodes, using probability to determine which path is the best fit. Some of the advantages of this approach are the fact that it helps to model the progression of a disease over time and the interaction between diseases; however, it is not always the case that medical knowledge knows exactly what causes certain symptoms, and it can be difficult to choose what level of detail to build the model to. The first clinical decision support system to use a causal probabilistic network was CASNET, used to assist in the diagnosis of glaucoma. CASNET featured a hierarchical representation of knowledge, splitting all of its nodes into one of three separate tiers: symptoms, states and diseases.
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Examples of CDSS • • • • • •
ESAGIL [5] CADUCEUS DiagnosisPro Dxplain MYCIN RODIA
References [1] [2] [3] [4]
"Decision support systems ." 26 July 2005. 17 Feb. 2009 . Berner, Eta S., ed. Clinical Decision Support Systems. New York, NY: Springer, 2007. http:/ / www. SimulConsult. com ONCOCIN: An expert system for oncology protocol management E. H. Shortliffe, A. C. Scott, M. B. Bischoff, A. B. Campbell, W. V. Melle, C. D. Jacobs Seventh International Joint Conference on Artificial Intelligence, Vancouver, B.C.. Published in 1981 [5] http:/ / www. esagil. org
External links • CDSS from Dr Y Health Informatics Knowledge Base (http://hayajneh.org/g/2011/02/ clinical-decision-support-systems/) • Decision support chapter from Coiera's Guide to Health Informatics (http://www.uni-kiel.de/medinfo/ material/kurs_ss06/8/expertsystems_03.pdf) • OpenClinical (http://www.openclinical.org/dss.html) maintains an extensive archive of Artificial Intelligence systems in routine clinical use. • Robert Trowbridge/ Scott Weingarten. Chapter 53. Clinical Decision Support Systems (http://www.ahrq.gov/ clinic/ptsafety/chap53.htm) • Stanford CDSS (http://clinicalinformatics.stanford.edu/scci_seminars/12_5_03.html)
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Computer-aided diagnosis
Computer-aided diagnosis In radiology, computer-aided detection (CADe), also called computer-aided diagnosis (CADx), are procedures in medicine that assist doctors in the interpretation of medical images. Imaging techniques in X-ray, MRI, and Ultrasound diagnostics yield a great deal of information, which the radiologist has to analyze and evaluate comprehensively in a short time. CAD systems help scan digital images, e.g. from computed tomography, for typical appearances and to highlight conspicuous sections, such as possible diseases. CAD is a relatively young interdisciplinary technology combining elements of artificial intelligence and digital image processing with radiological image processing. A typical application is the detection of a tumor. For instance, some hospitals use CAD to support preventive medical check-ups in mammography (diagnosis of breast cancer), the detection of polyps in the colon, and lung cancer.
Overview Computer-aided detection (CADe) systems are usually confined to marking conspicuous structures and sections. Computer-aided diagnosis (CADx) systems evaluate the conspicuous structures. For example, in mammography CAD highlights micro calcification clusters and hyperdense structures in the soft tissue. This allows the radiologist to draw conclusions about the condition of the pathology. Another application is CADq, which quantifies, e.g., the size of a tumor or the tumor's behavior in contrast medium uptake. Computer-aided simple triage (CAST) is another type of CAD, which performs a fully automatic initial interpretation and triage of studies into some meaningful categories (e.g. negative and positive). CAST is particularly applicable in emergency diagnostic imaging, where a prompt diagnosis of critical, life threatening condition is required. At the present stage of the technology, CAD cannot and may not substitute the doctor, but rather plays a supporting role. The doctor (generally a radiologist) is always responsible for the final interpretation of a medical image.
Computer-aided diagnosis topics Methodology CAD is fundamentally based on highly complex pattern recognition. X-ray images are scanned for suspicious structures. Normally a few thousand images are required to optimize the algorithm. Digital image data are copied to a CAD server in a DICOM-format and are prepared and analyzed in several steps. 1. Preprocessing for • Reduction of artifacts (bugs in images) • Image noise reduction • Leveling (harmonization) of image quality for clearing the image's different basic conditions e.g. different exposure parameter. 2. Segmentation for • Differentiation of different structures in the image, e.g. heart, lung, ribcage, possible round lesions • Matching with anatomic databank 3. Structure/ROI (Region of Interest) Analyze Every detected region is analyzed individually for special characteristics: • Compactness • Form, size and location • Reference to close-by structures / ROIs • Average greylevel value analyze within a ROI
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Computer-aided diagnosis • Proportion of greylevels to border of the structure inside the ROI 4. Evaluation / classification After the structure is analyzed, every ROI is evaluated individually (scoring) for the probability of a TP. Therefore the procedures are: • • • • • • •
Nearest-Neighbor Rule Minimum distance classifier Cascade Classifier Bayesian Classifier Multilayer perception Radial basis function network (RBF) SVM
If the detected structures have reached a certain threshold level, they are highlighted in the image for the radiologist. Depending on the CAD system these markings can be permanently or temporary saved. The latter's advantage is that only the markings which are approved by the radiologist are saved. False hits should not be saved, because an examination at a later date becomes more difficult then.
Sensitivity and specificity CAD systems seek to highlight suspicious structures. Today's CAD systems cannot detect 100% of pathological changes. The hit rate (sensitivity) can be up to 90% depending on system and application.[1] A correct hit is termed a True Positive (TP), while the incorrect marking of healthy sections constitutes a False Positive (FP). The less FPs indicated, the higher the specificity is. A low specificity reduces the acceptance of the CAD system because the user has to identify all of these wrong hits. The FP-rate in lung overview examinations (CAD Chest) could be reduced to 2 per examination. In other segments (e.g. CT lung examinations) the FP-rate could be 25 or more. In CAST systems the FP rate must be extremely low (less than 1 per examination) to allow a meaningful study triage.
Absolute detection rate The absolute detection rate of the radiologist is an alternative metric to sensitivity and specificity. Overall, results of clinical trials about sensitivity, specificity, and the absolute detection rate can vary markedly. Each study result depends on its basic conditions and has to be evaluated on those terms. The following facts have a strong influence: • • • • • •
Retrospective or prospective design Quality of the used images Condition of the x-ray examination Radiologist's experience and education Type of lesion Size of the considered lesion
Applications CAD is used in the diagnosis of breast cancer, lung cancer, colon cancer, prostate cancer, bone metastases, coronary artery disease and congenital heart defect.
Breast cancer CAD is used in screening mammography (X-ray examination of the female breast). Screening mammography is used for the early detection of breast cancer. CAD is especially established in US and the Netherlands and is used in addition to human evaluation, usually by a radiologist. The first CAD system for mammography was developed in a research project at the University of Chicago. Today it is commercially offered by iCAD and Hologic. There are currently some non-commercial projects being developed, such as Ashita Project, a gradient-based screening
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Computer-aided diagnosis software by Alan Hshieh, as well. However, while achieving high sensitivities, CAD systems tend to have very low specificity and the benefits of using CAD remain uncertain. Some studies suggest a positive impact on mammography screening programs,[2][3] but others show no improvement.[4][5] A 2008 systematic review on computer-aided detection in screening mammography concluded that CAD does not have a significant effect on cancer detection rate, but does undesirably increase recall rate (i.e. the rate of false positives). However, it noted considerable heterogeneity in the impact on recall rate across studies.[6] Procedures to evaluate mammography based on magnetic resonance imaging exist too.
Lung cancer (bronchial carcinoma) In the diagnosis of lung cancer, computed tomography with special three-dimensional CAD systems are established and considered as gold standard.[citation needed] At this a volumetric dataset with up to 3,000 single images is prepared and analyzed. Round lesions (lung cancer, metastases and benign changes) from 1mm are detectable. Today all well-known vendors of medical systems offer corresponding solutions. Early detection of lung cancer is valuable. The 5-year-survival-rate of lung cancer has stagnated in the last 30 years and is now at approximately just 15%. Lung cancer takes more victims than breast cancer, prostate cancer and colon cancer together. This is due to the asymptomatic growth of this cancer. In the majority of cases it is too late for a successful therapy if the patient develops first symptoms (e.g. chronic croakiness or hemoptysis). But if the lung cancer is detected early (mostly by chance), there is a survival rate at 47% according to the American Cancer Society.[7] At the same time the standard x-ray-examination of the lung is the most frequently x-ray examination with a 50% share. Indeed the random detection of lung cancer in the early stage (stage 1) in the x-ray image is difficult. It is a fact that round lesions vary from 5–10mm are easily overlooked.[8] The routine application of CAD Chest Systems may help to detect small changes without initial suspicion. Philips was the first vendor to present a CAD for early detection of round lung lesions on x-ray images.[9]
Colon cancer CAD is available for detection of colorectal polyps in the colon. Polyps are small growths that arise from the inner lining of the colon. CAD detects the polyps by identifying their characteristic "bump-like" shape. To avoid excessive false positives, CAD ignores the normal colon wall, including the haustral folds. In early clinical trials, CAD helped radiologists find more polyps in the colon than they found prior to using CAD.[10][11]
Coronary artery disease CAD is available for the automatic detection of significant (causing more than 50% stenosis) coronary artery disease in coronary CT angiography (CCTA) studies. A low false positives rate (60-70% specificity per patient)[12][13][14]allows using it as a computer-aided simple triage (CAST) tool distinguishing between positive and negative studies and yielding a preliminary report. This, for example, can be used for chest pain patients' triage in an emergency setting.
Congenital heart defect Early detection of pathology can be the difference between life and death. CADe can be done by auscultation with a digital stethoscope and specialized software, also known as Computer-aided auscultation. Murmurs, irregular heart sounds, caused by blood flowing through a defective heart, can be detected with high sensitivity and specificity. Computer-aided auscultation is sensitive to external noise and bodily sounds and requires an almost silent environment to function accurately.
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Computer-aided diagnosis
Nuclear medicine CADx is available for nuclear medicine images. Commercial CADx systems for the diagnosis of bone metastases in whole-body bone scans and coronary artery disease in myocardial perfusion images exist.[15]
References [1] T. Wollenweber, B. Janke, A. Teichmann, M. Freund: Korrelation zwischen histologischem Befund und einem Computer-assistierten Detektionssystem (CAD) für die Mammografie. Geburtsh Frauenheilk 2007; 67: 135-141 [2] Fiona J. Gilbert, F.R.C.R., Susan M. Astley, Ph.D., Maureen G.C. Gillan, Ph.D., Olorunsola F. Agbaje, Ph.D., Matthew G. Wallis, F.R.C.R., Jonathan James, F.R.C.R., Caroline R.M. Boggis, F.R.C.R., Stephen W. Duffy, M.Sc., for the CADET II Group (2008). Single Reading with Computer-Aided Detection for Screening Mammography, The New England Journal of Medicine, Volume 359:1675-1684 Full text (http:/ / content. nejm. org/ cgi/ reprint/ 359/ 16/ 1675. pdf) [3] Effect of Computer-Aided Detection on Independent Double Reading of Paired Screen-Film and Full-Field Digital Screening Mammograms Per Skaane, Ashwini Kshirsagar, Sandra Stapleton, Kari Young and Ronald A. Castellino [4] Taylor P, Champness J, Given-Wilson R, Johnston K, Potts H (2005). Impact of computer-aided detection prompts on the sensitivity and specificity of screening mammography. Health Technology Assessment 9(6), 1-70. [5] Fenton JJ, Taplin SH, Carney PA, Abraham L, Sickles EA, D'Orsi C et al. Influence of computer-aided detection on performance of screening mammography. N Engl J Med 2007 April 5;356(14):1399-409. Full text (http:/ / content. nejm. org/ cgi/ reprint/ 356/ 14/ 1399. pdf) [6] Taylor P, Potts HWW (2008). Computer aids and human second reading as interventions in screening mammography: Two systematic reviews to compare effects on cancer detection and recall rate. European Journal of Cancer. Full text (http:/ / eprints. ucl. ac. uk/ 5173/ ) [7] http:/ / www. cancer. org/ downloads/ CRI/ 6976. 00. pdf [8] Wu N, Gamsu G, Czum J, Held B, Thakur R, Nicola G: Detection of small pulmonary nodules using direct digital radiography and picture archiving and communication systems. J Thorac Imaging. 2006 Mar;21(1):27-31. PMID 16538152 [9] xLNA (x-Ray Lung Nodule Assessment) (http:/ / www. medical. philips. com/ main/ products/ xray/ products/ radiography/ cad_chest) [10] Petrick N, Haider M, Summers RM, Yeshwant SC, Brown L, Iuliano EM, Louie A, Choi JR, Pickhardt PJ. CT colonography with computer-aided detection as a second reader: observer performance study. Radiology 2008 Jan;246(1):148-56. Erratum in: Radiology. 2008 Aug;248(2):704. PMID 18096536 [11] Halligan S, Altman DG, Mallett S, Taylor SA, Burling D, Roddie M, Honeyfield L, McQuillan J, Amin H, Dehmeshki J. Computed tomographic colonography: assessment of radiologist performance with and without computer-aided detection. Gastroenterology 2006 Dec;131(6):1690-9. Epub 2006 Oct 1. PMID 17087934 [12] E. Arnoldi, M. Gebregziabher, U. J. Schoepf, R. Goldenberg, L. Ramos-Duran, P. L. Zwerner, K. Nikolaou, M. F. Reiser, P. Costello and C. Thilo, Automated computer-aided stenosis detection at coronary CT angiography: initial experience, European Radiology, 20(5):1160-7, May 2010 PMID 19890640 [13] E. J. Halpern, D. J. Halpern, Diagnosis of coronary stenosis with CT angiography: comparison of automated computer diagnosis with expert readings, Academic Radiology, 18(3):324-33, Mar 2011 PMID 21215663 [14] Kang KW, Chang HJ, Shim H, Kim YJ, Choi BW, Yang WI, Shim JY, Ha J, Chung N., Feasibility of an automatic computer-assisted algorithm for the detection of significant coronary artery disease in patients presenting with acute chest pain., Eur J Radiol, 81(4):e640-6, Apr 2012, PMID 22304980 [15] EXINI Diagnostics (http:/ / www. exini. com)
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Medical algorithm
Medical algorithm A medical algorithm is any computation, formula, statistical survey, nomogram, or look-up table, useful in healthcare. Medical algorithms include decision tree approaches to healthcare treatment (e.g., if symptoms A, B, and C are evident, then use treatment X) and also less clear-cut tools aimed at reducing or defining uncertainty.
Scope Medical algorithms are part of a broader field which is usually fit under the aims of medical informatics and A medical algorithm for assessment and treatment of overweight and obesity. medical decision making. Medical decisions occur in several areas of medical activity including medical test selection, diagnosis, therapy and prognosis, and automatic control of medical equipment. In relation to logic-based and artificial neural network-based clinical decision support system, which are also computer applications to the medical decision making field, algorithms are less complex in architecture, data structure and user interface. Medical algorithms are not necessarily implemented using digital computers. In fact, many of them can be represented on paper, in the form of diagrams, nomographs, etc.
Examples A wealth of medical information exists in the form of published medical algorithms. These algorithms range from simple calculations to complex outcome predictions. Most clinicians use only a small subset routinely. Examples of medical algorithms are: • Calculators,. e.g., an on-line or stand-alone calculator for body mass index (BMI) when stature and body weight are given; • Flowcharts, e.g., a binary decision tree for deciding what is the etiology of chest pain • Look-up tables, e.g., for looking up food energy and nutritional contents of foodstuffs • Nomograms, e.g., a moving circular slide to calculate body surface area or drug dosages. A common class of algorithms are embedded in guidelines on the choice of treatments produced by many national, state, financial and local healthcare organisations and provided as knowledge resources for day to day use and for induction of new physicians. A field which has gained particular attention is the choice of medications for psychiatric conditions. In the United Kingdom, guidelines or algorithms for this have been produced by most of the circa 500 primary care trusts, substantially all of the circa 100 secondary care psychiatric units and many of the circa 10 000 general practices. In the US, there is a national (federal) initiative to provide them for all states, and by 2005 six states were adapting the approach of the Texas Medication Algorithm Project or otherwise working on their production.
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Medical algorithm A grammar—the Arden syntax—exists for describing algorithms in terms of medical logic modules. An approach such as this should allow exchange of MLMs between doctors and establishments, and enrichment of the common stock of tools.
Purpose The intended purpose of medical algorithms is to improve and standardize decisions made in the delivery of medical care. Medical algorithms assist in standardizing selection and application of treatment regimens, with algorithm automation intended to reduce potential introduction of errors. Some attempt to predict the outcome, for example critical care scoring systems. Computerized health diagnostics algorithms can provide timely clinical decision support, improve adherence to evidence-based guidelines, and be a resource for education and research. Medical algorithms based on best practice can assist everyone involved in delivery of standardized treatment via a wide range of clinical care providers. Many are presented as protocols and it is a key task in training to ensure people step outside the protocol when necessary. In our present state of knowledge, generating hints and producing guidelines may be less satisfying to the authors, but more appropriate.
Cautions In common with most science and medicine, algorithms whose contents are not wholly available for scrutiny and open to improvement should be regarded with suspicion. Computations obtained from medical algorithms should be compared with, and tempered by, clinical knowledge and physician judgment.
External links • AlternativeMentalHealth.com [1] - 'Alternative Health Medical Evaluation Field Manual', Lorrin M. Koran, MD, Stanford University Medical Center (1991) • MedAl.org [2] - 'The Medical Algorithms Project' • NIH.gov [3] - 'Automated Medical Algorithms: Issues for Medical Errors', Kathy A. Johnson, PhD, John R. Svirbely, MD, M. G. Sriram, PHD, Jack W. Smith, MD, PHD, Gareth Kantor, MD, and Jorge Raul Rodriguez, MD, Journal of the American Medical Informatics Association • Regenstrief Institute [4] • MedFormula - a palm pilot based medical algorithm/calculator [5]
References [1] [2] [3] [4] [5]
http:/ / www. alternativementalhealth. com/ articles/ fieldmanual. htm http:/ / medal. org http:/ / www. pubmedcentral. nih. gov/ articlerender. fcgi?artid=419420 http:/ / www. regenstrief. org/ http:/ / www. internal-med. org/ palm. htm
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Medical logic module
Medical logic module A medical logic module (MLM) is an independent unit in a healthcare knowledge base that represents the knowledge published on a requirement for treating a patient according to a single medical decision. Possible usage is with an event monitor program in an intensive care ward or with hospital information system on occurrence of defined conditions. See Arden syntax reference for examples. Early introduction is given with monographs.[1]
Implementation The Arden syntax has been defined as a grammar which could make MLMs swappable between various platforms. There is no reference stated for general implementation as a transfer method between different information systems.
References [1] Hripcsak G., Writing Arden Syntax Medical Logic Modules, Comput Biol Med. 1994 Sep;24(5):331-63
Arden syntax Arden syntax for Medical logic modules is a language used for representing and sharing medical knowledge. It is used to generate alerts, interpretations, screen and manage messages. Clinical and scientific knowledge is represented by using this extensively recognized standard in an executable format which can be used by Clinical decision support systems. A Vital task of syntax is to share medical knowledge base across many institutions. Arden syntax version 2.0 was published by HL7 in 1999, which is responsible for developing all the latest versions. Arden syntax version 2.9 is the current version. The Knowledge base of Arden syntax consists of a set of rules called Medical Logic Modules, each of which comprises enough logic to make a single medical decision. Medical logic modules are written in Arden syntax, and are called by a program - an event monitor - when the condition they are written to help with occurs. Arden syntax was formerly a standard under ASTM, which was published in the year 1992 and is now part of HL7.
Rationale The Rationale behind the design of the Syntax is to offer potential users in deciding if the standard is appropriate for their purposes. It offers users and implementors knowledge of how parts of the standard were designed to be used. It also provides authors of other standards an insight that might be helpful in their own attempts in future designing of new languages.
History The name, "Arden Syntax", was adopted from Arden House, located about 90 minutes north of Manhattan in Orange County, New York. Originally purchased by Edward Henry (E. H.) Harriman in 1885, the estate was given to Columbia University by his son William Averell Harriman in 1950 following its use by the Navy in World War II. The house and grounds became a National Historic Landmark in 1966, and it is now a conference center. During the five-year IBM/CPMC R&D program, conferences and working sessions were hosted and led by CPMC at Arden House and attended by medical informaticians from several leading universities and hospitals, IBM personnel, and others directly or indirectly involved in the program. The "Arden Syntax" name was chosen in recognition of important milestones achieved at Arden House in the development and refinement of the syntax and its
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Arden syntax implementation.[1]
Structure of Arden syntax The unit of representation in the Arden syntax is the Medical logic Module (MLM). A Medical logic Module is composed of three categories, namely maintenance, library and knowledge with appropriate slots.
Maintenance Category It contains metadata about the MLM. The maintenance category consists of slots that indicate maintenance information unrelated to the medical knowledge in the module. The first slot is the title which gives a brief description of the module followed by a file name, a distinct identifier used to specify the MLM. The third slot is the version which specifies the version used. It also maintains a track of updates to the MLMs. A version slot is followed by institution and author slots that specify where the MLM is written and the person who wrote it. The sixth slot is the specialist slot that names the person in the institution liable for validating and installing the MLM in the institution. This slot is always meant to be blank when transferring information from one institution to another. This slot is followed by date and validation slots which show the date at which MLM was last updated. The validation level is set by the specialist, it indicates that the MLM is only used for testing. These slots are used for knowledge base maintenance and change control.
Library Category This category contains five slots called purpose, explanation, keywords, citations and links. The purpose slot explains what a particular MLM is used for, whereas the explanation slot illustrates how an MLM works. Terms that can be used to search through a knowledge base of MLM is supplied by a keyword slot. The citation and link slots are optional. References to literature that support MLM’s medical behaviour are included in the citation slot. Institution specific links to other sources of information such as electronic textbooks and educational modules are contained in the links slot.
Knowledge Category This category contains the actual medical knowledge of the MLM. It consists of type, data, priority, evoke, logic and action slots. The way in which MLM is used is known by type slot. Terms used in the rest of the MLM are defined by the data slot. Its goal is to separate those parts of the MLM that are specific to an institution from the more generic parts of the MLM. The order in which the MLM must be invoked are indicated by the priority, which can be a number from 1 (Last) to 99(first). It is a rarely used optional slot. An MLM can be activated by an event, or by a direct call from an MLM or an application programme which is specified by the evoke slot. A real medical condition or rule to test for is contained in the logic slot which may include compound calculations. The action slot creates a message that is sent to the health care provider, such as sending an alert to the destination, evoking other MLMs and returning values. The urgency slot is an optional; it can be a number from 1 to 99 which indicates the importance of an MLMs action or message.
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Arden syntax
Functions of Arden Syntax • When a clinically important situation such as a medication interaction or dangerous laboratory result arises, the provider is warned by an alert message. • An interpretation is a non-emergent message designed to supply a provider with supportive information such as an interpretation of liver function tests. • A Screen is a message sent to clinical research when patients meeting certain characteristics either for a clinical trial or quality assurance concern are admitted to the hospital. • Management messages are used for administrative purposes such as managing bed assignments, same day admissions and discharges from the hospital.
Testing Arden syntax is tested for reliability and imprecision using tools lex and Yacc. when used together creates a compiler or interpreter. Source file is split into tokens by lex and the hierarchical structure of the programme is found by Yacc. These tools reduce ambiguities in the syntax.
Implementation Several developers have used yacc-based compilers or similar tools to translate the MLMs to an intermediate form which is executed later. Other developers use Prolog for both parsing and interpretation and optimising MLMs by converting them to single-assignment declarative form.
Advantages • • • •
It is a part of the HL7 [2] standard and is well known by many healthcare providers. It allows easy encoding of several important medical concepts. It is more appropriate for practical implementation of Clinical decision support system. Every data element and event has date/time stamp that is clinically significant. The time functions help users specify data and time in MLMs. • The code is written in a way close to natural language, easily readable with several syntactic feature, such as flexible list handling that can be filtered with ease. • Easier to handle patient data created at different times by two components, namely the value and the primary time. • Developers are encouraged to document and annotate MLMs for producing large metadata by the standard, which is vital for making large collections of MLMs manageable.
Limitations • Problems related to adoption of Arden syntax are the curly braces problem and the compiler problem, which may be resolved in the future by the introduction of XML-based techniques like Virtual Medical Record (vMR). • Since it is divided in to various categories, it allows usage of various operators and statements at the same time, leading to inconsistencies. • Standard might be written in two separate documents, one for users to develop Arden syntax MLMs and the other for developers of Arden syntax compilers.
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Arden syntax
Uses of Arden Syntax Arden syntax is used in computerized care plans for the management of patients following Coronary artery bypass surgery The Regenstrief Institute, Inc. [4] uses Arden Syntax MLMs in its CARE system to deliver reminders or hints to clinicians regarding patient treatment recommendations (e.g. the next clinic appointment, based on rules applied to the digitized notes and pertinent patient data stored in the system. Regenstrief Institute is an international non-profit medical research organization with one of the world's largest brain trusts of physician as well as Health Services Researchers. Additionally, LDS hospital in Salt Lake City (HELP System...) has contributed much to this standard as well as body of knowledge.
Fuzzy Arden Syntax The main aim of fuzzy Arden syntax is to provide easy method in processing of uncertain data which routinely appears in medicine. New concepts are incorporated in to Arden Syntax by fuzzy Arden syntax in order to assist in processing information that may not be completely defined. For example a fuzzy logic has been used in knowledge base in Moni–ICU system at clinical institute of hospital hygiene of the Vienna general hospital. It is a system that detects and constantly checks Hospital-acquired infections. Use of fuzzy logic in knowledge base provide physicians with more precise information on the degree of the presence of nosocomial infections, that aids to recognize borderline cases and allows former detection of an infection onset and its decline.
Applications Arden Syntax and its first applications were conceived and developed as the primary deliverables of a multimillion-dollar joint research and development (R&D) program between Columbia Presbyterian Medical Center (CPMC) in New York City and IBM Health Industry Marketing in Atlanta, Georgia from 1989-1993. IBM provided program funding, S/370 mainframe hardware, software, peripheral equipment, and other materials for the work, and program management oversight of the collaborative effort. At Columbia-Presbyterian Medical center, 40 Arden syntax MLMs have been implemented in which eighteen of those are clinical MLMs, including four interpretations and fourteen alerts. For example, a user is alerted by three MLMs to the presence of hypokalemia and digoxin use that might lead to cardiac dysrhythmia. One MLM is activated by storage of a pharmacy order by digoxin,a second MLM is activated by the storage of a blood potassium result and the third activated by the storage of blood digoxin level. Twelve are research MLM examples, which include the ability to identify patients with abnormal cervical pathology, etc that notify the researcher of the details of the patient's medical record and their inpatient location to enrol the patient in a study, and the remaining ten are administrative MLMs. Arden syntax is implemented at LDS hospital, Salt Lake City, Utah, using the HELP system. A medical decision support system at Linkoping University, Linkoping, Sweden comprises a clinical data base, Medical database dictionary, and a knowledge base component. Syntax for the knowledge base is Arden syntax. Samwald et.al group developed many Clinical decision support system using Arden syntax standard ranging from a few to several dozens of MLMs. These systems are Hepaxpert,[3] Thyrexpert,[4] Toxopert[5] and RHEUMexpert.[6] The Hepaxpert system helps in interpretation of Hepatitis A, B and C serology test results, whereas the Thyrexpert system helps in interpretation of thyroid harmone test results. The Toxopert system helps in interpretation of time sequences of toxoplasmosis serology test results. Differential diagnosis decision support in rheumatology is offered by RHEUMexpert. IBM's artificial intelligence product, KnowledgeTool, provided the original basis for MLM syntax representation and processing, as enhanced and applied by CPMC researchers Drs. James J. Cimino, George Hripcsak, Steve Johnson, Carol Friedman, and others at CPMC, under the leadership of Dr. Paul D. Clayton. In a related effort under the same program, another prototype implementation of the syntax was developed by Peter Ludemann using Quintus Prolog.
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Arden syntax IBM program management and AI technology services were provided by Terry Rankin, Pete Smith, and Eddie Sanders.
Arden Syntax Example maintenance: ǀǀǀǀ title: To check Hypotension of the patient;; filename: Hypotension;; version: 1.00;; institution: Latrobe University Bundoora;; author: Lakshmi Devineni;; specialist: ;; date: 2013-06-02;; validation: testing;; library: purpose: check if hypotension of the patient is within limits;; explanation: This MLM is an example for reading data and writing a message;; keywords: hypotension; categorization;; citations: ;; knowledge: type: data-driven;; data: /* read the hypotension*/ diastolic_blood_pressure := read last {diastolic blood pressure}; /* the value in braces is specific to your runtime environment */ /* If the height is lower than height_threshold, output a message */ diastolic_pressure_threshold := 60; stdout_dest := destination {stdout}; ;; evoke: null_event;; logic: if (diastolic_blood_pressure is not number) then conclude false; endif; if (diastolic_blood_pressure >= diastolic_pressure_threshold) then conclude true; else conclude false; endif; ;; action: write "Your hypotension is too low" at stdout_dest; ;; end:
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Arden syntax
References [1] [2] [3] [4] [5] [6]
Arden house (http:/ / theardenhouse. com/ home. html) http:/ / www. hl7. org/ http:/ / www. medexter. com/ hepax/ hepax http:/ / systems. medexter. com/ Thyrexpert/ index. php?stateRequest=system http:/ / systems. medexter. com/ Toxopert/ InterpretationFuzzy/ index. php http:/ / systems. medexter. com/ RheumaDiff/ index. php?stateRequest=scientificdevelopment
External links • Introduction to the HL7 Standards (http://www.HL7.com.au/FAQ.htm) • Open Source Arden Syntax compiler implementation (http://arden2bytecode.sourceforge.net)
Concept Processing Concept Processing is a technology that uses an artificial intelligence engine to provide flexible user interfaces. This technology is used in some Electronic Medical Record (EMR) software applications, as an alternative to the more rigid template-based technology.
Some methods of data entry in electronic medical records The most widespread methods of data entry into an EMR are templates, voice recognition, transcription, and concept processing.
Templates The physician selects either a general, symptom-based or diagnosis-based template pre-fabricated for the type of case at that moment, making it specific through use of forms, pick-lists, check-boxes and free-text boxes. This method became predominant especially in Emergency Room Medicine during the late 1990s.
Voice recognition The physician dictates into a computer voice recognition device that enters the data directly into a free-text area of the EMR.
Transcription The physician dictates the case into a recording device, which is then sent to a transcriptionist for entry into the EMR, usually into free text areas.
Concept Processing Based on artificial intelligence technology and Boolean logic, Concept Processing attempts to mirror the mind of each physician by recalling elements from past cases that are the same or similar to the case being seen at that moment.
How Concept Processing works For every physician the bell-shaped curve effect is found, representing a frequency distribution of case types. Some cases are so rare that physicians will have never handled them before. The majority of other cases become repetitive, and are found on top of this bell shape curve.
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Concept Processing A Concept Processor brings forward the closest previous encounter in relation to the one being seen at that moment, putting that case in front of the physician for fine-tuning. There are only three possibilities of cases : The closest encounter could be identical to the current encounter (not an impossible event). It could be similar to the current note, or it could be a rare new case.
If the closest encounter is identical to your present one, the physician has effectively completed charting. A Concept Processor will pull through all the related information needed. If the encounter is similar but not identical, the physician modifies the differences from the closest case using hand-writing recognition, voice recognition, or keyboard. A Concept Processor then memorizes all the changes, so that when the next encounter falls between two similar cases, the editing is cut in half, and then by a quarter for the next case, and then by an eighth....and so on. In fact, the more a Concept Processor is used, the faster and smarter it becomes. Concept Processing also can be used for rare cases. These are usually combinations of SOAP note elements, which in themselves are not rare. If the text of each element is saved for a given type of case, there will be elements available to use with other cases, even though the other cases may not be similar overall. The role of a concept processor is simply to reflect that thinking process accurately in a doctor's own words.
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Guideline execution engine
Guideline execution engine A Guideline Execution Engine is a computer program which can interpret a clinical guideline represented in a computerized format and perform actions towards the user of an electronic medical record. A Guideline Execution Engine needs to communicate with a host Clinical information system. vMR is one possible interface which can be used. The engine's main function is to manage instances of executed guidelines of individual patients. Delivering the inferred engine recommendations or impacts to the host Clinical information system has to carefully respect current workflow of the clinicians (physicians, nurses, clerks, etc.)
Architecture of Guideline Execution Engine The following modules are generally needed for any engine • interface to Clinical Information System • new guidelines loading module • guideline interpreter module • clinical events parser • alert/recommendations dispatch
Guideline Interchange Format The Guideline Interchange Format (GLIF) is computer representation format for clinical guidelines. Represented guidelines can be executed using a guideline execution engine. The format has several versions as it has been improved. In 2003 GLIF3 was introduced.
References External links • Wang D, Peleg M, Tu SW, et al. (October 2004). "Design and implementation of the GLIF3 guideline execution engine" (http://linkinghub.elsevier.com/retrieve/pii/S1532046404000668). J Biomed Inform 37 (5): 305–18. doi: 10.1016/j.jbi.2004.06.002 (http://dx.doi.org/10.1016/j.jbi.2004.06.002). PMID 15488745 (http:// www.ncbi.nlm.nih.gov/pubmed/15488745). (PDF) (http://bmir.stanford.edu/file_asset/index.php/940/ BMIR-2004-1008.pdf) • Ram P, Berg D, Tu S, et al. (2004). "Executing clinical practice guidelines using the SAGE execution engine". Stud Health Technol Inform 107 (Pt 1): 251–5. PMID 15360813 (http://www.ncbi.nlm.nih.gov/pubmed/ 15360813). • Tu SW, Campbell J, Musen MA (2003). "The structure of guideline recommendations: a synthesis". AMIA Annu Symp Proc: 679–83. PMC 1480008 (http://www.ncbi.nlm.nih.gov/pmc/articles/PMC1480008). PMID 14728259 (http://www.ncbi.nlm.nih.gov/pubmed/14728259). (PDF) (http://bmir.stanford.edu/file_asset/ index.php/1511/BMIR-2003-0966.pdf) • Tu SW, Musen MA (1999). "A flexible approach to guideline modeling". Proc AMIA Symp: 420–4. PMC 2232509 (http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2232509). PMID 10566393 (http://www.ncbi. nlm.nih.gov/pubmed/10566393). (PDF) (http://bmir.stanford.edu/file_asset/index.php/211/ BMIR-1999-0789.pdf)
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CADUCEUS
CADUCEUS CADUCEUS was a medical expert system finished in the mid-1980s (first begun in the 1970s- it took a long time to build the knowledge base) by Harry Pople (of the University of Pittsburgh), building on Pople's years of interviews with Dr. Jack Meyers, one of the top internal medicine diagnosticians and a professor at the University of Pittsburgh. Their motivation was an intent to improve on MYCIN - which focused on blood-borne infectious bacteria - to focus on more comprehensive issues than a narrow field like blood poisoning (though it would do it in a similar manner); instead embracing all internal medicine. CADUCEUS eventually could diagnose up to 1000 different diseases. While CADUCEUS worked using an inference engine similar to MYCIN's, it made a number of changes (like incorporating abductive reasoning) to deal with the additional complexity of internal disease- there can be a number of simultaneous diseases, and data is generally flawed and scarce. CADUCEUS has been described as the "most knowledge-intensive expert system in existence".[1]
References [1] The Fifth Generation. Edward A. Feigenbaum and Pamela McCorduck. Addison-Wesley, Reading, Ma 01867, 275 Pp. Feb 1, 1984
Further reading • Banks, G (1986). "Artificial intelligence in medical diagnosis: the INTERNIST/CADUCEUS approach". Critical reviews in medical informatics 1 (1): 23–54. PMID 3331578 (http://www.ncbi.nlm.nih.gov/pubmed/ 3331578). • Wolfram, D (1995). "An appraisal of INTERNIST-I". Artificial Intelligence in Medicine 7 (2): 93–116. doi: 10.1016/0933-3657(94)00028-Q (http://dx.doi.org/10.1016/0933-3657(94)00028-Q). PMID 7647840 (http:/ /www.ncbi.nlm.nih.gov/pubmed/7647840). • First, MB; Soffer, LJ; Miller, RA (1985). "QUICK (QUick Index to Caduceus Knowledge): using the INTERNIST-1/CADUCEUS knowledge base as an electronic textbook of medicine". Computers and biomedical research, an international journal 18 (2): 137–65. doi: 10.1016/0010-4809(85)90041-2 (http://dx.doi.org/10. 1016/0010-4809(85)90041-2). PMID 3886276 (http://www.ncbi.nlm.nih.gov/pubmed/3886276). • "Expert systems: perils and promise" (http://portal.acm.org/citation.cfm?id=6597), D. G. Bobrow, S. Mittal, M. J. Stefik. Communications of the ACM, pp 880 - 894, issue 9, volume 29, (September 1986) • The AI Business: The commercial uses of artificial intelligence, ed. Patrick Winston and Karen A. Prendergast. 1984. ISBN 0-262-23117-4
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DXplain
DXplain DXplain is a Clinical decision support system (CDSS) available through the World Wide Web that assists clinicians by generating stratified diagnoses based on user input of patient signs and symptoms, laboratory results, and other clinical findings.[1] Evidential support for each differential diagnosis is presented, along with recommended follow-up that may be conducted by the clinician to arrive at a more definitive diagnosis. The system also serves as a clinician reference with a searchable database of diseases and clinical manifestations.
History Designed by the Laboratory of Computer Science at the Massachusetts General Hospital, work on DXplain began in 1984 with a first version being released in 1986.[2] .
Educational tool Use of DXplain as a tool for medical consultation has been common to some institutions since it fills a gap, particularly for medical students in clinical rotations, that is not adequately covered by textbook literature.[3] The system's large knowledge base combined with its ability to formulate diagnostic hypotheses have made it a popular education tool for US-based medical schools; by 2005, DXplain was supporting more than 33,189 total users.[4]
Methodology DXplain generates ranked differential diagnoses using a pseudo-probabilistic algorithm.[5] Each clinical finding entered into DXplain is assessed by determining the importance of the finding and how strongly the finding supports a given diagnosis for each disease in the knowledge base. Using this criterion, DXplain generates ranked differential diagnoses with the most likely diseases yielding the lowest rank. Using stored information regarding each disease’s prevalence and significance, the system differentiates between common and rare diseases.
Accuracy Analysis of accuracy has shown promise in DXplain and similar clinical decision support systems. In a preliminary trial investigation of 46 benchmark cases with a variety of diseases and clinical manifestations, the ranked differential diagnoses generated by DXplain were shown to be in alignment with a panel of five board-certified physicians.[6] In another study investigating how well decision support systems work at responding to a bioterrorism event, an evaluation of 103 consecutive internal medicine cases showed that Dxplain correctly identified the diagnosis in 73% of cases, with the correct diagnosis averaging a rank of 10.7.[7]
Clinical usage Despite its usage in clinician training, similar to other clinical decision support systems, DXplain has not expanded beyond the research laboratory or medical training setting, due in part to a lack of support by clinicians in real-world settings.[8]
References [1] Barnett GO, Cimino JJ, Hupp JA, Hoffer EP. DXplain. An evolving diagnostic decision-support system. JAMA. 1987 Jul 3;258(1):67-74. [2] MGH Laboratory of Computer Science – projects – dxplain,” Laboratory of Computer Science, Massachusetts General Hospital. 2007. (http:/ / lcs. mgh. harvard. edu/ projects/ dxplain. html) [3] London S. DXplain: a Web-based diagnostic decision support system for medical students. Med Ref Serv Q. 1998 Summer;17(2):17-28. [4] Computer Science and Artificial Intelligence Laboratory (CSAIL), MIT. 2005. (http:/ / groups. csail. mit. edu/ medg/ courses/ 6872/ 2004/ DXp HST Lec 05. pdf)
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DXplain [5] Detmer WM, Shortliffe EH. Using the Internet to Improve Knowledge Diffusion in Medicine. Communications of the Association of Computing Machinery. 1997; 40(8):101-108. 1997. [6] Feldman MJ, Barnett GO. An approach to evaluating the accuracy of DXplain. Comput Methods Programs Biomed. 1991 Aug;35(4):261-6. [7] Bravata DM, Sundaram V, McDonald KM, Smith WM, Szeto H, Schleinitz MD, et al. Detection and diagnostic decision support systems for bioterrorism response. Emerg Infect Dis. 2004 Jan. (http:/ / www. cdc. gov/ ncidod/ EID/ vol10no1/ 03-0243. htm) [8] Coiera E. Guide to Health Informatics: 2nd Edition. Arnold, 2003; 332-343.
External links • DXplain at the MGH Lab of Computer Science (http://www.lcs.mgh.harvard.edu/projects/dxplain.html)
Internist-I INTERNIST-I was a broad-based computer-assisted diagnostic tool developed in the early 1970s at the University of Pittsburgh as an educational experiment. The system was designed to capture the expertise of just one man, Jack D. Myers, MD, chairman of internal medicine in the University of Pittsburgh School of Medicine. The Division of Research Resources and the National Library of Medicine funded INTERNIST-I. Other major collaborators on the project included Randolph A. Miller and Harry E. Pople.
Development INTERNIST-I is the successor of the DIALOG system. For ten years, INTERNIST-I was the centerpiece of a Pittsburgh course entitled “The Logic of Problem-Solving in Clinical Diagnosis.” In consultation with faculty experts, much responsibility for data entry and updating of the system fell to the fourth-year medical students enrolled in the course. These students encoded the findings of standard clinicopathological reports. By 1982, the INTERNIST-I project represented fifteen person-years of work, and by some reports covered 70-80% of all the possible diagnoses in internal medicine. Data input into the system by operators included signs and symptoms, laboratory results, and other items of patient history. The principal investigators on INTERNIST-I did not follow other medical expert systems designers in adopting Bayesian statistical models or pattern recognition. This was because, as Myers explained, “The method used by physicians to arrive at diagnoses requires complex information processing which bears little resemblance to the statistical manipulations of most computer-based systems.” INTERNIST-I instead used a powerful ranking algorithm to reach diagnoses in the domain of internal medicine. The heuristic rules that drove INTERNIST-I relied on a partitioning algorithm to create problems areas, and exclusion functions to eliminate diagnostic possibilities. These rules, in turn, produce a list of ranked diagnoses based on disease profiles existing in the system’s memory. When the system was unable to make a determination of diagnosis it asked questions or offered recommendations for further tests or observations to clear up the mystery. INTERNIST-I worked best when only a single disease was expressed in the patient, but handled complex cases poorly, where more than one disease was present. This was because the system exclusively relied on hierarchical or taxonomic decision-tree logic, which linked each disease profile to only one “parent” disease class.
Use of INTERNIST-I By the late 1970s, INTERNIST-I was in experimental use as a consultant program and educational “quizmaster” at Presbyterian-University Hospital in Pittsburgh. INTERNIST-I’s designers hoped that the system could one day become useful in remote environments—rural areas, outer space, and foreign military bases, for instance—where experts were in short supply or unavailable. Still, physicians and paramedics wanting to use INTERNIST-I found the training period lengthy and the interface unwieldy. An average consultation with INTERNIST-I required about thirty to ninety minutes, too long for most clinics. To meet this challenge, researchers at nearby Carnegie Mellon
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Internist-I University wrote a program called ZOG that allowed those unfamiliar with the system to master it more rapidly. INTERNIST-I never moved beyond its original status as a research tool. In one instance, for example, a failed attempt to extract “synthetic” case studies of “artificial patients” from the system’s knowledge base in the mid-1970s overtly demonstrated its “shallowness” in practice.
INTERNIST-I and QMR In the first version of INTERNIST-I (completed in 1974) the computer program “treated the physician as unable to solve a diagnostic problem,” or as a “passive observer” who merely performed data entry. Miller and his collaborators came to see this function as a liability in the 1980s, referring to INTERNIST-I derisively as an example of the outmoded “Greek Oracle” model for medical expert systems. In the mid-1980s INTERNIST-I was succeeded by a powerful microcomputer-based consultant developed at the University of Pittsburgh called Quick Medical Reference (QMR). QMR, meant to rectify the technical and philosophical deficiencies of INTERNIST-I, still remained dependent on many of the same algorithms developed for INTERNIST-I, and the systems are often referred to together as INTERNIST-I/QMR. The main competitors to INTERNIST-I included CASNET, MYCIN, and PIP.
References • Gregory Freiherr, The Seeds of Artificial Intelligence: SUMEX-AIM (NIH Publication 80-2071, Washington, D.C.: National Institutes of Health, Division of Research Resources, 1979). • Randolph A. Miller, et al., “INTERNIST-1: An Experimental Computer-Based Diagnostic Consultant for General Internal Medicine,” New England Journal of Medicine 307 (August 19, 1982): 468-76. • Jack D. Myers, “The Background of INTERNIST-I and QMR,” in A History of Medical Informatics, eds. Bruce I. Blum and Karen Duncan (New York: ACM Press, 1990), 427-33. • Jack D. Myers, et al., “INTERNIST: Can Artificial Intelligence Help?” in Clinical Decisions and Laboratory Use, eds. Donald P. Connelly, et al., (Minneapolis: University of Minnesota Press, 1982), 251-69. • Harry E. Pople, Jr., “Presentation of the INTERNIST System,” in Proceedings of the AIM Workshop (New Brunswick, N.J.: Rutgers University, 1976).
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Mycin
Mycin MYCIN was an early expert system that used artificial intelligence to identify bacteria causing severe infections, such as bacteremia and meningitis, and to recommend antibiotics, with the dosage adjusted for patient's body weight — the name derived from the antibiotics themselves, as many antibiotics have the suffix "-mycin". The Mycin system was also used for the diagnosis of blood clotting diseases. MYCIN was developed over five or six years in the early 1970s at Stanford University. It was written in Lisp as the doctoral dissertation of Edward Shortliffe under the direction of Bruce Buchanan, Stanley N. Cohen and others. It arose in the laboratory that had created the earlier Dendral expert system. MYCIN was never actually used in practice but research indicated that it proposed an acceptable therapy in about 69% of cases, which was better than the performance of infectious disease experts who were judged using the same criteria.
Method MYCIN operated using a fairly simple inference engine, and a knowledge base of ~600 rules. It would query the physician running the program via a long series of simple yes/no or textual questions. At the end, it provided a list of possible culprit bacteria ranked from high to low based on the probability of each diagnosis, its confidence in each diagnosis' probability, the reasoning behind each diagnosis (that is, MYCIN would also list the questions and rules which led it to rank a diagnosis a particular way), and its recommended course of drug treatment. Despite MYCIN's success, it sparked debate about the use of its ad hoc, but principled, uncertainty framework known as "certainty factors". The developers performed studies showing that MYCIN's performance was minimally affected by perturbations in the uncertainty metrics associated with individual rules, suggesting that the power in the system was related more to its knowledge representation and reasoning scheme than to the details of its numerical uncertainty model. Some observers felt that it should have been possible to use classical Bayesian statistics. MYCIN's developers argued that this would require either unrealistic assumptions of probabilistic independence, or require the experts to provide estimates for an unfeasibly large number of conditional probabilities. Subsequent studies later showed that the certainty factor model could indeed be interpreted in a probabilistic sense, and highlighted problems with the implied assumptions of such a model. However the modular structure of the system would prove very successful, leading to the development of graphical models such as Bayesian networks.
Results Research conducted at the Stanford Medical School found MYCIN to propose an acceptable therapy in about 69% of cases, which was better than the performance of infectious disease experts who were judged using the same criteria. This study is often cited as showing the potential for disagreement about thereapeutic decisions, even among experts, when there is no "gold standard" for correct treatment.
Practical use MYCIN was never actually used in practice. This wasn't because of any weakness in its performance. As mentioned, in tests it outperformed members of the Stanford medical school faculty. Some observers raised ethical and legal issues related to the use of computers in medicine — if a program gives the wrong diagnosis or recommends the wrong therapy, who should be held responsible? However, the greatest problem, and the reason that MYCIN was not used in routine practice, was the state of technologies for system integration, especially at the time it was developed. MYCIN was a stand-alone system that required a user to enter all relevant information about a patient by typing in response to questions that MYCIN would pose. The program ran on a large time-shared system, available over the
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Mycin early Internet (ARPANet), before personal computers were developed. In the modern era, such a system would be integrated with medical record systems, would extract answers to questions from patient databases, and would be much less dependent on physician entry of information. In the 1970s, a session with MYCIN could easily consume 30 minutes or more—an unrealistic time commitment for a busy clinician. MYCIN's greatest influence was accordingly its demonstration of the power of its representation and reasoning approach. Rule-based systems in many non-medical domains were developed in the years that followed MYCIN's introduction of the approach. In the 1980s, expert system "shells" were introduced (including one based on MYCIN, known as E-MYCIN (followed by KEE)) and supported the development of expert systems in a wide variety of application areas. A difficulty that rose to prominence during the development of MYCIN and subsequent complex expert systems has been the extraction of the necessary knowledge for the inference engine to use from the human expert in the relevant fields into the rule base (the so-called "knowledge acquisition bottleneck").
References • The AI Business: The commercial uses of artificial intelligence, ed. Patrick Winston and Karen A. Prendergast. ISBN 0-262-23117-4.
External links • Rule-Based Expert Systems: The MYCIN Experiments of the Stanford Heuristic Programming Project (http:// aitopics.net/RuleBasedExpertSystems) -(edited by Bruce G. Buchanan and Edward H. Shortlife; ebook version) • TMYCIN (http://www.cs.utexas.edu/users/novak/tmycin.html), system based on MYCIN • "Mycin Expert System (http://raa.ruby-lang.org/project/mycin/): A Ruby Implementation" • "MYCIN: A Quick Case Study" (http://cinuresearch.tripod.com/ai/www-cee-hw-ac-uk/_alison/ai3notes/ section2_5_5.html) • " SOME EXPERT SYSTEM NEED COMMON SENSE" (http://www-formal.stanford.edu/jmc/someneed/ someneed.html) -(by John McCarthy) • "Expert Systems" (http://www.cs.cf.ac.uk/Dave/AI1/mycin.html)
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Physicians' Information and Education Resource
Physicians' Information and Education Resource The Physicians' Information and Education Resource (PIER) is an electronic, evidence-based, decision-support tool designed for point-of-care use by internists and other physicians. PIER is a product of the American College of Physicians and was launched in 2002.[1] Information in PIER is presented in a "drill down" format, in which the user clicks from an opening guidance statement through to specific recommendations for treatment and the evidence used to formulate the recommendations. The South Central Chapter of the Medical Library Association (SCC/MLA) ranked ACP's Physicians' Information and Education Resource (PIER) as the leading evidence-based, point-of-care tool compared to 13 other major evidence-based medical resources.[2] This top grade given by the South Central Chapter is significant, as the national organization represents more than 1,100 institutions in the health sciences information field. Recently, at the Medical Library Association Annual Meeting, a poster was presented by researchers at the University of Toronto Libraries that placed PIER at the top of the distillation pyramid, where evidence-based material is at its most distilled.[3]
Breadth Over 490 modules are included on topics in five areas: • • • • •
Diseases Screening and Prevention Complementary/Alternative Medicine Ethical and Legal Issues Procedures
Depth All guidance statements and recommendations are given a strength of recommendation rating to help clinicians assess their usefulness. All references included in PIER are also rated on the level of evidence they represent. All PIER modules are written by experts in the field and are peer reviewed. Modules are updated on a regular basis.[4] The disease-based modules are PIER's core. Each module presents a series of succinct guidance statements and practice recommendations supported by more detailed levels of pertinent rationale and evidence. There are links to abstracts and the full text of carefully selected references; to a comprehensive drug resource; to other ACP knowledge resources, including guidelines, the Medical Knowledge Self-Assessment Program (MKSAP [5]), and Annals of Internal Medicine (including ACP Journal Club [6]); and other resources, including PubMed and various Web sites. Tables, figures, algorithms, and video and audio clips are also included.
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Physicians' Information and Education Resource
Availability PIER is freely available to members of the American College of Physicians as a Web-based service. ACP licenses PIER to institutions and other customers directly or through business partners, who provide it in stand-alone form or integrated with other resources. It is available in desktop and handheld formats. PIER has also been integrated into electronic health records systems.
References [1] PIER - Who We Are (http:/ / pier. acponline. org/ who_we_are. html) [2] Trumble JM, et al. 2006. A Systematic Evaluation of Evidence Based Medicine Tools for Point-of-Care. Presented at South Central Chapter/Medical Library Association, October 2006. original paper (http:/ / ils. mdacc. tmc. edu/ papers. html) summary (http:/ / pier. acponline. org/ ebmstudy. html) [3] R. Vine, R. Shaughnessy, M. Thuna. 2008. EBM Tool-Picking Made Easy. Presented at Medical Library Association Annual Meeting 2008. poster PDF (http:/ / www. mlanet. org/ am/ am2008/ e-present/ 20080518_056_shaughnessy. pdf) [4] PIER Update Process (http:/ / pier. acponline. org/ overview. html) - development and update process [5] http:/ / mksap. acponline. org [6] http:/ / www. acpjc. org
External links • Official website (http://pier.acponline.org)
RetroGuide
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RetroGuide
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RetroGuide is a name of a research project in medical informatics (or more precisely clinical informatics) focusing on using workflow technology in healthcare. In 2009, RetroGuide became a component in a larger project/system called HealthFlow This is an ongoing project. Several past phases could be described. Initial phase was at Intermountain Health Care in Utah, USA and happened during 2004-2007. The main mode was retrospective use. The development continues at Marshfield Clinic (Marshfield Clinic Biomedical Informatics Research Center) during 2008-2010. The prospective version of the RetroGuide system has been developed (called FlowGuide) and the overal system which is using a workflow engine within an EHR system is called HealthFlow. RetroGuide uses a flowchart paradigm to represent knowledge. Knowledge represented can be a prospective alert logic or retrospective EHR query question. RetroGuide has been used on several clinical problems • investigating blood pressure control in diabetics patients • investigating pregnancy rate in female patients after treatment of Hodgkin lymphoma • investigating HEDIS quality improvement measures from NCQA • osteoporosis measure (OMW) • cholesterol control in cardiovascular patients (CMC) • investigating alternative glucose protocol logic for blood glucose control in ICU patients • course of care for diabetes
Scenario in RetroGuide
• course of care for AMI • course of care for chronic kidney disease • detecting adverse drug events (respiratory failure after use of narcotics, Naloxone)
Evaluation RetroGuide graphical approach to model queries has been formally evaluated in a study involving 18 human subjects with limited database expertise. The study compared RetroGuide technology with SQL. Each subject had to solve 14 analytical tasks using both compared technologies. The qualitative comparison of average test scores showed that the study subjects achieved significantly higher scores using the RG technology. Each subject also filled a follow-up questionnaire which compared both technologies qualitatively. The results of this qualitative study showed that 94% of subjects preferred RG to SQL because RetroGuide was easier to learn, it better supported temporal tasks, and it seemed to be a more logical modeling paradigm. The second part of the follow-up qualitative questionnaire also asked RG-specific questions based on validated constructs from the Unified Theory of Acceptance and Use of Technology. The results of this second part suggested that a fully developed, RetroGuide-like technology would be well accepted by users.
RetroGuide
References Scientific Articles • Huser V, Rocha RA, James BC, "Use of Workflow Technology Tools to Analyze Medical Data," pp.455–460, 19th IEEE Symposium on Computer-Based Medical Systems (CBMS'06), 2006. article (doi) (http://doi. ieeecomputersociety.org/10.1109/CBMS.2006.162) • Huser V, "Running Decision Support Logic Retrospectively to Determine Guideline Adherence: a Case Study With Diabetes," Spring AMIA2007 symposium poster (http://workflow.minfor.net/ebox/posters/ RetroGuide-diabetes_and_hypertension.jpg) conference (http://www.amia.org/meetings/s07/track_cds.asp) • Huser V, Rocha, RA, "Analyzing medical data from multi-hospital healthcare information system using graphical flowchart models," BMIC Symposium, Orlando, 2007 (accepted). • Huser V, Rocha RA, Huser M, "Conducting Time Series Analyses on Large Data Sets: a Case Study With Lymphoma," Medinfo 2007, Brisbane, 2007. • Huser V, Rocha RA, "Retrospective Analysis of the Electronic Health Record of Patients Enrolled in a Computerized Glucose Management Protocol," CBMS 2007 DOI link (http://doi.ieeecomputersociety.org/10. 1109/CBMS.2007.93) • Huser V, Rocha RA, "Graphical Modeling of HEDIS Quality Measures and Prototyping of Related Decision Support Rules to Accelerate Improvement", AMIA Fall Symposium, 2007, Chicago, USA poster (http:// vojtechhuser.minfor.net/ebox/15-posters/2007_huser-hedis-amia.pdf) • Huser V, Rocha RA, "Graphical Modeling of HEDIS Quality Measures and Prototyping of Related Decision Support Rules to Accelerate Improvement", AMIA Fall Symposium, 2007, Chicago, USA poster (http:// vojtechhuser.minfor.net/ebox/15-posters/2007_huser-hedis-amia.pdf) • Huser V, Peissig PL, Christensen CA, Starren JB. Evaluation of commercial workflow engine for modeling clinical processes in quality improvement and decision support. Proc of 15th Annual HMO Research Network Conference 2009. • Huser V, Rocha RA, Narus SP. Evaluation of a flowchart-based EHR query system: a case study of RetroGuide. J Biomed Inform 2009;ePub ahead of print PubMed (http://www.ncbi.nlm.nih.gov/pubmed/19560553) DOI link (http://dx.doi.org/10.1016/j.jbi.2009.06.001) • Huser V, Rasmussen L, Starren JB. Representing clinical processes in XML process definition language (XPDL). AMIA Spring Symp 2009. • Huser V, Starren JB. EHR Data Pre-processing Facilitating Process Mining: an Application to Chronic Kidney Disease. AMIA Annu Symp Proc 2009 (accepted) 2009.
External links • • • • •
http://workflow.minfor.net http://minfor.wikispaces.com/RetroGuide http://retroguide.blip.tv RetroGuide on SourceForge (RetroGuide Express) (http://retroguideexpr.wiki.sourceforge.net) http://healthcareworkflow.wordpress.com
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STD Wizard
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STD Wizard Sexually transmitted infection Classification and external resources
U.S. propaganda poster targeted at World WarII soldiers and sailors appealed to their patriotism in urging them to protect themselves. The text at the bottom of the poster reads, "You can't beat the Axis if you get VD." Images of women were used to catch the eye on many VD posters. [1]
ICD-10
A64
ICD-9
099.9
DiseasesDB
27130
MeSH
D012749
[2] [3] [4]
Sexually transmitted diseases (STD), also referred to as sexually transmitted infections (STI) and venereal diseases (VD), are illnesses that have a significant probability of transmission between humans by means of sexual behavior, including vaginal intercourse, oral sex, and anal sex. While in the past, these illnesses have mostly been referred to as STDs or VD, in recent years the term sexually transmitted infections (STIs) has been preferred, as it has a broader range of meaning; a person may be infected, and may potentially infect others, without having a disease. Some STIs can also be transmitted via the use of IV drug needles after its use by an infected person, as well as through childbirth or breastfeeding. Sexually transmitted infections have been well known for hundreds of years, and venereology is the branch of medicine that studies these diseases.
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Classification Until the 1990s, STIs were commonly known as venereal diseases, the word venereal being derived from the Latin word venereus, and meaning relating to sexual intercourse or desire, ultimately derived from Venus, the Roman goddess of love. Social disease was a phrase used as a euphemism. Sexually transmitted infection is a broader term than sexually transmitted disease. An infection is a colonization by a parasitic species, which may not cause any adverse effects. In a disease, the infection leads to impaired or abnormal function. In either case, the condition may not exhibit signs or symptoms. Increased understanding of infections like HPV, which infects most sexually active individuals but cause disease in only a few has led to increased use of the term STI. Public health officials originally introduced the term sexually transmitted infection, which clinicians are increasingly using alongside the term sexually transmitted disease in order to distinguish it from the former.
A poster from the Office for Emergency Management. Office of War Information, 1941-1945
STD may refer only to infections that are causing diseases, or it may be used more loosely as a synonym for STI. Most of the time, people do not know that they are infected with an STI until they are tested or start showing symptoms of disease.
Moreover, the term sexually transmissible disease is sometimes used since it is less restrictive in consideration of other factors or means of transmission. For instance, meningitis is transmissible by means of sexual contact but is not labeled as an STI because sexual contact is not the primary vector for the pathogens that cause meningitis. This discrepancy is addressed by the probability of infection by means other than sexual contact. In general, an STI is an infection that has a negligible probability of transmission by means other than sexual contact, but has a realistic means of transmission by sexual contact (more sophisticated means—blood transfusion, sharing of hypodermic needles—are not taken into account). Thus, one may presume that, if a person is infected with an STI, e.g., chlamydia, gonorrhea, genital herpes, it was transmitted to him/her by means of sexual contact. The diseases on this list are most commonly transmitted solely by sexual activity. Many infectious diseases, including the common cold, influenza, pneumonia, and most others that are transmitted person-to-person can also be transmitted during sexual contact, if one person is infected, due to the close contact involved. However, even though these diseases may be transmitted during sex, they are not considered STIs.
Cause Transmission
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Risk per unprotected sexual act with an infected person Known risks Performing oral sex on a man
• • • • •
Performing oral sex on a woman • •
Possible
Throat chlamydia • Throat gonorrhea (25–30%) • Herpes (rare) • HPV Syphilis (1%)
Hepatitis B (low risk) HIV (0.01%) Hepatitis C (unknown)
Herpes HPV
• •
Throat gonorrhea Throat chlamydia
Receiving oral sex—man
• • • •
Chlamydia Gonorrhea Herpes Syphilis (1%)
•
HPV
Receiving oral sex—woman
•
Herpes
• • •
HPV Bacterial Vaginosis Gonorrhea
Vaginal sex—man
• • • • • • • • • •
Chlamydia (30–50%) • Crabs Scabies Gonorrhea (22%) Hepatitis B Herpes (0.07% for HSV-2) HIV (0.05%) HPV (high: around 40-50%) Syphilis Trichomoniasis
Hepatitis C
Vaginal sex—woman
• • • • • • • • • •
Chlamydia (30–50%) • Crabs Scabies Gonorrhea (47%) Hepatitis B (50–70%) Herpes HIV (0.1%) HPV (high; around 40-50%) Syphilis Trichomoniasis
Hepatitis C
Anal sex—insertive
• • • • • • • • •
Chlamydia Crabs Scabies (40%) Gonorrhea Hepatitis B Herpes HIV (0.62%) HPV Syphilis (14%)
•
Hepatitis C
Anal sex—receptive
• • • • • • • • •
Chlamydia Crabs Scabies Gonorrhea Hepatitis B Herpes HIV (1.7%) HPV Syphilis (1.4%)
•
Hepatitis C
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• • • • •
Amebiasis Cryptosporidiosis (1%) Giardiasis Hepatitis A (1%) Shigellosis (1%)
•
HPV (1%)
The risks and transmission probabilities of sexually transmitted diseases are summarized by act in the tableo the right.[5][][][6]
Bacterial • • • • •
Chancroid (Haemophilus ducreyi) Chlamydia (Chlamydia trachomatis) Gonorrhea (Neisseria gonorrhoeae), colloquially known as "the clap" Granuloma inguinale or (Klebsiella granulomatis) Syphilis (Treponema pallidum)
Fungal • Candidiasis (yeast infection)
Viral • Viral hepatitis (Hepatitis B virus)—saliva, venereal fluids. (Note: Hepatitis A and Hepatitis E are transmitted via the fecal-oral route; Hepatitis C is rarely sexually transmittable, and the route of transmission of Hepatitis D (only if infected with B) is uncertain, but may include sexual transmission.) • Herpes simplex (Herpes simplex virus 1, 2) skin and mucosal, transmissible with or without visible blisters • HIV (Human Immunodeficiency Virus)—venereal fluids, semen, breast milk, blood • HPV (Human Papillomavirus)—skin and mucosal contact. 'High risk' types of HPV cause almost all cervical cancers, as well as some anal, penile, and vulvar cancer. Some other types of HPV cause genital warts.
Micrograph showing the viral cytopathic effect of herpes (ground glass nuclear inclusions, multi-nucleation). Pap test. Pap stain.
• Molluscum contagiosum (molluscum contagiosum virus MCV)—close contact
Parasites • Crab louse, colloquially known as "crabs" or "pubic lice" (Pthirus pubis) • Scabies (Sarcoptes scabiei)
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Protozoal • Trichomoniasis (Trichomonas vaginalis), colloquially known as "trich"
Pathophysiology Many STIs are (more easily) transmitted through the mucous membranes of the penis, vulva, rectum, urinary tract and (less often—depending on type of infection) the mouth, throat, respiratory tract and eyes.[7] The visible membrane covering the head of the penis is a mucous membrane, though it produces no mucus (similar to the lips of the mouth). Mucous membranes differ from skin in that they allow certain pathogens into the body.[8] The amount of contact with infective sources which causes infection varies with each pathogen but in all cases a disease may result from even light contact from fluid carriers like venereal fluids onto a mucous membrane. This is one reason that the probability of transmitting many infections is far higher from sex than by more casual means of transmission, such as non-sexual contact—touching, hugging, shaking hands—but it is not the only reason. Although mucous membranes exist in the mouth as in the genitals, many STIs seem to be easier to transmit through oral sex than through deep kissing. According to a safe sex chart, many infections that are easily transmitted from the mouth to the genitals or from the genitals to the mouth are much harder to transmit from one mouth to another.[9] With HIV, genital fluids happen to contain much more of the pathogen than saliva. Some infections labeled as STIs can be transmitted by direct skin contact. Herpes simplex and HPV are both examples. KSHV, on the other hand, may be transmitted by deep-kissing but also when saliva is used as a sexual lubricant. Depending on the STI, a person may still be able to spread the infection if no signs of disease are present. For example, a person is much more likely to spread herpes infection when blisters are present than when they are absent. However, a person can spread HIV infection at any time, even if he/she has not developed symptoms of AIDS. All sexual behaviors that involve contact with the bodily fluids of another person should be considered to contain some risk of transmission of sexually transmitted diseases. Most attention has focused on controlling HIV, which causes AIDS, but each STI presents a different situation. As may be noted from the name, sexually transmitted diseases are transmitted from one person to another by certain sexual activities rather than being actually caused by those sexual activities. Bacteria, fungi, protozoa or viruses are still the causative agents. It is not possible to catch any sexually transmitted disease from a sexual activity with a person who is not carrying a disease; conversely, a person who has an STI got it from contact (sexual or otherwise) with someone who had it, or his/her bodily fluids. Some STIs such as HIV can be transmitted from mother to child either during pregnancy or breastfeeding. Although the likelihood of transmitting various diseases by various sexual activities varies a great deal, in general, all sexual activities between two (or more) people should be considered as being a two-way route for the transmission of STIs, i.e., "giving" or "receiving" are both risky although receiving carries a higher risk. Healthcare professionals suggest safer sex, such as the use of condoms, as the most reliable way of decreasing the risk of contracting sexually transmitted diseases during sexual activity, but safer sex should by no means be considered an absolute safeguard. The transfer of and exposure to bodily fluids, such as blood transfusions and other blood products, sharing injection needles, needle-stick injuries (when medical staff are inadvertently jabbed or pricked with needles during medical procedures), sharing tattoo needles, and childbirth are other avenues of transmission. These different means put certain groups, such as medical workers, and haemophiliacs and drug users, particularly at risk. Recent epidemiological studies have investigated the networks that are defined by sexual relationships between individuals, and discovered that the properties of sexual networks are crucial to the spread of sexually transmitted diseases. In particular, assortative mixing between people with large numbers of sexual partners seems to be an important factor.
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It is possible to be an asymptomatic carrier of sexually transmitted diseases. In particular, sexually transmitted diseases in women often cause the serious condition of pelvic inflammatory disease.
Prevention Prevention is key in addressing incurable STIs, such as HIV and herpes. Sexual health clinics promote the use of condoms and provide outreach for at-risk communities. The most effective way to prevent sexual transmission of STIs is to avoid contact of body parts or fluids which can lead to transfer with an infected partner. Not all sexual activities involve contact: cybersex, phonesex or masturbation from a distance are methods of avoiding contact. Proper use of condoms reduces contact and risk. Although a condom is effective in limiting exposure, some disease transmission may occur even with a condom. Ideally, both partners should get tested for STIs before initiating sexual contact, or before resuming contact if a partner engaged in contact with someone else. Many infections are not detectable immediately after exposure, so enough time must be allowed between possible exposures and testing for the tests to be accurate. Certain STIs, particularly certain persistent viruses like HPV, may be impossible to detect with current medical procedures.
San Francisco City Clinic a municipal STI testing center in San Francisco.
Many diseases that establish permanent infections can so occupy the immune system that other diseases become more easily transmitted. The innate immune system led by defensins against HIV can prevent transmission of HIV when viral counts are very low, but if busy with other viruses or overwhelmed, HIV can establish itself. Certain viral STI's also greatly increase the risk of death for HIV infected patients.
Vaccines Vaccines are available that protect against some viral STIs, such as Hepatitis A, Hepatitis B, and some types of HPV. Vaccination before initiation of sexual contact is advised to assure maximal protection.
Condoms Condoms and female condoms only provide protection when used properly as a barrier, and only to and from the area that it covers. Uncovered areas are still susceptible to many STDs. In the case of HIV, sexual transmission routes almost always involve the penis, as HIV cannot spread through unbroken skin, thus properly shielding the insertive penis with a properly worn condom from the vagina or anus effectively stops HIV transmission. An infected fluid to broken skin borne direct transmission of HIV would not be considered "sexually transmitted", but can still theoretically occur during sexual contact, this can be avoided simply by not engaging in sexual contact when having open bleeding wounds. Other STIs, even viral infections, can be prevented with the use of latex, polyurethane or polyisoprene condoms as a barrier. Some microorganisms and viruses are small enough to pass through the pores in natural skin condoms, but are still too large to pass through latex or synthetic condoms. Proper usage entails: • Not putting the condom on too tight at the end, and leaving 1.5cm (3/4inch) room at the tip for ejaculation. Putting the condom on snug can and often does lead to failure. • Wearing a condom too loose can defeat the barrier. • Avoiding inverting, spilling a condom once worn, whether it has ejaculate in it or not.
STD Wizard • Avoiding condoms made of substances other than latex, polyisoprene or polyurethane that do not protect against HIV. • Avoiding the use of oil based lubricants (or anything with oil in it) with latex condoms, as oil can eat holes into them. • Using flavored condoms for oral sex only, as the sugar in the flavoring can lead to yeast infections if used to penetrate. Not following the first five guidelines above perpetuates the common misconception that condoms are not tested or designed properly.[citation needed] In order to best protect oneself and the partner from STIs, the old condom and its contents should be assumed to be infectious. Therefore the old condom must be properly disposed of. A new condom should be used for each act of intercourse, as multiple usage increases the chance of breakage, defeating the effectiveness as a barrier.
Nonoxynol-9 Researchers had hoped that nonoxynol-9, a vaginal microbicide would help decrease STI risk. Trials, however, have found it ineffective and it may put women at a higher risk of HIV infection.[10]
Diagnosis STI tests may test for a single infection, or consist of a number of individual tests for any of a wide range of STIs, including tests for syphilis, trichomonas, gonorrhea, chlamydia, herpes, hepatitis and HIV tests. No procedure tests for all infectious agents. STI tests may be used for a number of reasons: • as a diagnostic test to determine the cause of symptoms or illness • as a screening test to detect asymptomatic or presymptomatic infections • as a check that prospective sexual partners are free of disease before they engage in sex without safer sex precautions (for example, when starting a long term mutually monogamous sexual relationship, in fluid bonding, or for procreation). • as a check prior to or during pregnancy, to prevent harm to the baby • as a check after birth, to check that the baby has not caught an STI from the mother • to prevent the use of infected donated blood or organs • as part of the process of contact tracing from a known infected individual • as part of mass epidemiological surveillance Not all STIs are symptomatic, and symptoms may not appear immediately after infection. In some instances a disease can be carried with no symptoms, which leaves a greater risk of passing the disease on to others. Depending on the disease, some untreated STIs can lead to infertility, chronic pain or even death. Early identification and treatment results in less chance to spread disease, and for some conditions may improve the outcomes of treatment. There is often a window period after initial infection during which an STI test will be negative. During this period the infection may be transmissible. The duration of this period varies depending on the infection and the test. Diagnosis may also be delayed by reluctance of the infected person to seek a medical professional. One report indicated that afflicted people turn to the Internet rather than to a medical professional for information on STIs to a higher degree than for other sexual problems.
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Management High-risk exposure such as that which occurs in rape cases may be treated prophylactically using antibiotic combinations such as azithromycin, cefixime, and metronidazole. An option for treating partners of patients (index cases) diagnosed with chlamydia or gonorrhea is patient-delivered partner therapy, which is the clinical practice of treating the sex partners of index cases by providing prescriptions or medications to the patient to take to his/her partner without the health care provider first examining the partner.[11]
Epidemiology STD incidence rates remain high in most of the world, despite diagnostic and therapeutic advances that can rapidly render patients with many STDs noninfectious and cure most. In many cultures, changing sexual morals and oral contraceptive use have eliminated traditional sexual restraints, especially for women, and both physicians and patients have difficulty dealing openly and candidly with sexual Age-standardized, disability-adjusted life years issues. Additionally, development and spread of drug-resistant bacteria for STDs (excluding HIV) per 100,000 inhabitants in 2004. (e.g., penicillin-resistant gonococci) makes some STDs harder to cure. The effect of travel is most dramatically illustrated by the rapid spread of the AIDS virus (HIV-1) from Africa to Europe and the Americas in the late 1970s. In 1996, the World Health Organization estimated that more than 1 million people were being infected daily. About 60% of these infections occur in young people S n="" GTM>S n=$order(^nodex(n)) GTM>zwr n n=" building" GTM>S n=$order(^nodex(n)) GTM>zwr n n=" name:gd" GTM>S n=$order(^nodex(n)) GTM>zwr n n="%kml:guid" Here, the argument-less For repeats until stopped by a terminating Quit. This line prints a table of i and stuff(i) where i is successively 6, 10, and 15. Multi-User/Multi-Tasking/Multi-Processor: MUMPS supports multiple simultaneous users and processes even when the underlying operating system does not (e.g., MS-DOS). Additionally, there is the ability to specify an environment for a variable, such as by specifying a machine name in a variable (as in SET ^|"DENVER"|A(1000)="Foo"), which can allow you to access data on remote machines. For a thorough listing of the rest of the MUMPS commands, operators, functions and special variables, see these online resources: • MUMPS by Example [6], or the (out of print) book of the same name by Ed de Moel. Much of the language syntax is detailed there, with examples of usage. • The Annotated MUMPS Language Standard [7], showing the evolution of the language and differences between versions of the ANSI standard.
"MUMPS" vs. "M" naming debate While of little interest to those outside the MUMPS/M community, this topic has been contentious there. All of the following positions can be, and have been, supported by knowledgeable people at various times: • • • •
The language's name became M in 1993 when the M Technology Association adopted it. The name became M on December 8, 1995 with the approval of ANSI X11.1-1995 Both M and MUMPS are officially accepted names. M is only an "alternate name" or "nickname" for the language, and MUMPS is still the official name.
Some of the contention arose in response to strong M advocacy on the part of one commercial interest, InterSystems, whose chief executive disliked the name MUMPS and felt that it represented a serious marketing obstacle. Thus, favoring M to some extent became identified as alignment with InterSystems. The dispute also reflected rivalry between organizations (the M Technology Association, the MUMPS Development Committee, the ANSI and ISO Standards Committees) as to who determines the "official" name of the language. Some writers have attempted to
MUMPS defuse the issue by referring to the language as M[UMPS], square brackets being the customary notation for optional syntax elements. A leading authority, and the author of an open source MUMPS implementation, Professor Kevin O'Kane, uses only 'MUMPS'. The most recent standard (ISO/IEC 11756:1999, re-affirmed on 25 June 2010), still mentions both M and MUMPS as officially accepted names.
References [1] [2] [3] [4] [5]
http:/ / www. cs. uni. edu/ ~okane http:/ / www. iso. ch/ iso/ en/ CatalogueDetailPage. CatalogueDetail?CSNUMBER=29268& ICS1=35& ICS2=60& ICS3=& scopelist http:/ / www. iso. ch/ iso/ en/ CatalogueDetailPage. CatalogueDetail?CSNUMBER=29269& printable=true http:/ / www. iso. ch/ iso/ en/ CatalogueDetailPage. CatalogueDetail?printable=true& CSNUMBER=29270 The Annotated MUMPS Standards (http:/ / 71. 174. 62. 16/ Demo/ AnnoStd) - Ed De Moel, Jacquard Systems Research (http:/ / jacquardsystems. com/ ) [6] http:/ / 71. 174. 62. 16/ Demo/ AnnoStd?Frame=Main& Page=a100006 [7] http:/ / 71. 174. 62. 16/ Demo/ AnnoStd
Further reading • Walters, Richard (1989). "ABCs of MUMPS. 1989: Butterworth-Heinemann, ISBN 1-55558-017-3. • Walters, Richard (1997). M Programming: A Comprehensive Guide. Digital Press. ISBN 1-55558-167-6. • Lewkowicz, John. The Complete MUMPS : An Introduction and Reference Manual for the MUMPS Programming Language. ISBN 0-13-162125-4 • Kirsten, Wolfgang, et al. (2003) Object-Oriented Application Development Using the Caché Postrelational Database ISBN 3-540-00960-4 • Martínez de Carvajal Hedrich, Ernesto (1993). "El Lenguaje MUMPS". Completa obra en castellano sobre el lenguaje Mumps. ISBN 84-477-0125-5. Distribuido exclusivamente por su author ([emailprotected]) • O'Kane, K.C.; A language for implementing information retrieval software, Online Review, Vol 16, No 3, pp 127–137 (1992). • O'Kane, K.C.; and McColligan, E. E., A case study of a Mumps intranet patient record, Journal of the Healthcare Information and Management Systems Society, Vol 11, No 3, pp 81–95 (1997). • O'Kane, K.C.; and McColligan, E.E., A Web Based Mumps Virtual Machine, Proceedings of the American Medical Informatics Association 1997 • O'Kane, K.C., The Mumps Programming Language, Createspace, ISBN 1-4382-4338-3, 120 pages (2010).
External links • MUMPS Information (http://neamh.cns.uni.edu/MedInfo/mumps.html) • M Technology and MUMPS Language FAQ (http://www.faqs.org/faqs/m-technology-faq/part1/) (1999) General source; also specific source for the Poitras quote re the origin of the 1840 epoch. • Mumps MultiDimensional & Hierarchical Toolkit (GPL/LGPL), Kevin O'Kane Univ Northern Iowa (http:// www.cs.uni.edu/~okane/mumps.html) • Information Retrieval in Mumps (book) (http://www.cs.uni.edu/~okane/source/ISR/isr.html) • MDH Database Toolkit (http://www.cs.uni.edu/~okane/source/MUMPS-MDH/mdh.html) C++ class library to access O'Kane's Open Source Mumps • MDC - MUMPS Development Committee (http://71.174.62.16/MDC/) • The Annotated M{UMPS} Standards (http://71.174.62.16/Demo/AnnoStd) • Caché & MUMPS Technology Association of UK & Ireland (http://www.camta.net) • GT.M Open Source MUMPS System - Fidelity/Sanchez/Greystone (http://sourceforge.net/projects/fis-gtm) • MUMPS Systems - Source Forge index (http://mumps.sourceforge.net/)
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MUMPS • • • • • • • • • • • •
Globals: a primer for Relational Programmers (http://www.mgateway.com/extreme1.doc) The M Technology Resource Center (http://www.mcenter.com/mtrc/) M Links at Hardhats.org (http://www.hardhats.org/links/Mlinks.html) M21 - An ANSI M(UMPS) Implementation (http://www.m21.uk.com) EsiObjects (http://www.esiobjects.org) An Object Oriented extension of MUMPS InterSystems Caché (http://www.intersystems.com/cache/index.html) InterSystems M Technologies (http://www.openm.com/index.html) DSM, MSM and OpenM M/DB (http://www.mgateway.com/mdb.html) An Open Source MUMPS-based API-compatible alternative to SimpleDB MiniM Database Server, A MUMPS Implementation (http://www.minimdb.com) Development and Operation of a MUMPS Laboratory Information System: A Decade's Experience at Johns Hopkins Hospital (http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2245220/) IDEA Systems' technology solutions based on Caché and GT.M (http://www.idea.cz/technology) MUMPS documentation, topics, and resources (mixed Czech and English) (http://www.mumps.cz/)
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Internet Projects Bing Health Bing Health
Bing Health displaying results of the search term 'Common Cold' Developer(s)
Microsoft
Stable release
Final / January 12, 2010
Type
Search engine
Website
[1]
Bing Health (previously Live Search Health) is a health-related search service as part of Microsoft's Bing search engine. It is a search engine specifically for health-related information through a variety of trusted and credible sources, including Medstory, Mayo Clinic, National Institutes of Health's MedlinePlus, as well as from Wikipedia.
History Bing Health comes about as a result of the Microsoft's acquisition of Medstory in February 2007, gaining a foothold in the health search and health information market.[2] It was released for beta testing on October 8, 2007 as Live Search Health and served as the front-end to Microsoft HealthVault Search. Search results in Live Search Health were presented in a three-column layout with health-related articles from the trusted sources in the left, web search results in the middle, and sponsored results on the right. The topic dashboad on the top also displays relevant topics, and allow users to add the search results to their scrapbook in Microsoft HealthVault Account. One particular feature for Live Search Health is that all health search queries and responses were encrypted to provide a measure of privacy and security when dealing with health issues. However, on June 3, 2009, the Live Search Health front-end became fully integrated into Bing search results, accessible only via the "Explorer pane" on the left when the contextual search engine detects a health-related search query entered. On January 10, 2010, Bing Health search results got an upgrade. Typing in a specific illness will now highlight important information such as related conditions, and common medications to reduce symptoms. In addition reference materials and documentation about the disease and its history can be shown.[3] Bing Health is only available in the United States
Bing Health
References [1] http:/ / www. bing. com [2] Microsoft Press Release: Microsoft Demonstrates Further Commitment to Healthcare Market With Planned Acquisition of Web Search Company (http:/ / www. microsoft. com/ presspass/ press/ 2007/ feb07/ 02-26MSMedstoryPR. mspx) on 2007-02-26 [3] Bringing Knowledge into Health Search (http:/ / www. bing. com/ community/ blogs/ search/ archive/ 2010/ 01/ 12/ bringing-knowledge-into-health-search. aspx)
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Dossia Dossia Industry
Healthcare
Founder(s)
AT&T Applied Materials BP America Cardinal Health Intel Pitney Bowes sanofi-aventis Walmart Abraxis BioScience Vanguard Health Systems
Headquarters United States Cambridge, Massachusetts Key people
Michael Critelli, CEO Steve Munini, COO
Website
Dossia.org
[1]
Dossia is a Personal health record service offered by some of the largest employers in the United States. Along with Microsoft's HealthVault and Google's Google Health, Dossia is one of the largest PHR deployments in the world Unlike the other two large PHR efforts, Dossia is based on open source software. Dossia released their API in summer of 2009. Dossia differs from traditional tethered PHR services, by providing user access to health information regardless of health plan, employer or physician. Users also have the ability to download their full record, in electronic form, at any time. It is an initiative formed from the following companies: • • • • • • • • • •
AT&T Applied Materials BP America Cardinal Health Intel Pitney Bowes Sanofi-aventis Walmart Abraxis BioScience Vanguard Health Systems
Dossia is formed by some of the largest employers in the United States with the intention of offering a PHR to its employees. Given the number of employees of these combined institutions, Dossia could be one of the largest PHR systems in the world.
Dossia
Dossia’s History In 2006, a group of Fortune 500 employers, including Applied Materials, BP America, Inc, Intel Corporation, Pitney Bowes Inc., Wal-Mart, Cardinal Health, AT&T, and sanofi-aventis, formed an alliance called the Dossia Consortium. Later, in April 2009, Dossia announced that Abraxis BioScience also joined the Dossia Founders Group. The Consortium’s goal was to empower their employees to make smarter, more informed decisions about their healthcare by offering them Personally Controlled Health Records (PCHR) . The Consortium funded the development of a web-based framework through which Consortium employees, dependents, and retirees can maintain private, personal, and portable PCHRs. In 2008, the Dossia Consortium Board of Directors decided to create two additional organizations within the Dossia umbrella. These included the Dossia Foundation and the Dossia Service Corporation. The Dossia Foundation aims at advancing knowledge and progress in the healthcare space through a variety research, strategy, and advocacy initiatives pertaining to Personally Controlled Health Records (PCHRs). The Dossia Service Corporation is responsible for delivering the PCHR infrastructure and service to subscribing employers and customers. Also during 2008, Dossia established an agreement to work with Children’s Hospital Boston to provide strategic and technological expertise and guidance in creating, deploying and operating the electronic health record infrastructure. In fall of 2008, WalMart was the first Dossia Consortium member to roll out the PCHRs to their 1.4 million employees plus their dependents. Following WalMart’s roll out, Dossia continued to roll out its PCHRs to the other Dossia Consortium members including Vanguard, Intel, Pitney Bowes, AT&T and BP America.
Dossia’s PHP(Personal Health Platform) The Dossia system enables individuals to gather copies of their own medical data (in digital form) from multiple sources and to create and utilize their own personal, private and portable electronic health records. Initially, the data will come primarily from insurers’ databases and the patient’s own annotations. As the system develops, additional information will come directly from the patient’s medical chart and various other sources. The information the system provides will empower individuals to manage their own healthcare, improve communications with their doctors, and ensure more complete and accurate information for healthcare providers than the current fragmented, paper-based system.
Dossia Board Members Dossia’s board is composed of industry leaders from a variety of Fortune 500 companies. • • • • • • • • • •
Craig Barrett, Chairman, Board Of Directors, Dossia, Executive Chairman, (formerly) Intel Jean Paul Gagnon, Secretary, Board of Directors, Dossia Director of Public Policy, sanofi-aventis Monica Foster, Vice President of Benefits, Cardinal Health Karl Dalal, Director of U.S. Health and Welfare Benefits, BP America Adena Handly,Director, Healthcare Marketing, AT&T Business Solutions Diana Finucane, Director, Global Benefits Applied Materials Andrew Gold, Executive Director, Global Benefit Planning, Pitney Bowes Steven Lampkin, Vice President, Benefits Services and Strategic Initiatives, Walmart Patrick Soon-Shiong, M.D., Executive Chairman and Chief Executive Officer, Abraxis Health Brad Perkins, M.D., Executive VP Strategy and Innovation and Chief Transformation Officer, Vanguard Health Systems • Tami L. Graham, Intel Global Benefits Design Director, Intel Corporation • Liz Cirri, Senior Director, Reimbursement, Government Programs, sanofi-aventis
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Notes [1] http:/ / www. dossia. org/
External links • Dossia Official Site (http://www.dossia.org/)
E-Patient An e-patient is a health consumer who participates fully in his/her medical care. Sometimes referred to as an "internet patient," e-patients see themselves as equal partners with their doctors in the healthcare process. E-patients gather information about medical conditions that impact them and their families, using electronic communication tools (including Web 2.0 tools) in coping with medical conditions. The term encompasses both those who seek guidance for their own ailments and the friends and family members (e-caregivers) who go online on their behalf. e-Patients report two effects of their health research: "better health information and services, and different (but not always better) relationships with their doctors." [1] e-Patients are active in their care and are demonstrating the power of the Participatory Medicine or Health 2.0 / Medicine 2.0.[2] model of care. The "e" can stand for electronic but can also stand for: • • • • • • •
Equipped with the skills to manage their own condition. Enabled to make choices about self-care and those choices are respected. Empowered Engaged patients are engaged in their own care Equals in their partnerships with the various physicians involved in their care Emancipated Expert patients can improve their self-rated health status, cope better with fatigue and other generic features of chronic disease such as role limitation, and reduce disability and their dependence on hospital care.
Based on the current state of knowledge on the impact of e-patients on the healthcare system and the quality of care received: • A growing number of people say the internet has played a crucial or important role as they helped another person cope with a major illness.[3] • Since the advent of the Internet, many clinicians have underestimated the benefits and overestimated the risks of online health resources for patients. • Medical online support groups have become an important healthcare resource. • “…the net friendliness of clinicians and provider organizations—as rated by the e-patients they serve—is becoming an important new aspect of healthcare quality.” • This is one the most important cultural medical revolution of the past century, mediated and driven by technology. • In order to understand the impact of the e-patient, clinicians will likely need to move beyond “pre-internet medical constructs.” Research must combine expertise from specialties that are not used to work together.[citation needed] • It is crucial for medical education to take the e-patient into account, and to prepare students for medical practice that encompasses the e-patient The proportion of e-patients in selected patient populations seem to be highest in the US and Canada.[citation needed]
E-Patient
References [1] Fox, Susannah; Fallows, Deborah. 2003. Health searches and email have become more commonplace, but there is room for improvement in searches and overall Internet access. (http:/ / www. pewinternet. org/ ~/ media/ / Files/ Reports/ 2003/ PIP_Health_Report_July_2003. pdf. pdf) [2] Eysenbach G Medicine 2.0: Social Networking, Collaboration, Participation, Apomediation, and Openness (http:/ / www. jmir. org/ 2008/ 3/ e22/ ). J Med Internet Res 2008;10(3):e22 [3] Finding Answers Online in Sickness and in Health, 5/2/2006, Pew Internet (http:/ / www. pewinternet. org/ pdfs/ PIP_Health_Decisions_2006. pdf).
External links • van Woerkum CM (1 April 2003). "The Internet and primary care physicians: coping with different expectations" (http://www.ajcn.org/cgi/content/full/77/4/1016S). Am. J. Clin. Nutr. 77 (4 Suppl): 1016S–1018S. PMID 12663310 (http://www.ncbi.nlm.nih.gov/pubmed/12663310). • Susannah Fox, Pew Internet & American Life Project (2004-09-27). "Today's E-Patients: Hunters and Gatherers of Health Information Online" (http://www.pewinternet.org/PPF/r/27/presentation_display.asp). • Rimer BK, Lyons EJ, Ribisl KM, et al. (July 2005). "How New Subscribers Use Cancer-Related Online Mailing Lists" (http://www.jmir.org/2005/3/e32/). J. Med. Internet Res. 7 (3): e32. doi: 10.2196/jmir.7.3.e32 (http:// dx.doi.org/10.2196/jmir.7.3.e32). PMC 1550655 (http://www.ncbi.nlm.nih.gov/pmc/articles/ PMC1550655). PMID 15998623 (http://www.ncbi.nlm.nih.gov/pubmed/15998623). • Meier A, Lyons EJ, Frydman G, Forlenza M, Rimer BK (2007). "How Cancer Survivors Provide Support on Cancer-Related Internet Mailing Lists" (http://www.jmir.org/2007/2/e12). J. Med. Internet Res. 9 (2): e12. doi: 10.2196/jmir.9.2.e12 (http://dx.doi.org/10.2196/jmir.9.2.e12). PMC 1874721 (http://www.ncbi.nlm. nih.gov/pmc/articles/PMC1874721). PMID 17513283 (http://www.ncbi.nlm.nih.gov/pubmed/17513283). • The rise of the e-patient (http://www.pewinternet.org/Presentations/2009/40-The-rise-of-the-e-patient.aspx), Lee Rainie from the Pew Internet and American Life Project (http://www.pewinternet.org/) presentation at the Medical Library Association, Oct 7, 2009 • E-patients With a Disability or Chronic Disease (http://www.pewinternet.org/PPF/r/222/report_display.asp), from the Pew Internet and American Life Project (http://www.pewinternet.org/) • Association of Cancer Online Resources (ACOR) (http://acor.org/), an aggregate of e-patient online communities for knowledge-sharing about cancer. • Time Magazine article: "When the patient is a Googler" (http://www.time.com/time/health/article/ 0,8599,1681838,00.html) • Who Cares (http://www.ftc.gov/bcp/edu/pubs/consumer/health/hea17.pdf) Booklet by the Federal Trade Commission, a guide to health information • Dave deBronkart: Meet e-Patient Dave (http://www.ted.com/talks/dave_debronkart_meet_e_patient_dave. html), video at TED • Greenwald, Ted. "A Social Network for Crohn’s Disease | MIT Technology Review" (http://www. technologyreview.com/news/518886/patients-take-control-of-their-health-care-online/). Technologyreview.com. Retrieved 2013-09-13.
451
Google Health
452
Google Health Google Health [1]
Web address
www.google.com/health
Commercial?
Yes
Type of site
Personal health record service
Registration
Required
Wikipedia:Link rot
Content license Continuity of Care Document Owner
Launched
May20,2008
Current status
Discontinued on January1,2012
Google Health was a personal health information centralization service (sometimes known as personal health record services) by Google introduced in 2008 and cancelled in 2011. The service allowed Google users to volunteer their health records – either manually or by logging into their accounts at partnered health services providers – into the Google Health system, thereby merging potentially separate health records into one centralized Google Health profile. Volunteered information could include "health conditions, medications, allergies, and lab results". Once entered, Google Health used the information to provide the user with a merged health record, information on conditions, and possible interactions between drugs, conditions, and allergies. Google Health's API was based on a subset of the Continuity of Care Record.
History Google Health was under development from mid-2006. In 2008, the service underwent a two-month pilot test with 1,600 patients of The Cleveland Clinic. Starting on May 20, 2008, Google Health was released to the general public as a service in beta test stage. On September 15, 2010 Google updated Google Health with a new look and feel. On June 24, 2011 Google announced it was retiring Google Health in January 1, 2012; data was available for download through January 1, 2013. The reason Google gave for abandoning the project was the lack of widespread adoption.
Partners Google Health, like many other Google products, was free to use for consumers. Unlike other Google services, however, Health contained no advertising. Google did not reveal how it planned to make money with the service, but a Wall Street Journal article said that Google "hasn't ruled [advertising] out for the future." Google has filed U.S. Patent Application #20070282632, "Method and apparatus for serving advertisements in an electronic medical record system". Google Health could import medical and/or drug prescription information from the following partners: Allscripts, Anvita Health, The Beth Israel Deaconess Medical Center, Blue Cross Blue Shield of Massachusetts, The Cleveland Clinic, CVS Caremark, Drugs.com, Healthgrades, Longs Drugs, Medco Health Solutions, Quest Diagnostics, RxAmerica, and Walgreens.
Google Health
453
Users whose health records reside with other providers had to either manually enter their data or pay to have a Google Health partner perform the service. MediConnect Global [2] was one such partner; for a fee, they would retrieve a user's medical records from around the world and add them to his or her profile. Since January 2010, the Withings WiFi Body scale enables Google Health users to seamlessly update their weight and other data to their online profiles[3] Recently,Wikipedia:Manual of Style/Dates and numbers#Chronological items in response to demand for added convenience, Google Health began establishing relationships with telehealth providers that will allow their users to sync the data shared during telehealth consultations with their online health records. To date, partnerships have been formed with the following companies: MDLiveCare and Hello Health.
Privacy concerns Google Health was an opt-in service, meaning it could only access medical information volunteered by individuals. It did not retrieve any part of a person's medical records without his or her explicit consent and action. However, it did encourage users to set up profiles for other individuals. According to its Terms of Service, Google Health is not considered a "covered entity" under the Health Insurance Portability and Accountability Act of 1996; thus, HIPAA privacy laws do not apply to it. In an article covering Google Health's launch, the New York Times discussed privacy issues and said that "patients apparently did not shun the Google health records because of qualms that their personal health information might not be secure if held by a large technology company." Others contend that Google Health may be more private than the current "paper" health record system because of reduced human interaction. Post-launch reactions to Google's stance that it was not a covered entity varied. Some were very negative, such as those of Nathan McFeters at ZDNet. Others, including Free/Open Source Software Healthcare activist Fred Trotter, argued that a personal health record service like Google Health would be impossible if it were HIPAA covered.
Competitors Google Health is a personal health record (PHR) service whose primary competitors in the United States are Microsoft's HealthVault, Dossia, and the open-source Indivo project. There are numerous other open-source and proprietary PHR systems, including those that compete outside the United States.[4] On July 18, 2011, Microsoft released a tool that lets Google Health customers transfer their personal health information to a Microsoft HealthVault account.[5] On December 7, 2011, MediConnect Global announced a similar capability that allows displaced Google Health users to transfer their personal health records to a MyMediConnect [6] account.[7]
Discontinuance On June 24, 2011, Google announced that Google Health would be discontinued. Google stated that they were discontinuing Google Health because it did not have as broad impact as had been expected: ... with a few years of experience, we’ve observed that Google Health is not having the broad impact that we hoped it would.
“
”
—From The Official Google Blog
[8]
Google continued to operate the Google Health site until January 1, 2013, and offered options for users to download data or transfer data to Microsoft's HealthVault.
Google Health
References [1] [2] [3] [4]
[5] [6] [7]
[8]
http:/ / www. google. com/ health http:/ / www. mediconnect. net http:/ / www. theregister. co. uk/ 2010/ 01/ 29/ wiscale/ History of the Personally Controlled Health Record (http:/ / indivohealth. org/ research): "The Indivo project has its roots in the Guardian Angel project, a collaboration between Harvard and MIT ..."; the article shows a simple timeline or pedigree of the Personally Controlled Health Record. Jay Greene, CNET. " Microsoft offers transfer tool to Google Health users (http:/ / news. cnet. com/ 8301-10805_3-20080403-75/ microsoft-offers-transfer-tool-to-google-health-users/ ?part=rss& subj=news& tag=2547-1_3-0-20)." Jul 18, 2011. Retrieved Jul 18, 2011. http:/ / www. mymediconnect. net BusinessWire. " MyMediConnect Offers Displaced Google Health Users Free, Simple Conversion Process for Transferring Personal Health Record Account (http:/ / www. businesswire. com/ news/ home/ 20111207005872/ en/ MyMediConnect-Offers-Displaced-Google-Health-Users-Free)." Dec 7, 2011. Retrieved May 9, 2012. http:/ / googleblog. blogspot. com/ 2011/ 06/ update-on-google-health-and-google. html
External links • Official website (http://www.google.com/health) • Google Health Integration (http://www.google.com/intl/en-US/health/about/integconsults.html) • "Google Health Architecture: CCR Reference" (http://healthtechsynthesis.wordpress.com/2009/05/12/ consultant_guide_to_google_health_part_i_of_iv/). Retrieved 2010-03-31.
454
IMedicor
455
IMedicor iMedicor Web address http:/ / www. imedicor. com/ Type of site
Social network service
Owner
Vemics, Inc.
Created by
Vemics, Inc.
Launched
October, 2007
The iMedicor Web portal, which went live on October 10, 2007, is online personal health data exchange and secure messaging portal for physician collaboration, community and referrals. iMedicor reached its 32,000th physician registration on December 12, 2007. It has been discussed in such journals as Healthcare Informatics, Advance for Health Information Professionals,[1] Virtual Medical Worlds, and Highway Hypodermics among others and received positive review by the Internet journal Medgadget. The portal's proprietary HIPAA-compliant technology and ability to enable health providers to exchange medical record data, documentation and images distinguish it from chat-room-style portals for the medical community. The launch of iMedicor’s portal coincides with the entrance of Microsoft's Healthvault and Google Health into the personal health record space. iMedicor has a proprietary HIPAA-compliant interface. HIPAA, which stands for the American Health Insurance Portability and Accountability Act of 1996, is a set of strict rules to be followed by doctors, hospitals and other health care providers concerning the handling and privacy protection of vital patient medical data. Violations can result in serious fines or, in extreme cases, imprisonment. On November 20, 2007, the iMedicor portal was given a positive review by Medgadget, the influential Internet Journal of Emerging Medical Technologies.[2] The journal said that iMedicor is "strikingly different" from other medical networks such as iMedExchange and Sermo. Medgadget also stated, "The service behaves more like a typical email provider and a file sharing site, lumped together with a social network for clinicians." Some of iMedicor's partner associations include the Association of Black Cardiologists (ABC), the American Society for Hypertension (ASH), and the Hypertrophic Cardiomyopathy Association (HCA).
External links • iMedicor [3] • Vemics [4]
References [1] Advance for Health Information Professionals. (http:/ / health-information. advanceweb. com/ Article/ The-PHR-Revolution. aspx) [2] Medgadget. Tuesday, November 20, 2007 - Review of iMedicor Portal for Medical Professionals (http:/ / medgadget. com/ archives/ 2007/ 11/ imedicor. html) [3] http:/ / www. imedicor. com/ [4] http:/ / www. vemics. com/
• iMedicor Announces Agreement With eRx Network - Thursday, December 06, 2007; Posted: 09:00 AM (http:// www.tradingmarkets.com/.site/news/Stock News/889796/?hcode=relatednews) • January 14, 2008, 09:06 AM Eastern Time - American Society of Hypertension Partners with iMedicor - Online Medical Portal Tapped to Help Expand Hypertension Initiative (http://www.businesswire.com/portal/site/
IMedicor
456
google/index.jsp?ndmViewId=news_view&newsId=20080114005818&newsLang=en)
Microsoft Amalga Microsoft Amalga Original author(s) Washington Hospital Center Developer(s)
Microsoft Health Solutions Group
Operating system
Windows Server
Website
Amalga's product page
[1]
on Microsoft's website
Microsoft Amalga Unified Intelligence System (formerly known as Azyxxi) is a unified health enterprise platform designed to retrieve and display patient information from many sources, including scanned documents, electrocardiograms, X-rays, MRI scans and other medical imaging procedures, lab results, dictated reports of surgery, as well as patient demographics and contact information.
History Amalga was developed initially as Azyxxi by doctors and researchers at the Washington Hospital Center emergency department in 1996. After heavy adoption, in 2006 it was acquired by the Microsoft Health Solutions Group as part of a plan to enter the fast-growing market for health care information technology. It has since been adopted at a number of leading hospitals and health systems across America including St Joseph Health System, New York Presbyterian Hospital, Georgetown University Hospital, Johns Hopkins, the Mayo Clinic and five hospitals in the MedStar Health group, a nonprofit network in the Baltimore-Washington, D.C. area. Amalga can be used to tie together many unrelated medical systems using a wide variety of data types in order to provide an immediate, updated composite portrait of the patient’s healthcare history. All of Amalga’s components are integrated using middleware software that allows the creation of standard approaches and tools to interface with the many software and hardware systems found in hospitals. A physician using Amalga can obtain within seconds a patient's past and present hospital records, medication and allergy lists, lab studies, and views of relevant X-rays, CT Scans, and other clips and images, all organized into one customized format to highlight the most critical information for that user. In clinical use since 1996, Amalga has the ability to manage more than 40 terabytes of data and provide real-time access to more than 12,000 data elements associated with a given patient. The system was first implemented by the Washington Hospital Center emergency department to reduce average waiting times. Since then it has also been used by the District of Columbia Department of Health for management of such mass-casualty incidents as a bioterrorism attack and in a variety of other settings in Arizona, Maryland, and Virginia. The Cleveland Clinic recently installed the system in a pilot project as an imaging and data integration system. Besides clinical data, Amalga is also designed to collect financial and operational data for hospital administrators. Amalga currently runs on Microsoft Windows Server operating system and uses SQL Server 2008 as the data store. At the time of acquisition, Microsoft hired Dr. Craig F. Feied, principal designer of the software, and 40 members of the development team at Washington Hospital Center. Dr. Mark Smith, who helped design the system, remained at Washington Hospital Center as director of the emergency department. Since then the Amalga team has grown to include 115 members.
Microsoft Amalga
Amalga HIS With only a handful of customers, Microsoft pulled the plug on Amalga HIS: Amalga Hospital Information System (different from the Amalga UIS identified above). On July 22, 2010 Microsoft announced that it was shutting down operations and sales for Amalga HIS. Chillmark Research reported that "Amalga HIS has only six customers today, and those customers will receive support for the next five years. After that, they will be on their own..." As for the remnants of Amalga HIS in 2011, Dale Sanders, CIO of the Cayman Island Health Authority stated that "work remains" for Amalga HIS, one drawback of which is its "split personality for data collection at the point of care — orders, problems, medications, and progress notes are orphaned." CIO Sanders found that, after working closely with Microsoft, some of the organizations he consulted "adopted, but the vast majority did not. In the end, we (Northwestern and Microsoft) couldn’t agree on a value proposition for Northwestern, which already had Cerner, Epic, and a data warehouse. There wasn’t any room or need for a product like Amalga." Brian Eastwood of Health IT Exchange concluded, "If nothing else, Amalga HIS outlasted the Kin."
References [1] http:/ / www. microsoft. com/ en-us/ microsofthealth/ products/ microsoft-amalga. aspx
External links • Microsoft Amalga Home Page (http://www.microsoft.com/amalga/products/microsoftamalga/default.mspx)
457
Microsoft HealthVault
458
Microsoft HealthVault Microsoft HealthVault Microsoft HealthVault website Developer(s)
Microsoft
Stable release 1.0 / 2010 Type
Medical database
Website
[1] http:/ / healthvault. com [2] http:/ / healthvault. co. uk
Microsoft HealthVault is a web-based platform from Microsoft to store and maintain health and fitness information.[3] Started in October 2007, the website is accessible at www.healthvault.com [1] and addresses both individuals [4] and healthcare professionals [5]. In June 2010, Microsoft HealthVault was launched in the UK, the website is accessible at www.healthvault.co.uk [2]
Components A HealthVault record [6] stores an individual's health information. Access to a record is through a HealthVault account, which may be authorized to access records for multiple individuals, so that a mother may manage records for each of her children or a son may have access to his father's record to help the father deal with medical issues. Authorization of the account can be through Windows Live ID, Facebook or a limited set of OpenID providers.
Authorization An individual interacts with their HealthVault record through the HealthVault site, or, more typically, through an application that talks to the HealthVault platform. When an individual first uses a HealthVault application, they are asked to authorize the application to access a specific set of data types, and those data types are the only ones the application can use. An individual can also share a part (some data types) or the whole of their health record with another interested individual such as a doctor, a spouse, a parent, etc.
Devices HealthVault Connection Center allows health and fitness data to be transferred from devices (such as heart rate watches, blood pressure monitors and the Withings wifi bodyscale[7]) into an individual's HealthVault record. It can also be used to find and download drivers for medical devices.
Medical Imaging HealthVault supports storage of DICOM based medical imaging. Consumers can upload and download medical imaging DVD through HealthVault connection center. Third parties can also upload and download medical imaging to/from HealthVault, for example Candelis. In addition, there has been plethora of HealthVault medical imaging viewers released by the third party to connect to HealthVault even on mobile phones.
Microsoft HealthVault
459
Interoperability HealthVault supports a number of exchange formats including industry standards such as the Continuity of Care Document and the Continuity of Care Record. Support for industry standards makes it possible to integrate with many personal health record solutions.
Competitors HealthVault's primary competitors are Dossia and World Medical Card. Google announced [8] in June 2011 that Google Health would be discontinued as of January 1, 2012, and encouraged users to either download their data or to directly transfer it to Microsoft's HealthVault service before January 1, 2013.
HealthVault Web Applications A comprehensive list of web applications is available at the HealthVault website listed below.
[8]
. Some of the notable ones are
• InstantPHR [9]. This is released by Get Real Health [10] • HealthUnity PHR Gateway [11]. This is released by HealthUnity • PassportMD [12]. This is released by PassportMD • ActivePHR [13]. This is released by ActiveHealth
References [1] http:/ / www. healthvault. com [2] http:/ / www. healthvault. co. uk [3] Microsoft Launches Health Records Site (http:/ / www. foxnews. com/ wires/ 2007Oct04/ 0,4670,MicrosoftHealthVault,00. html) [4] http:/ / www. healthvault. com/ Personal/ index. html [5] http:/ / www. healthvault. com/ industry/ index. html [6] http:/ / www. healthvault. com/ personal/ index. html [7] http:/ / cln-online. org/ index. php?option=com_content& view=article& id=666:microsoftscales& catid=49:wellness& Itemid=105 [8] http:/ / www. healthvault. com/ personal/ websites. html?type=application [9] http:/ / www. getrealhealth. com/ features-and-specifications [10] http:/ / www. getrealhealth. com [11] http:/ / www. healthvault. com/ websites/ healthunity-phrgateway. html [12] http:/ / www. healthvault. com/ websites/ PassportMD-PassportMD. html [13] http:/ / www. healthvault. com/ websites/ activehealth-activephr. html
External links • Microsoft HealthVault (http://www.healthvault.com/) • Microsoft HealthVault Developer Center (http://msdn.microsoft.com/HealthVault) • HealthVault Review: A Click away from Your Health Information (http://www.edocscan.com/ microsoft-healthvault-phr)
Patient portal
Patient portal Patient Portals are healthcare-related online applications that allow patients to interact and communicate with their healthcare providers, such as physicians and hospitals. Typically, portal services are available on the Internet at all hours of the day and night. Some patient portal applications exist as stand-alone web sites and sell their services to healthcare providers. Other portal applications are integrated into the existing web site of a healthcare provider. Still others are modules added onto an existing electronic medical record (EMR) system. What all of these services share is the ability of patients to interact with their medical information via the Internet. Currently, the lines between an EMR, a personal health record, and a patient portal are blurring. For example, Intuit Health and Microsoft HealthVault describe themselves as personal health records (PHRs), but they can interface with EMRs and communicate through the Continuity of Care Record standard, displaying patient data on the Internet so it can be viewed through a patient portal.
Features and benefits of patient portals The central feature that makes any system a patient portal is the ability to expose individual patient health information in a secure manner through the Internet. In addition, virtually all patient portals allow patients to interact in some way with health care providers. Patient portals benefit both patients and providers by increasing efficiency and productivity. Patient portals are also regarded as a key tool to help physicians meet "meaningful use" requirements in order to receive federal incentive checks, especially for providing health information to patients.[1] Some patient portal applications enable patients to register and complete forms online, which can streamline visits to clinics and hospitals. Many portal applications also enable patients to request prescription refills online, order eyeglasses and contact lenses, access medical records, pay bills, review lab results, and schedule medical appointments. Patient portals also typically allow patients to communicate directly with healthcare providers by asking questions, leaving comments, or sending e-mail messages.
Disadvantages The major shortcoming of most patient portals is their linkage to a single health organization. If a patient uses more than one organization for healthcare, the patient normally needs to log on to each organization’s portal to access information. This results in a fragmented view of individual patient data.
Practice portals Portal applications for individual practices typically exist in tandem with patient portals, allowing access to patient information and records, as well as schedules, payments, and messages from patients.[2] Most patient portals require the practice to have some type of electronic medical record or patient management system, as the patient data needs to be stored in a data repository then retrieved by the patient portal. While lauding its ease-of-use, some physicians note that it is hard to encourage patients to utilize online portals to benefit both themselves and the medical practice staff.[3]
Security Health care providers in the US are bound to comply with HIPAA regulations. These regulations specify what patient information must be held in confidence. Something as seemingly trivial as a name is viewed by HIPAA as protected health information. For this reason, security has always been a top concern for the industry when dealing with the adoption of patient portals. While there may be systems that are not HIPAA compliant, certainly most patient and practice portals are secure and compliant with HIPAA regulations. The use of SSL and access control patterns are commonplace in the industry. Patient access is typically validated with a user name and password.
460
Patient portal
History Internet portal technology has been in common use since the 1990s. The financial industry has been particularly adept at using the Internet to grant individual users access to personal information. Possibly because of the strictness of HIPAA regulations, or the lack of financial incentives for the health care providers, the adoption of patient portals has lagged behind other market segments. The American Recovery and Reinvestment Act of 2009 (ARRA), in particular the HITECH Act within ARRA, sets aside approximately $19 billion for health information technology. This funding will potentially offset the costs of electronic medical record systems for practicing physicians. Because the conversion to electronic medical records is typically complex, systems often transition to patient portals first and then follow with a complete implementation of electronic medical records. Concurrently, personal health record systems are becoming more common and available. At present, individual health data are located primarily on paper in physicians' files. Patient portals have been developed to give patients better access to their information. Given the patient mobility and the development of clear interoperable standards, the best documentation of patient medical history may involve data stored outside physician offices.
Future E-visits (remote use of medical services) may soon become one of the most commonly used options of patient portals. The most likely demographic for uptake of e-visits are patients who live in remote rural areas, far from clinical services. An Internet session would be much cheaper and more convenient than traveling a long distance, especially for simple questions or minor medical complaints. Providing a route that does not require in-person patient visits to a clinic may potentially benefit both patients and providers. Many organizations find that overall utilization drops when e-visits are implemented, in some places by as much as 25%[citation needed]. This makes e-visits a very interesting proposition for insurance companies, although few actually re-imburse for them currently. E-visits, with the proper functionality, also allow the patient to update their allergies, vital signs, and history information. Providing e-visits allows the standard healthcare organization to offer a product that can compete on price with the retail clinics that are popping up in strip malls and Wal-mart.
Vendors Some vendors, such as Epic Systems, WEBeDoctor,[4] and Cerner, offer patient portals as one module of a complete Electronic Health Record (EHR) system. Other vendors offer patient portals that can be integrated with any EHR.[citation needed]
References [1] Terry, Ken "Patient Portals: Beyond Meaningful Use" (http:/ / www. physicianspractice. com/ technology/ content/ article/ 1462168/ 1890621). Physicians Practice. June 27, 2010. [2] Practice Portals (http:/ / practiceportals. com/ ) [3] Litton, J. Scott "Encouraging Patients to Use Online Communication " (http:/ / www. physicianspractice. com/ blog/ content/ article/ 1462168/ 2026624). Physicians Practice. February 3, 2012. [4] https:/ / www. WEBeDoctor. com
461
Virtual patient
Virtual patient The term virtual patient is used to describe interactive computer simulations used in health care education.[1] The special focus is targeted on the simulation of clinical processes with virtual patients. Virtual patients combine scientific excellence, modern technologies and the innovative concept of game-based learning. Virtual patients allow the learner to take the role of a health care professional and develop clinical skills such as making diagnoses and therapeutic decisions[2] The use of virtual patient programmes is increasing in healthcare education, partly in response to increasing demands on health care professionals and education of students but also because they allow opportunity for students to practice in a safe environment There are many different formats a virtual patient may take. However the overarching principle is that of interactivity - a virtual patient will have mechanisms for the learner to interact with the case and material or information is made available to the learner as they complete a range of learning activities[3] The interactivity is non-sequential.
Forms of Virtual Patients Virtual patients may take a number of different forms: • Artificial patients: computer simulations of biochemical processes such as the effect of drugs in organisms, the physiologic processes of a given organ or entire systems (systems biology) in a given organism. These can be used in different phases of a compound or drug in development in a given pharmacological research as a preliminary to testing on animals and humans for the drug development processes. • Real patients: reflected in data e.g. electronic health records (EHRs). In this case the virtual patient is the reflection of the real patient in terms of data held about them. These are sometimes called e-patients. • Physical simulators: mannequins (sometimes spelled 'manikins'), models or related artefacts. • Simulated patients: where the patient is recreated by humans [4] or computer-generated characters [5] and Virtual Humans [6] acting as such or engaging in other kinds of role-play. • Electronic case-studies and scenarios where users work through problems, situations or similar narrative-based activities.
Types of Interaction with Simulated or Electronic Patients A number of different modes of virtual patient delivery have been defined [7]: • • • • • •
Predetermined scenario [directed mode] The learner may build up the patient or case data from observations and interactions [blank mode] The learner may view and appraise or review an existing patient or scenario [critique mode or rehearsal mode] The VP may be used as a mechanism to address particular topics [context mode] The learner may use a scenario or patient to explore personal/professional dimensions [reflective mode] Banks of patients or scenarios may collectively address broad issues of healthcare [pattern mode]
462
Virtual patient
Types of Interaction with Artificial Patients • To create and run a mathematical quantitative simulation of a healthy person (physiology) and diseased person to test multiple hypothesis against known and unknown processes in a given set or sets of processes to help fill gaps in knowledge of the physiology or system under investigation.
Possible Benefits of Physical Simulators and Simulated Patients Simulated patients increase the availability of training opportunities for medical students, making them less dependent on actual cases to learn how to handle different situations. Unlike real patients, simulated patients can be accessed on demand and they can be endlessly replayable to allow the user to explore different options and strategies. They can be structured with narratives that represent real situations while challenging the user with a wide range of tasks. They also allow simulation of rare or unusual events, and reduce risk to actual patients in the process. Despite their efficacy simulated patients are still a tangent and a prosthesis to reality. They should be viewed as augmenting existing modes and methods of clinical teaching.
Possible Benefits of Artificial Patients Artificial patients increase the possibility of exploring millions of hypothesis driven experiments on known areas of biological systems to extrapolate the unknown, which enables efficient exploration, informed research and development predictive simulation, which must also be proven by real patient studies clinical trial. If more tests can be done on Artificial patients to filter out possibly unnecessary tests or experiments, fewer subjects pharmacovigilance maybe needed. The Artificial patients insilico modeling are still in the early to middle developmental stages. It will require continual updates and development with the endless availability of new data.
Virtual Patient Data Standards The MedBiquitous consortium [8] established a working group in 2005 to create a free and open data standard for expressing and exchanging virtual patients between different authoring and delivery systems. This was in part to address the problem of exchanging and reusing virtual patients and in part to encourage and support easier and wider use of virtual patients in general. This standard has been very successful and is now widely adopted, e.g. in major projects like eViP. In 2010, this standard attained status as an ANSI standard.
Examples Electronic Cases • • • • • • • • •
[9] (SIMPLE, CLIPP, fmCASES, WISE-MD) WebSP from Karolinska Institutet [10] Virtual Patients from Harvard Medical School [11] Virtual Patient Project from New York University [12] Virtual Patients from Centre for Virtual Patients (University of Heidelberg) [13] OpenLabyrinth from Canada [14] Labyrinth from the University of Edinburgh [15] TUSK Case Simulator from Tufts University [16] Virtual Patient from Keele University School of Pharmacy [17]
• Virtual Patients Group Consortium at the University of Florida, University of Central Florida, Medical College of Georgia, and University of Georgia [18] • [19] (A whole virtual clinic with 25 different faculties and offer 250 virtual patients)
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Virtual patient Simulators • • • •
TheraSim Virtual Patient Simulation [4] Limbs and Things simulators [20] SimMan simulator [21] “Harvey” mannequin
Virtual physiology • Entelos PhysioLabs / Biologic Systems / Quantitative Mathematical Models [22]
External links • • • •
MedBiquitous Virtual Patient Working Group [23] The electronic Virtual Patient (eViP) programme [24] eLearning since 1996 [25] TheraSim Virtual Patient Simulation [4]
Notes and references [1] JiSC (2009) Repurposing existing virtual patients. Available http:/ / www. jisc. ac. uk/ whatwedo/ programmes/ elearningcapital/ reproduce/ revip. aspx accessed 08.06.09 [2] Imison M, Hughes C(2008) The virtual patient project: using low fidelity, student generated online case studies in medical education. in Hello? Where are you in the landscape of educational technology? Proceedings ascilite Melbourne 2008.http:/ / www. ascilite. org. au/ conferences/ melbourne08/ procs/ imison. pdf [3] Ellaway R (2009) Modelliing virtual patients and virtual cases. Available http:/ / meld. medbiq. org/ primers/ virtual_patients_cases_ellaway. htm [4] http:/ / www. therasim. com [5] http:/ / www. cise. ufl. edu/ research/ vegroup/ vp. html [6] http:/ / vrpsych. ict. usc. edu/ Virtual_Patient_Projects. html [7] http:/ / meld. medbiq. org/ primers/ virtual_patients_cases_ellaway. htm [8] http:/ / www. medbiq. org/ [9] http:/ / med-U. org [10] http:/ / websp. lime. ki. se/ [11] http:/ / research. bidmc. harvard. edu/ VPTutorials/ [12] http:/ / www. tinkering. net/ vp/ [13] http:/ / www. medizinische-fakultaet-hd. uni-heidelberg. de/ index. php?id=109894& L=en [14] http:/ / openlabyrinth. ca/ [15] http:/ / labyrinth. mvm. ed. ac. uk/ [16] http:/ / tusk. tufts. edu/ view/ url/ H1185C/ 471802/ 490012/ [17] http:/ / www. keele. ac. uk/ schools/ pharm/ vp/ [18] http:/ / www. virtualpatientsgroup. com/ [19] http:/ / www. inmedea-simulator. net [20] http:/ / www. limbsandthings. com/ [21] http:/ / www. laerdal. com/ document. asp?docid=1022609 [22] http:/ / www. entelos. com/ [23] http:/ / www. medbiq. org/ working_groups/ virtual_patient/ index. html [24] http:/ / www. virtualpatients. eu [25] http:/ / inmedea. com
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Clinical Research Informatics Translational research informatics Translational Research Informatics (TRI) is a sister domain to or a sub-domain of Biomedical informatics or Medical Informatics concerned with the application of informatics theory and methods to translational research. There is some overlap with the related domain of Clinical Research Informatics, but TRI is more concerned with enabling multi-disciplinary research to accelerate clinical outcomes, with clinical trials often being the natural step beyond translational research. Translational Research as defined by the National Institutes of Health includes two areas of translation. One is the process of applying discoveries generated during research in the laboratory, and in preclinical studies, to the development of trials and studies in humans. The second area of translation concerns research aimed at enhancing the adoption of best practices in the community. Cost-effectiveness of prevention and treatment strategies is also an important part of translational research.
Overview of Translational Research Informatics Translational Research Informatics can be described as “An integrated software solution to manage the: (i) logistics, (ii) data integration, and (iii) collaboration, required by translational investigators and their supporting institutions.” It is the class of informatics systems that sits between and often interoperates with: (i) Health Information Technology/Electronic Medical Record systems, (ii) CTMS/Clinical Research Informatics, and (iii) statistical analysis and data mining. Translational Research Informatics is relatively new, with most CTSA awardee academic medical centers actively acquiring and integrating systems to enable the end-to-end TRI requirements. One advanced TRI system is being implemented at the Windber Research Institute in collaboration with GenoLogics and InforSense. Translational Research Informatics systems are expected to rapidly develop and evolve over the next couple of years.
Systems in Translational Research Informatics System Type
Description of System
Translational Study Management
Systems to manage investigator lead biomarker validation studies / outcomes / observational studies.
Electronic Patient Questionnaires
Web based forms for capturing participant demographic, condition, treatment, and outcomes information.
Clinical Information Management
Systems to integrate clinical annotations extracted from various sources systems, like HL7 Electronic Medical Records, Cancer Registries, Clinical Data Management Systems, and Clinical Data Warehouses.
Biorepository Management Systems
Manage biospecimens derrived from study participants, operating rooms, etc.
Laboratory Information Management Systems
Systems to manage clinical, analytical, and life sciences core technology laboratories - often conducting genomics, proteomics, metabolomics, molecular imaging, peptide synthesis, flow cytometry, etc.
Systems Biology / Science Data Management
A data base and content management system to archive raw instrument files and database science results data.
Research Collaboration System
A software solution to enable investigators and their research teams to share project information, results data, and insights.
Translational research informatics
CTRI Dedicated WIKI Further discussion of this domain can be found at the Clinical Research Informatics Wiki (CRI Wiki), a wiki dedicated to issues in Clinical and Translational Research Informatics.
Clinical data management system A clinical data management system or CDMS is a tool used in clinical research to manage the data of a clinical trial. The clinical trial data gathered at the investigator site in the case report form are stored in the CDMS. To reduce the possibility of errors due to human entry, the systems employ various means to verify the data. Clinical data management can be a self-contained system or part of the functionality of a CTMS. A CTMS with clinical data management functionality can help with the validation of clinical data as well as the help the site employ the data for other important activities like building patient registries and assist in patient recruitment efforts.
Classification The CDMS can be broadly divided into paper-based and electronic data capturing systems.
Paper-based systems Case report forms are manually filled at site and mailed to the company for which trial is being performed. The data on forms is transferred to the CDMS tool through data entry.The most popular method being double data entry where two different data entry operators enter the data in the system independently and both the entries are compared by the system. In case the entry of a value conflicts, system alerts and a verification can be done manually. Another method is Single Data Entry. The data in CDMS are then transferred for the data validation. Also, in these systems during validation the data clarification from sites are done through paper forms, which are printed with the problem description and sent to the investigator site and the site responds by answering on forms and mailing them back.
Electronic data capturing systems In such CDMS the investigators directly uploads the data on CDMS and the data can then be viewed by the data validation staff. Once the data are uploaded by site, data validation team can send the electronic alerts to sites if there are any problems. Such systems eliminate paper usage in clinical trial validation of data. Case report forms are manually filled at site and mailed to the company for which trial is being performed. The data on forms is transferred to the CDMS tool through data entry.The most popular method being double data entry where two different data entry operators enter the data in the system independently and both the entries are compared by the system. In case the entry of a value conflicts, system alerts and a verification can be done manually. Another method is Single Data Entry. The data in CDMS are then transferred for the data validation. Also, in these systems during validation the data clarification from sites are done through paper forms, which are printed with the problem description and sent to the investigator site and the site responds by answering on forms and mailing them back.
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Clinical data management system
Clinical data management Once data have been screened for typographical errors, the data can be validated to check for logical errors. An example is a check of the subject's date of birth to ensure that they are within the inclusion criteria for the study. These errors are raised for review to determine if there are errors in the data or if clarifications from the investigator are required. Another function that the CDMS can perform is the coding of data. Currently, the coding is generally centered around two areas— adverse event terms and medication names. With the variance on the number of references that can be made for adverse event terms or medication names, standard dictionaries of these terms can be loaded into the CDMS. The data items containing the adverse event terms or medication names can be linked to one of these dictionaries. The system can check the data in the CDMS and compare them to the dictionaries. Items that do not match can be flagged for further checking. Some systems allow for the storage of synonyms to allow the system to match common abbreviations and map them to the correct term. As an example, ASA (acetylsalicylic acid) could be mapped to aspirin, a common notation. Popular adverse event dictionaries are MedDRA and WHOART and popular Medication dictionaries are COSTART and WHO Drug Dictionary. At the end of the clinical trial the data set in the CDMS is extracted and provided to statisticians for further analysis. The analysed data are compiled into clinical study report and sent to the regulatory authorities for approval. Most of the drug manufacturing companies are using Web-based systems for capturing, managing and reporting clinical data. This not only helps them in faster and more efficient data capture, but also speeds up the process of drug development. Perceptive Informatics, Medidata RAVE and Forte Research Systems' OnCore eClinical are examples of Web-based data capture systems. In such systems, studies can be set up for each drug trial. In-built edit checks help in removing erroneous data. The system can also be connected to other external systems. For example, RAVE can be connected to an IVRS (Interactive Voice Response System) facility to capture data through direct telephonic interviews of patients.
References • Stuart Summerhayes, CDM Regulations Procedures Manual, Blackwell Publishing, ISBN 1-4051-0740-5 • Tai BC, Seldrup J., A review of software for data management, design and analysis of clinical trials, Ann Acad Med Singapore. 2000 Sep;29(5):576-81. • Greenes RA, Pappalardo AN, Marble CW, Barnett GO., Design and implementation of a clinical data management system, Comput Biomed Res. 1969 Oct;2(5):469-85.
External links • • • • •
Clinical Trial Management Systems (https://cabig.nci.nih.gov/workspaces/CTMS) (caBIG) Association for Clinical Data Management (http://www.acdm.org.uk/) (ACDM) Society for Clinical Data Management (http://www.scdm.org/) (SCDM) French network of Data Managers in Academic biomedical research (http://www.acadm.fr/en/) (AcaDM) Data Quality Research Institute (http://www.dqri.org/) (DQRI)
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Case report form
Case report form A case report form (or CRF) is a paper or electronic questionnaire specifically used in clinical trial research. The Case Report Form is the tool used by the sponsor of the clinical trial to collect data from each participating site. All data on each patient participating in a clinical trial are held and/or documented in the CRF, including adverse events. The sponsor of the clinical trial develops the CRF to collect the specific data they need in order to test their hypotheses or answer their research questions. The size of a CRF can range from a handwritten one-time 'snapshot' of a patient's physical condition to hundreds of pages of electronically captured data obtained over a period of weeks or months. (It can also include required check-up visits months after the patient's treatment has stopped.) The sponsor is responsible for designing a CRF that accurately represents the protocol of the clinical trial, as well as managing its production, monitoring the data collection and auditing the content of the filled-in CRFs. Case report forms contain data obtained during the patient's participation in the clinical trial. Before being sent to the sponsor, this data is usually de-identified (not traceable to the patient) by removing the patient's name, medical record number, etc., and giving the patient a unique study number. The supervising Institutional Review Board (IRB) oversees the release of any personally identifiable data to the sponsor. From the sponsor's point of view, the main logistic goal of a clinical trial is to obtain accurate CRFs. However, because of human and machine error, the data entered in CRFs is rarely completely accurate or entirely readable. To combat these errors monitors are usually hired by the sponsor to audit the CRF to make sure the CRF contains the correct data. When the study administrators or automated mechanisms process the CRFs that were sent to the sponsor by local researchers, they make a note of queries. Queries are non-sensible or questionable data that must be explained. Examples of data that would lead to a query: a male patient being on female birth control medication or having had an abortion, or a 15-year old participant having had hip replacement surgery. Each query has to be resolved by the individual attention of a member of each local research team, as well as an individual in the study administration. To ensure quality control, these queries are usually addressed and resolved before the CRF data is included by the sponsor in the final clinical study report. Depending on variables relating to the nature of the study, (e.g., the health of the study population), the effectiveness of the study administrators in resolving these queries can significantly impact the cost of studies.
eCRF eCRF is an electronic case report form.
References • Debbie Kennedy, CRF Designer, Canary Publications, ISBN 0-9531174-7-2
External links • • • •
International Clinical Sciences Support Center (ICSSC) CRF Development [1] Standardized Case Report Form (CRF) Work Group [2] - National Cancer Institute Standard Operating Procedure – Develop and Manage a Case Report Form [3] links to CRF information (PDF) Clinical Trials Software - Case Report Forms [4]
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References [1] http:/ / www. icssc. org/ services/ services-crfdevelopment. html [2] https:/ / cabig. nci. nih. gov/ workspaces/ CTMS/ CTWG_Implementation/ crf-standardization-sig [3] https:/ / cabig. nci. nih. gov/ workspaces/ CTMS/ Meetings/ SIGs/ Best_Practices/ SOPs/ CR003_SOP_Develop_Manage_CRF. pdf#search=%22%22Case%20Report%20Form%22%20standardization%22 [4] http:/ / www. entrypointplus. com/ casereportforms. htm
Clinical coder A clinical coder – also known as diagnostic coder, medical coder or medical records technician – is a health care professional whose main duties are to analyse clinical statements and assign standard codes using a classification system. The data produced are an integral part of health information management, and are used by local and national governments, private healthcare organizations and international agencies for various purposes, including medical and health services research, epidemiological studies, health resource allocation, case mix management, public health programming, medical billing, and public education. For example, a clinical coder may use a set of published codes on medical diagnoses and procedures, such as the International Classification of Diseases or the Common Coding System for Healthcare Procedures, for reporting to the health insurance provider of the recipient of the care. The use of standard codes allows insurance providers to map equivalencies across different service providers who may use different terminologies or abbreviations in their written claims forms, and be used to justify reimbursement of fees and expenses. The codes may cover topics related to diagnoses, procedures, pharmaceuticals or topography. The medical notes may also be divided into specialities for example cardiology, gastroenterology, nephrology, neurology or orthopedic care. A clinical coder therefore requires a good knowledge of medical terminology, clinical documentation, legal aspects of health information, health data standards, classification conventions, and computer- or paper-based data management, usually as obtained through formal education and/or on-the-job training.[1][2]
Clinical coders in practice The basic task of a clinical coder is to classify medical and health care concepts using a standardised classification. Most clinical coders are employed in coding inpatient episodes of care.[citation needed] However, mortality events, outpatient episodes, general practitioner visits and population health studies can all be coded. Clinical coding has three key phases: a) Abstraction; b) Assignment; and c) Review.
Abstraction The abstraction phase involves reading the entire record of the health encounter and analysing the information to determine what condition(s) the patient had, what caused it and how it was treated. The information comes from a variety of sources within the medical record, such as clinical notes, laboratory and radiology results, and operation notes.
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Assignment The assignment phase has two parts: finding the appropriate code(s) from the classification for the abstraction; and entering the code into the system being used to collect the coded data.
Review Reviewing the code set produced from the assignment phase is very important. Clinical coder must ask themselves, "does this code set fairly represent what happened to this patient in this health encounter at this facility." By doing this, clinical coders are checking that they have covered everything that they must, but not used extraneous codes. For health encounters that are funded through a case mix mechanism, the clinical coder will also review the diagnosis-related group to ensure that it does fairly represent the health encounter.
Competency levels Clinical codes may have different competency levels depending on the specific tasks and employment setting: • Entry-level coder: someone who has completed (or nearly completed) an introductory training course in clinical classification, and whose work is typically checked by an experienced coder before being used. • Intermediate level coder: has acquired the skills necessary to code many cases independently. Coders at this level are also able to code cases with incomplete information. They have a good understanding of anatomy and physiology along with disease processes. Intermediate level coders have their work audited periodically by an Advanced coder. • Advanced level coder: authorized to code all cases including the most complex. Advanced coders will usually be credentialled and will have several years experience. An advanced coder is also able to train entry-level coders. • Nosologist: understands how the classification is underpinned. Nosologists consult nationally and internationally to resolve issues in the classification and are viewed as experts who can not only code, but design and deliver education, assist in the development of the classification and the rules for using it. Nosologists are usually expert in more than one classification, including morbidity, mortality and casemix. In some countries the term "nosologist" is used as a catch-all term for all levels.[3] In some countries, clinical coders may seek voluntary accreditation through assessments conducted by professional associations or health authorities.
Classification types Clinical coders may use many different classifications, which fall into two main groupings: statistical classifications and nomenclatures. • A statistical classification brings together similar clinical concepts and groups them into one category. This allows the number of categories to be limited so that the classification does not become too big. An example of this is in ICD-10 at code I47.1. The code title (rubric) is Supraventricular tachycardia. However, there are several other clinical concepts that are also classified here. Amongst them are paroxysmal atrial tachycardia, paroxysmal junctional tachycardia, auricular tachycardia and nodal tachycardia. • In a nomenclature there is a separate listing and code for every clinical concept. So, in the example in the previous paragraph, each of the tachycardia listed would have its own code. This makes nomenclatures unwieldy for compiling health statistics.
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Professional associations There are several associations that medical coders in the United States may join, including the American Health Information Management Association (AHIMA)[4] and the American Academy of Professional Coders (AAPC).[5] Other national associations include the Health Information Management Association of Australia (HIMAA) and the Canadian Health Information Management Association (CHIMA).
References [1] World Health Organization. Classifying health workers: Medical records and health information technicians. Geneva, 2010. [2] Department of Human Services, Victoria, Australia. Clinical Coders Creed. (http:/ / www. dhs. vic. gov. au/ ahs/ archive/ hdss/ 13-19720. htm#clinical) Health Data Standards and Systems Bulletin, Issue 13, 19 July 2000. [3] Nosologist. (http:/ / www. popsci. com/ scitech/ article/ 2004-11/ nosologist) Popular Science, posted 11.11.2004. [4] AHIMA - Certification and Credentials (http:/ / www. ahima. org/ certification/ credentials. aspx) [5] American Academy of Professional Coders (http:/ / www. aapc. com/ )
External links • WHO Family of International Classifications (http://www.who.int/classifications/en/) • American Health Information Management Association (http://www.ahima.org) • Canadian Health Information Management Association (http://www.eCHIMA.ca) • National Library of Medicine (http://www.nlm.nih.gov/research) (U.S.)
Clinical data acquisition Acquisition or collection of clinical trial data can be achieved through various methods that may include, but are not limited to, any of the following: paper or electronic medical records, paper forms completed at a site, interactive voice response systems, local electronic data capture systems, or central web based systems. There is arguably no more important document than the instrument that is used to acquire the data from the clinical trial with the exception of the protocol, which specifies the conduct of that clinical trial. The quality of the data collected relies first and foremost on the quality of that instrument. No matter how much time and effort go into conducting the clinical trial, if the correct data points were not collected, a meaningful analysis may not be possible. It follows, therefore, that the design, development and quality assurance of such an instrument must be given the utmost attention. The ICH guidelines on Good clinical practice (GCP) use the term ‘Case report form’ or ‘CRF’ to refer to these systems 1 . No matter what CRF is utilized, the quality and integrity of the data is of primary importance. The following recommendations are meant to assist in the design, development and quality assurance of the CRF such that the data collected will meet the highest standards. For an extensive discussion regarding creation of CRFs and examples of actual data collection forms, see Data Collection Forms for Clinical Trials by Spilker 2 . The following is meant to highlight some of the most important points to consider during the design process.
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Minimum standards • Design the CRF to collect the data specified by the protocol. • Document the process for CRF design, development, approval and version control. • Make the CRF available at the clinical site prior to enrollment of a subject. • Document training of clinical site personnel on the protocol, CRF completion instructions and data submittal procedures prior to enrollment of a subject.
Best practices • Design the CRF along with protocol to assure collection of only the data that protocol specifies. • Keep questions, prompts and instructions clear and concise. • Design the CRF to follow the data flow from the perspective of the person completing it, taking into account the flow of study procedures and typical organization of data in a medical record. • Avoid referential and redundant data points within the CRF whenever possible. If redundant data collection is used to assess data validity, the measurements should be obtained through independent means. • Design the CRF with the primary safety and efficacy endpoints in mind as the main goal of data collection. • Establish and maintain a library of standard forms. • Make the CRF available for review at the clinical site prior to approval. • Use NCR (no carbon required) paper or other means to assure exact replicas of paper collection tools.
References • Debbie Kennedy, CRF Designer, Canary Publications, ISBN 0-9531174-7-2 • Rebecca Daniels Kush (2003), eClinical Trials: Planning and Implementation, CenterWatch / Thomson Healthcare, ISBN 1-930624-28-X • Spilker B.L. Schoenfelder J. (1991), Data Collection Forms in Clinical Trials Raven Press, New York.
External links • FDA Website: Clinical Data Management Regulations [1] • Association For Clinical Data Management [2] • Society For Clinical Data Management [3]
References [1] http:/ / www. fda. gov/ [2] http:/ / www. acdm. org. uk/ [3] http:/ / www. scdm. org/
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Data clarification form
Data clarification form A Data Clarification Formor Data Query Form is a questionnaire specifically used in clinical research. The DCF is the primary data clarification tool from the trial sponsor or Contract Research Organization (CRO) towards the investigator to clarify discrepancies and ask the investigator for clarification. The DCF is part of the data validation process in a clinical trial.
References • Celine Clive (2004), Handbook of SOPs for Good Clinical Practice, CRC, ISBN 0-8493-2181-6
External links • DCF entry in Clinical Research Dictionary [1]
References [1] http:/ / www. med. umich. edu/ cacr/ dictionary/ D-F. htm
Patient-reported outcome A patient-reported outcome or PRO is a questionnaire used in a clinical trial or a clinical setting, where the responses are collected directly from the patient.
Terminology The term PRO should not be confused with patient-based outcomes. The latter implies that questionnaire covers issues of specific concern to the patient. However, patient-reported implies only that the patient provides the information. This information may, or may not, be of concern to the patient. The term PROs is synonymous with the increasing use of the term patient reported outcome measures (PROMs).
Overview PRO is an umbrella term that covers a whole range of potential types of measurement but is used specifically to refer to self-reports by the patient. PRO data may be collected via self-administered questionnaires completed by the patient themselves or via interviews. The latter will only qualify as a PRO where the interviewer is gaining the patient's views, not where the interviewer uses patient responses to make a professional assessment or judgment of the impact of the patient's condition. Thus, PROs are a means of gathering patient rather than clinical or other views on outcomes. This patients' perspective can play an important role in drug approval.
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Characteristics A well-designed PRO questionnaire should assess either a single underlying characteristic or, where it addresses multiple characteristics, should be a number of scales that each address a single characteristic. These measurement "characteristics" are termed constructs and the questionnaires used to collect them, termed instruments, measures, scales or tools. A questionnaire that measures a single construct is described as unidimensional. Items (questions) in a unidimensional questionnaire can be added to provide a single scale score. However, it cannot be assumed that a questionnaire is unidimensional simply because the author intended it to be. This must be demonstrated empirically (for example, by confirmatory factor analysis or Rasch analysis). A questionnaire that measures multiple constructs is termed multi-dimensional. A multi-dimensional questionnaire is used to provide a profile of scores; that is, each scale is scored and reported separately. It is possible to create an overall (single summary) score from a multi-dimensional measure using factor analysis or preference-based methods but some may see this as akin to adding apples and oranges together. Questionnaires may be generic (designed to be used in any disease population and cover a broad aspect of the construct measured) or condition-targeted (developed specifically to measure those aspects of outcome that are of importance for a people with a particular medical condition). The most commonly used PRO questionnaires assess one of the following constructs: • • • • • • •
Symptoms (impairments) and other aspects of well-being Functioning (disability) Health status General health perceptions Quality of life (QoL) Health related quality of life (HRQoL) Reports and Ratings of health care.
Measures of symptoms may focus on a range of impairments or on a specific impairment such as depression or pain. Measures of functioning assess activities such as personal care, activities of daily living and locomotor activities. Health-related quality of life instruments are generally multi-dimensional questionnaires assessing a combination of aspects of impairments and/or disability and reflect a patient's health status. In contrast, QoL goes beyond impairment and disability by asking about the patient's ability to fulfill their needs and also about their emotional response to their restrictions. A new generation of short and easy-to-use tools to monitor patient outcomes on a regular basis has been recently proposed. These tools are quick, effective, and easy to understand, as they allow patients to evaluate their health status and experience in a semi-structured way and accordingly aggregate input data, while automatically tracking their physio-emotional sensitivity. As part of the National Institute of Health's Roadmap Initiative, the Patient-Reported Outcomes Measurement Information System (PROMIS) uses modern advances in psychometrics such as Item Response Theory (IRT) and Computerized Adaptive Testing (CAT) to create highly reliable and validated measurement tools.
Validation and quality assessment It is essential that a PRO instrument satisfy certain development, psychometric and scaling standards if it is to provide useful information. Specifically, measures should have a sound theoretical basis and should be relevant to the patient group with which they are to be used. They should also be reliable and valid (including responsive to underlying change) and the structure of the scale (whether it possesses a single or multiple domains) should have been thoroughly tested using appropriate methodology in order to justify the use of scale or summary scores.
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Patient-reported outcome These standards must be maintained throughout every target language population. In order to ensure that developmental standards are cosnistent in translated versions of a PRO instrument, the translated instrument undergoes a process known as Linguistic validation in which the preliminary translation is adapted to reflect cultural and linguistic differences between diverse target populations.
Examples Many of the common generic PRO tools assess health-related quality of life or patient evaluations of health care. For example, the SF-36 Health Survey (SF-36 Health Survey), SF-12 Health Survey (SF-12 Health Survey), the Sickness Impact Profile, the Nottingham Health Profile, the Health Utilities Index, the Quality of Well-Being Scale, the EuroQol (EQ-5D), and the Consumer Assessment of Healthcare Providers and Systems (CAHPS) survey instruments are PRO instruments. Condition-targeted tools may capture any of the constructs listed above, depending on the purpose for which they were designed. Examples include the Adult Asthma Quality of Life Questionnaire (AQLQ), the Kidney Disease Quality of Life Instrument, National Eye Institute Visual Functioning Questionnaire, Epilepsy Surgery Inventory, Migraine Specific Quality of Life (MSQOL), the Ankylosing Spondylititis Quality of Life questionnaire (ASQoL) and the Seattle Angina Questionnaire (SAQ), to name a few.
PROMs in the NHS Since 1 April 2009 all providers of care funded by the National Health Service (NHS) in England have been required to provide Patient-Reported Outcome Measures (PROMs) in four elective surgical procedures: hip replacement, knee replacement, varicose vein surgery and hernia surgery.,. Patients are asked to complete a questionnaire before undergoing the surgical procedure; a follow-up questionnaire is then sent to the patient some weeks or months later. Patient participation is, however, not compulsory.
References • Bradley C. Importance of differentiating health status from quality of life. Lancet 2001; 357 (9249):7-8. • Fung CH, Hays RD. Prospects and challenges in using patient-reported outcomes in clinical practice. Quality of Life Research 2008; 17: 1297-302. • Doward LC, McKenna SP, Defining Patient-Reported Outcomes. Value in Health 2004; 7(S1): S4-S8. • Fayers P, Hays RD. (eds.) Assessing Quality of Life in Clinical Trials: Methods and Practice. Oxford: Oxford University Press, 2005. • Gallagher P, Ding L, Ham HP, Schor EL, Hays RD, Cleary PD. Development of a new patient-based measure of pediatric ambulatory care. Pediatrics 2009; 124: 1348-1354. • Hays RD, Reeve BB. Measurement and modeling of health-related quality of life. In J. Killewo, H. K. Heggenhougen & S. R. Quah (eds.), Epidemiology and Demography in Public Health (pp.195–205). Elsevier, 2010. • Kennedy D, CRF Designer, Canary Publications, ISBN 0-9531174-7-2 • McKenna SP, Doward LC, Integrating Patient-Reported Outcomes. Value in Health 2004; 7(S1): S9-S12. • Patrick DL, Bergner M. Measurement of Health Status in the 1990s. Annu Rev Public Health. 1990; 11: 165-83. • Tennant A, McKenna SP. Conceptualising and defining outcome. Br J Rheumatol 1995;34:899-900. • Valderas JM, Alonso J. Patient reported outcome measures: a model-based classification system for research and clinical practice. Qual Life Res. 2008; 17: 1125-35. • Wiklund I., Assessment of patient-reported outcomes in clinical trials: the example of health-related quality of life, Fundam Clin Pharmacol. 2004 Jun;18(3):351-63. • Willke RJ, Burke LB, Erickson P., Measuring treatment impact: a review of patient-reported outcomes and other efficacy endpoints in approved product labels, Control Clin Trials. 2004 Dec;25(6):535-52.
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Patient-reported outcome
External links • • • • • • •
EuroQol Group (EQ-5D) (http://www.euroqol.org/) Patient Reported Outcomes Measurement Information System (http://www.nihpromis.org/) Medical Outcomes Trust (http://www.outcomes-trust.org/) Health Surveys (http://www.rand.org/health/surveys_tools.html/RAND) SF-36.org (http://www.sf-36.org/) Mapi (http://www.mapigroup.com/) Mapi Research Trust (non-profit organization involved in Patient-Reported Outcomes (PRO) instruments and Epidemiology (http://www.mapi-trust.org/) • ProQolid (Patient-Reported Outcome & Quality of Life Instruments Database) (http://www.proqolid.org/) • PROLabels(Database on Patient-Reported Outcome claims in marketing authorizations) (http://www. mapi-prolabels.org/) • Vector Psychometric Group, LLC: PRO consulting, development, and delivery systems (http://www. VPGcentral.com/)
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Standards, Coding and Nomenclature Diagnosis codes In healthcare, Diagnosis codes are used as a tool to group and identify diseases, disorders, symptoms, poisonings, adverse effects of drugs & chemicals, injuries and other reasons for patient encounters. Diagnostic coding is the translation of written descriptions of diseases, illnesses and injuries into codes from a particular classification. In medical classification, diagnosis codes are used as part of the clinical coding process alongside intervention codes. Both diagnosis and intervention codes are assigned by a health professional trained in medical classification such as a clinical coder or Health Information Manager. Several diagnosis classification systems have been implemented to various degrees of success across the world. The various classifications have a focus towards a particular patient encounter type such as emergency, inpatient, outpatient, mental health as well as surgical care. The International Statistical Classification of Diseases and Related Health Problems (ICD) is one of the most widely used classification systems for diagnosis coding as it allows comparability and use of mortality and morbidity data. As the knowledge of health and medical advances arise, the diagnostic codes are generally revised and updated to match the most up to date current body of knowledge in the field of health. The codes may be quite frequently revised as new knowledge is attained. DSM (see below) changes some of its coding to correspond to the codes in ICD. In 2005, for example, DSM changed the diagnostic codes for circadian rhythm sleep disorders from the 307-group to the 327-group; the new codes reflect the moving of these disorders from the Mental Disorders section to the Neurological section in the ICD
Diagnostic Coding Systems A number of diagnostic coding systems are current implemented across the world to code the stay of patients within a typical health setting such as a hospital. The following table provides a basic list of the currently used coding systems: Classification System
Detail
ICD-9-CM
Volumes 1 and 2 only. Volume 3 contains Procedure codes
ICD-10
The Current International Standard
ICPC-2
Also includes reasons for encounter (RFE), procedure codes and process of care
International Classification of Sleep Disorders NANDA Diagnostic and Statistical Manual of Mental Disorders Primarily psychiatric disorders Mendelian Inheritance in Man
Genetic diseases
Read code
Used throughout United Kingdom General Practice computerised records
SNOMED
D Axis
Diagnosis codes
Financial aspects of Diagnostic Coding Diagnosis codes are generally used as a representation of admitted episodes in health care settings. The principal diagnosis, additional diagnoses alongside intervention codes essentially depict a patient's admission to a hospital. Diagnoses codes are subjected to ethical considerations as they contribute to the total coded medical record in health services areas such as a hospital. Hospitals that are based on Activity Based Funding and Diagnoses Related Group Classification systems are often subjected to high end decision making that could affect the outcome of funding. It’s important to look at the scope of diagnoses codes in terms of their application in finance. The diagnoses codes in particular the Principal Diagnoses and Additional Diagnoses can significantly affect the total funding that a hospital may receive for any patient admitted. Ethically this highlights the fact that the assignment of the diagnoses code can be influenced by a decision to maximize reimbursement of funding. For example when looking at the activity based funding model used in the public hospital system in Victoria the total coded medical record is responsible for its reflected funding. These decisions also affect clinical documentation by physicians as recommendations from a Health Information Service can directly affect how a clinician may document a condition that a patient may have. The difference between the codes assigned for confusion and delirium can alter a hospitals DRG assignment as delirium is considered a higher level code than confusion within the ICD-10 coding hierarchy in terms of severity. A clinical coder or Health Information Manager may feel obliged to maximize funding above the ethical requirement to be honest within their diagnostic coding; this highlights the ethical standpoint of diagnoses codes as they should be reflective of a patient’s admission.
Factors affecting accuracy in Diagnostic Coding Accuracy is a major component in diagnoses codes. The accurate assignment of diagnoses codes in clinical coding is essential in order to effectively depict a patients stay within a typical health service area. A number of factors can contribute to the overall accuracy coding which includes medical record legibility, physician documentation, clinical coder experience, financial decision making, miscoding as well as classification system limitations. Medical Record Legibility The legibility of a medical record is a contributing factor in the accuracy of diagnostic coding. The assigned proxy that is extracting information from the medical record is dependent on the quality of the medical record. Factors that contribute to a medical records quality are physician documentation, handwriting legibility, compilation of forms, duplication and inaccurate patient data. For example if a clinical coder or Health Information Manager was extracting data from a medical record in which the principal diagnoses was unclear due to illegible handwriting, the health professional would have to contact the physician responsible for documenting the diagnoses in order to correctly assign the code. In Australia, the legibility of records has been sufficiently maintained due to the implementation of highly detailed standards and guidelines which aim to improve the legibility of medical records. In particular the paper medical record standard 'AS 2828' created by Standards Australia focuses on a few key areas which are critical to maintaining a legible paper medical record. The following criteria should be used as a guideline when creating a medical record specific to the aid of providing clear documentation for diagnostic coding. In particular the legibility of a medical record is dependent on; 1. Durability: If a medical record wasn't durable, overtime if a coder was to revisit the record and it wasn't legible it wouldn't be feasible to code from that record. 2. Ready Identification: A coder must be able to identify the exact record being coded in order to effectively extract diagnoses codes. 3. Reproducible: A coder would need to make sure that the record is reproducible in that copies can be made to aid in effective coding. Clinical Coder Experience
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Diagnosis codes The experience of the health professional coding a medical record is an essential variable that must be accounted for when analysing the accuracy of coding. Generally a coder with years of experience is able to extract all the relevant information from a medical record whether it is paper, scanned or semi-electronic. The diagnoses codes selected from the extraction are generally compiled and sequenced in order to represent the admission. An experienced coder may incorrectly assign codes due a lack of application of a classification systems relevant standards. An example to highlight clinical coding experience would be the standard within the Australian Coding Standards 0010 General Abstraction Guidelines. These guidelines indicate that a coder must seek further detail within a record in order to correctly assign the correct diagnoses code. An inexperienced coder may simply just use the description from the discharge summary such as Infarction and may not use the correct detail which could be further found within the details of the medical record. This directly relates to the accuracy of diagnoses codes as the experience of the health professional coder is significant in its accuracy and contribution to finance.
Weaknesses in Diagnostic Coding Generally coding is a concept of modeling reality with reduced effort but with physical copying. • Hence the result of coding is a reduction to the scope of representation as far as possible to be depicted with the chosen modeling technology. There will be never an escape, but choosing more than one model to serve more than one purpose. That led to various code derivatives, all of them using one basic reference code for ordering as e.g. with ICD-10 coding. However, concurrent depiction of several models in one image remains principally impossible. • Focusing a code on one purpose lets other purposes unsatisfied. This has to be taken into account when advertising for any coding concept. The operability of coding is generally bound to purpose. Inter-referring must be subject of evolutionary development, as code structures are subject of frequent change.[1] • Unambiguous coding requires strict restriction to hierarchical tree structures possibly enhanced with multiple links, but no parallel branching for contemporary coding whilst maintaining bijectivity. • Spatial depictions of n-dimensional code spaces as coding scheme trees on flat screens may enhance imagination, but still leave the dimensionality of image limited to intelligibility of sketching, mostly as a 3D object on a 2D screen. Pivoting such image does not solve the intelligibility problem. • Projections of code spaces as flattened graphs may ease the depiction of a code, but generally reduce the contained information with the flattening. There is no explanation given with many of the codes for transforming from one code system to another. That leads to specialized usage and to limitations in communication between codes. The escape is with code reference structures (as e.g. not existing with SNOMED3). • Hierarchical ordering of more than one code system may be seen as appropriate, as the human body is principally invariant to coding. But the dependency implied with such hierarchies decrease the cross referencing between the code levels down to unintelligibility. The escape is with hyper maps that exceed planar views (as e.g. with SNOMED3) and their referring to other codes (as e.g. yet not existing with SNOMED3). • Purpose of documenting will be seen as essential just for the validation of a code system in aspects of correctness. However this purpose is timely subordinate to the generating of the respective information. Hence some code system shall support the process of medical diagnosis and of medical treatment of any kind. Escape is with a specialised coding for the processes of working on diagnosis as on working with treatment (as e.g. not intended with SNOMED3). • Intelligibility of results of coding is achieved by semantic design principles and with ontologies to support navigating in the codes. One major aspect despite the fuzziness of language is the bijectivity of coding. Escape is with explaining the code structure to avoid misinterpreting and various codes for the very same condition (as e.g. yet not served at all with SNOMED3).
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Diagnosis codes
References [1] Towards Semantic Interoperability in Healthcare (http:/ / crpit. com/ confpapers/ CRPITV72Ryan. pdf)
Procedure codes Procedure codes are numbers or alphanumeric codes used to identify specific health interventions taken by medical professionals.
Examples of procedure codes International • International Classification of Procedures in Medicine (ICPM) and International Classification of Health Interventions (ICHI) • ICPC-2 (International Classification of Primary Care, which contains diagnosis codes, reasons for encounter (RFE), and process of care as well as procedure codes)
North American • Healthcare Common Procedure Coding System (including Current Procedural Terminology) (used in United States) • ICD-10 Procedure Coding System (ICD-10-PCS) (used in United States) • ICD-9-CM Volume 3 (subset of ICD-9-CM) (used in United States) • Canadian Classification of Health Interventions (CCI) (used in Canada. Replaced CCP.) [1]Wikipedia:Link rot • Nursing Interventions Classification (NIC) (used in United States) [2]Wikipedia:Link rot • Nursing Minimum Data Set (NMDS) • Nursing Outcomes Classification (NOC) • SNOMED (P axis) • Current Dental Terminology (CDT)
European • • • • • • • •
OPS-301 (adaptation of ICPM used in Germany) OPCS-4.6 (used by the NHS in England) Classification des Actes Médicaux (CCAM) (used in France)[3]Wikipedia:Link rot NOMESCO Gebührenordnung für ärzte (GOÄ) (Germany) Nomenclature des prestations de santé de l'institut national d'assurance maladie invalidité (Belgium) TARMED (Switzerland) Classificatie van virrichtingen (Dutch)
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Procedure codes
Other • Australian Classification of Health Interventions (ACHI)[4]Wikipedia:Link rot • Read codes system, used in United Kingdom General Practice
References [1] [2] [3] [4]
secure.cihi.ca (http:/ / secure. cihi. ca/ cihiweb/ dispPage. jsp?cw_page=codingclass_cci_e) duke.edu (http:/ / www. duke. edu/ ~goodw010/ vocab/ NIC. html) ccam.sante.fr (http:/ / www. ccam. sante. fr/ ) fhs.usyd.edu.au (http:/ / www3. fhs. usyd. edu. au/ ncchwww/ site/ 4. 1. 3. htm)
Bar Code Medication Administration Bar Code Medication Administration (BCMA) is a barcode system designed to prevent medication errors in healthcare settings and improve the quality and safety of medication administration. The overall goals of BCMA are to improve accuracy, prevent errors, and generate online records of medication administration. It consists of a barcode reader, a portable or desktop computer with wireless connection, a computer server, and some software. When a nurse gives medicines to a patient in a healthcare setting, the nurse can scan barcode on the wristband on the patient and make sure that the patient is the right patient. The nurse can then scan the barcode on medicine, the nurse and the software can then verify if it is the right medicine at the right dose at the right time by the right route ("Five rights").[1] Bar Code Medication administration was designed as an additional check to aid the nurse in administering medications; however, it cannot replace the expertise and professional judgment of the nurse. BCMA was first implemented in 1995 [2] at the Colmery-O'Neil Veteran Medical Center in Topeka, Kansas, USA. It was conceived by a nurse who was inspired by a car rental service using barcode. From 1999 to 2001, Department of Veterans Affairs promoted the system to 161 facilities.[3] Cummings and others recommend the BCMA system for its reduction of errors. They suggest healthcare settings to consider the system first while they are waiting for RFID. They also pointed out that adopting the system takes a careful plan and a deep change in work patterns.[4]
References [1] Felkey, B., Fox, B. & Thrower, M. (2006) Health Care Informatics: A Skills-Based Resource. Washington: American Pharmaceutical Association. [2] Wideman, M. V., Whittler, M. E., & Anderson, T. M. (n.d.). Barcode medication administration: Lessons learned from an intensive care u implementation . Retrieved from Harry S. Truman Memorial Veterans Hospital website: http:/ / www. ahrq. gov/ downloads/ pub/ advances/ vol3/ Wideman. pdf [3] Coyle, G. A., & Heinen, M. (2005). Evolution of BCMA Within the Department of Veterans Affairs. Nursing Administration Quarterly, 29(1), 32-38. [4] Cummings J., Bush P., Smith D., Matuszewski K. Bar-coding medication administration overview and consensus recommendations. American Journal of Health System Pharmacy, 62(24), 2626-2629.
The Canadian Pharmaceutical Bar Coding Project: The Institute for Safe Medication Practices Canada (ISMP Canada) and Canadian Patient Safety Institute (CPSI) http://www.ismp-canada.org/barcoding/index.htm
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Bidirectional Health Information Exchange
Bidirectional Health Information Exchange BHIE is an acronym for Bidirectional Health Information Exchange, a series of communications protocols developed by the Department of Veterans Affairs. It is used to exchange healthcare information between Department of Veterans Affairs healthcare facilities nationwide and between VA healthcare facilities and Department of Defense healthcare facilities. It is one of the most widely used healthcare data exchange systems in routine healthcare use, and is used to facilitate healthcare data exchange associated with a patient's medical record.
Types of data managed Outpatient pharmacy data, allergy data, patient identification correlation, laboratory result data (including surgical pathology reports, cytology and microbiology data, chemistry and hematology data), lab orders data, radiology reports, problem lists, encounters, procedures, and clinical notes are examples of the types of healthcare data that are exchanged using BHIE.
Integration with Electronic Health Record systems BHIE is currently integrated into the VistA EMR (electronic medical record) system used nationwide in Department of Veterans Affairs hospitals. This integration is able to provide increased efficiency in healthcare for veterans. Veterans Hospitals have regional specialized capabilities, and veterans often travel to receive specialized care. Their VistA medical records are able to be transmitted in their entirety using this protocol.
History GCPR to BHIE- a brief history In response to 1998 Presidential Review Directive 5 [1], the Department of Defense (DoD), the Department of Veterans Affairs (VA), and the US Indian Health Service (IHS) collaborated to create the first developmental instances of a secure data-sharing system for electronic patient record data. This was initially called the Government Computer-based Patient Record system, or GCPR. The development of GCPR used UML modeling tools to define the various expected use cases where medical Care Providers in any Medical Treatment Facility (MTF) would need to have access to patient records or other data from within another participating agency. The UML modeling design was selected for its ability to clearly define the business logic that would be required for the GCPR Framework in an object-oriented way, and for its ability to provide detailed tracking of the iterative development of the Framework software. The UML model for the Framework is still used for the ongoing maintenance and support of the BHIE system. Early development of the GCPR system proved that it could meet the requirements of a robust interagency data sharing system, but details of implementation, policy, and security management issues caused delays in full implementation of the GCPR system as it was originally designed. As the project progressed, the Indian Health Service withdrew from GCPR participation, and agreements between the DoD and the VA led to the GCPR Near-Term Solution (GCPR-NTS) being managed principally by the VA, with support from the DoD. The VA installed the preliminary systems for GCPR-NTS in the VA Silver Spring, MD OIFO, where extensive testing took place between the DoD EI/DS and the VA CPRS developers. These teams worked together to finalize the needed infrastructure and security systems for one-way data transport of DoD Separatee data to the VA. The GCPR-NTS was structurally designed to house a static repository of this DoD Separatee data for use by VA Care providers. This one-way transfer of data from DoD to the VA repository continues to be one of the principal functions of the BHIE system.
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Bidirectional Health Information Exchange Upon completion of initial testing, the VA deployed another GCPR-NTS system into the Austin Automation Center, in Austin Texas. This system became the “Production” environment, which came to be known as the GCPR-Mid-Term Solution (GCPR-MTS). As the use of the system grew within the VA, it was later renamed to become the Federal Healthcare Information Exchange, or FHIE. The previously constructed system in the Silver Spring OIFO was re-tasked to become an iterative testing environment for proofing planned changes prior to deployment in the FHIE Production system in Austin. In 2004, interest in the system for use within the DoD was renewed, and further development was done to add a true bi-directional connection component to the FHIE system. Initially called the Data Sharing Initiative (DSI), adapters were added to the FHIE system using the Web Services XML-based protocol standard. A similar Web Services adapter was developed for the DoD to connect to their CHCS-I legacy patient record systems. In this way, both systems hosted a peer Web Services client that is accessible to the other with proper authentication, allowing bi-directional, query-based data exchanges between the disparate systems. Direct cross-Domain write capability and fully computable data storage and transfers are not supported at this time. With the addition of the DSI components to the FHIE system, the entire project was renamed the Federal Bi-Directional Healthcare Information Exchange, or BHIE. All references to FHIE (other than historical) are generally being phased out. BHIE represents the previous Framework System as was deployed for the VA, and all additional capabilities added to support near-real-time data exchange between the Framework and participating DoD Medical Treatment Facilities (MTFs). In short: FHIE + DSI = BHIE. The current BHIE Project participants are exclusively the Department of Defense (DoD) and the Department of Veterans Affairs (VA), though any number of additional Domains will probably be added over time with proper development of adapters and policies. This project has the support of the VA Under-Secretary for Health, and the Acting Assistant Secretary of Defense/Health Affairs of DoD. There is also congressional interest in a successful outcome to this work. Since 2Q-FY05, the DoD is supporting the development of a separate DoD BHIE Domain, including dedicated hardware and infrastructure to support this new system within the DISA network. The details of the DoD system are still in development, as are the details of the expected interoperability with the existing VA BHIE system. Additional data types were added to the system during the 2005-2006 operational periods, including the provision of Discharge Summaries from selected DoD MTFs, and the inclusion of Pre-Post deployment form data availability. In March 2006, the usage of BHIE across the country was outlined before the House Committee on Veterans Affairs. In 2007 the DoD's AHLTA interface was connected to BHIE to allow AHLTA clinicians to see VA data and VA clinicians to see DoD data stored within the CDR. Additionally in 2007 the Theater Medical Data Store (TMDS) was connected to BHIE to allow VA and DoD clinicians to access medical records from combat theaters. In the 2007-2009 years, a parallel “two-pass” system for exchanging imaging metadata was added to the scope of BHIE. A special-purpose server, the BHIE Imaging Adapter (BIA) was added to the other BHIE systems. This BIA server takes the first pass of an Image Study query, obtains metadata about the images for a specific patient from the BHIE system, then presents a list of available images to the end-user, who can then select the images of interest from the list. The BIA then has variable functions as an intelligent proxy for retrieving and delivering the selected images. As of 2011, other additional functions related to images are being added to both the BIA and BHIE systems. From 2008 through 2011, the central focus of BHIE was to upgrade the system hardware and migrate all of the Production functions onto the new hardware. The upgrades began in the spring of 2009, when the initial sets of hardware were delivered and development began to create a set of identical-hardware environments on which the BHIE systems' migration could occur. The migration to the new Production BHIE location in Philadelphia, PA was accomplished in January 2011, and enhancements to all of the systems continue as an ongoing process. The Austin “Legacy” BHIE system remained in Production operation until 2011, when the replacement BHIE hardware installed in Philadelphia, PA assumed all of those functions. The Austin systems went dark and were
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Bidirectional Health Information Exchange retired from service in April 2011.
References [1] http:/ / www. fas. org/ irp/ offdocs/ prd-5-report. htm
External links • "BHIE overview" (http://www1.va.gov/VADODHEALTHITSHARING/ Bidirectional_Health_Information_Exchange.asp). Department of Veterans Affairs. Archived (http://web. archive.org/web/20100407050407/http://www1.va.gov/VADODHEALTHITSHARING/ Bidirectional_Health_Information_Exchange.asp) from the original on 7 April 2010. Retrieved Apr 1, 2010.
Classification Commune des Actes Médicaux Classification Commune des Actes Médicaux is a French medical classification for clinical procedures. Starting in 2005, the CCAM serves as the reimbursement classification for clinicians. The CCAM was evaluated using OpenGALEN tools and technologies. This classification is used to establish • In private practice and hospital fees for acts performed during technical consultations • In private clinics, the fees for procedures performed • In public and private hospitals, the DRG and its pricing of hospital stays provided to health insurance as part of T2 A. The choice of acts of this nomenclature is up to the Evaluation Commission of Acts Professionals (CEAP) of the High Authority of Health It coexists with the Nomenclature Générale des Actes Professionnels (NGAP).
Structure In the version V2, the ACPC 7623 codes included. Each is accompanied by wording to clarify its meaning unambiguously followed by its price in euros and tariff details.
Code Principal Explicit hierarchical coding. This code and / or its title in the presence of personally identifiable information may impair the protection of people and lift the confidentiality of those who entrust themselves to organizations and managed care organization. Each code comprises the four letters and three numbers. • • • •
The first letter refers to a large anatomical unit; The second letter indicates the body (or function) in the unit corresponding to the first letter; The third letter denotes the action performed; The fourth letter identifies the surgical approach or technique used.
The next three digits are used to differentiate between acts with four identical letters keys. e.g. HHFA001: Appendectomy, for the first quadrant HH. F A. 001 Action Technical topography Counter
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Classification Commune des Actes Médicaux
Hierarchical ACPC CCAM codes are structured in a tree whose top-level comprises 19 chapters, organized mainly by large anatomical structure or function: • • • • • • • • • • • • • •
01. central nervous system, device and independent 02. eye and notes 03. ear 04. circulatory 05. immune system and hematopoietic 06. respiratory 07. digestive 08. urinary and genital 09. acts on the reproductive, pregnancy and the newborn 10. endocrine and metabolic 11. osteoarticular apparatus and muscle of the head 12. osteoarticular apparatus and muscle neck and trunk 13. osteoarticular apparatus and muscle of the upper limb 14. osteoarticular apparatus and muscle of lower limb
• • • • •
15. osteoarticular apparatus and muscle without precision surveying 16. integumentary system - mammary glands 17. acts without precision surveying 18. anesthetic actions and additional statements 19. transitional adjustments to the acpc
The second level separates the diagnostic and therapeutic procedures, it is optionally followed by one or more sub-levels.
Modifiers acts and association Some acts may receive more than their one or more main code details called Modifiers. A modifier is information associated with a label that identifies a particular criterion for the performance of an act or his recovery. It applies to a specific list of acts. Modifiers are explicitly allowed in respect of each of the acts concerned. The application of a modifier leads to a rate increase of the act. Only modifiers can be charged in connection with acts that have a tariff. The description of these modifiers is found in Article III-2 of Book III of the General Provisions official. Four modifiers than can be priced by deed. In the context of pricing, the association of acts is the realization of several acts at the same time, for the same patient by the same doctor, since there is no incompatibility between these acts. Codes 1,2,3,4 or 5 and their application rates of these associations are listed in Article III-3 of Paper III.
Versions of CCAM Version 22 of the'TechnicalACPC will be applicable on September 30, 2013 for clinics and public hospitals. Version 21 shall be in use until that date. The construction of theclinical ACPC on intellectual activities that is to say without tools or technical movement provided by the medical convention of 2005 was due to start before 2007. A survey of clinicians from FIFG is announced for late 2010.
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Classification Commune des Actes Médicaux
Revision history http://www.ameli.fr/fileadmin/user_upload/documents/DATE_CCAM.pdf: • • • • • • • • • • • • • • •
ACPC's V23 01.25.2011 Official Journal of 26 December 2010 applicable as of January 25, 2011 V22 ACPC of 30/09/2010 (bariatric surgery, hyperbaric medicine, respiratory support, ...) ACPC V21 from 25/05/2010 (recasting of Anatomy Cyto Pathology) V20 ACPC 01/05/2010 (recast EBRT) ACPC V19 from 01/02/2010 V18 ACPC 01/01/2010 V17 ACPC of 19/10/2009 V16 ACPC 28/05/2009 ACPC V15 from 21/12/2008 (12001 codes acts) ACPC V14, 16/10/2008 ACPC V13 from 01/05/2008 V12 ACPC of 14/03/2008 ACPC 28/12/2007 V11 (7838 rate changes compared to version 10). V10 ACPC of 12/09/2007
• • • • • • • •
ACPC V9 of 28/06/2007 V8 ACPC on 16/05/2007 ACPC V7 of 16/04/2007 ACPC's V6 16/09/2006 ACPC V2 from 01/09/2005 ACPC V1 25/03/2005 ACPC V0bis, 27/11/2003 V0 ACPC, 2002
Learn more about the site Health Insurance [1] = 000310000000's ATIH [2]
References [1] http:/ / www. ameli. fr/ accueil-de-la-ccam/ index. php [2] http:/ / www. atih. sante. fr/ ?id
External links • The site of the ACPC (http://www.ameli.fr/accueil-de-la-ccam/index.php/) • Evaluation Commission Acts Professionals (CEAP) of the Haute Autorité de Santé (HAS) (http://www. has-sante.fr/portail/upload/docs/application/pdf/reglement_interieur_ceap_valide22nov06.pdf) • [Search http://www.codage.ext.cnamts.fr/codif/ccam/acts in the ACPC] • FAQs on the ACPC (http://www.ccam.sante.fr) • ACPC website of Health Insurance (http://www.ameli.fr/77/DOC/2300/enquete.html) • the website ATIH (http://www.atih.sante.fr/index.php?id=000310000000) • Rover: free software for viewing and research in the ACPC (http://omiro.free.fr)
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Classification of Pharmaco-Therapeutic Referrals
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Classification of Pharmaco-Therapeutic Referrals Classification of Pharmaco-Therapeutic Referrals. MEDAFAR
MEDAFAR logo. Author
Raimundo Pastor Sánchez, Carmen Alberola Gómez-Escolar, Flor Álvarez de Toledo Saavedra, Nuria Fernández de Cano Martín, Nancy Solá Uthurry.
Original title
Clasificación de Derivaciones Fármaco-terapéuticas. MEDAFAR
Country
Spain
Language
English
Subject
Medicine
Publisher
IMC
Publication date 2008 Pages
96
ISBN
978-84-691-8426-4
The Classification of Pharmaco-Therapeutic Referrals (CPR) is a taxonomy focused to define and group together situations requiring a referral from pharmacists to physicians (and vice versa) regarding the pharmacotherapy used by the patients. It has been published in 2008. It is bilingual: English/Spanish (Clasificación de Derivaciones Fármaco-terapéuticas).[1] It is a simple and efficient classification of pharmaco-therapeutic referrals between physicians and pharmacists permitting a common inter-professional language.[2] It is adapted to any type of referrals among health professionals, and to increase its specificity it can be combined with ATC codes, ICD-10, and ICPC-2 PLUS. It is a part of the MEDAFAR Project, whose objective is to improve, through different scientific activities, the coordination processes between physicians and pharmacists working in primary health care.[3][4][5][6]
Supporting institutions • Pharmaceutical Care Foundation of Spain (Fundación Pharmaceutical Care España) • Spanish Society of Primary Care Doctors (Sociedad Española de Médicos de Atención Primaria) (SEMERGEN)
Authors • Raimundo Pastor Sánchez (Family practice, "Miguel de Cervantes" Primary Health Centre SERMAS Alcalá de Henares – Madrid – Spain) • Carmen Alberola Gómez-Escolar (Pharmacist, Vice-President Fundación Pharmaceutical Care España) • Flor Álvarez de Toledo Saavedra (Community pharmacist, Past-President Fundación Pharmaceutical Care España)
Classification of Pharmaco-Therapeutic Referrals • Nuria Fernández de Cano Martín (Family practice, "Daroca" Primary Health Centre SERMAS Madrid – Spain) • Nancy Solá Uthurry (Doctor in Pharmacy, Fundación Pharmaceutical Care España)
Structure It is structured in 4 chapters (E, I, N, S) and 38 rubrics. The terminology used follows the rules of ICPC-2.[7] Each rubric consists in an alphanumeric code (the letter corresponds to the chapters and the number to the component) and each title of the rubric (the assigned name) is expressed and explained by: – A series of terms related with the title of the rubric. – A definition expressing the meaning of the rubric – A list of inclusion criteria and another list with exclusion criteria to select and qualify the contents corresponding to a rubric. – Some example to illustrate every term. It also includes a glossary of 51 terms defined by consensus, an alphabetical index with 350 words used in the rubrics; and a standardized model of inter-professional referral form, to facilitate referrals from community pharmacists to primary care physicians.
Classification of Pharmaco-Therapeutic Referrals MEDAFAR E. Effectiveness / Efficiency • E 0. Effectiveness / Efficiency, unspecified • E 1. Indication • E 2. Prescription and dispensing conditions • E 3. Active substance / excipient • E 4. Pharmaceutical form / how supplied • E 5. Dosage • E 6. Quality • E 7. Storage • E 8. Consumption • E 9. Outcome.
I. Information / Health education • I 0. Information / Health education, unspecified • I 1. Situation / reason for encounter • I 2. Health problem • I 3. Complementary examination • I 4. Risk • I 5. Pharmacological treatment • I 6. No pharmacological treatment • I 7. Treatment goal • I 8. Socio-healthcare system.
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N. Need • N 0. Need, unspecified • N 1. Treatment based on symptoms and/or signs • N 2. Treatment based on socio–economic-work issues • N 3. Treatment based on public health issues • N 4. Prevention • N 5. Healthcare provision • N 6. Complementary test for treatment control • N 7. Administrative activity • N 8. On patient request (fears, doubts, wants).
S. Safety • S 0. Safety, unspecified • S 1. Toxicity • S 2. Interaction • S 3. Allergy • S 4. Addiction (dependence) • S 5. Other side effects • S 6. Contraindication • S 7. Medicalisation • S 8. Non-regulate substance • S 9. Data / confidentiality.
References [1] Pastor Sánchez R, Alberola Gómez-Escolar C, Álvarez de Toledo Saavedra F, Fernández de Cano Martín N, Solá Uthurry N. Classification of Pharmaco-Terapeutic Referrals (CPR). MEDAFAR. Madrid: IMC; 2008. (http:/ / www. medafar. com/ eWebs/ GetFicheroDoc. do?doc=580632& ver=581762& fichero=580632_fi_4. pdf& entorno=lectura) ISBN 978-84-691-8426-4 [2] Álvarez de Toledo F, Pastor Sánchez R. La Clasificación de Derivaciones Fármaco-Terapéuticas: una herramienta para la coordinación entre médicos y farmacéuticos. Ars Pharm. 2011; 52(Supl 1):20-5. [3] García Cebrián F. La seguridad del paciente y la colaboración entre médicos y farmacéuticos [editorial]. SEMERGEN. 2006; 32(2):55-7. [4] Pastor Sánchez R, Barbero González A, del Barrio Sánchez H, García Olmos LM, editores. Comunicación interprofesional en atención primaria de salud. Madrid: REAP; 1996. [5] Uribe G, Martínez de la Hidalga G. Médicos y farmacéuticos: éxitos y fracasos de colaboración profesional. SEMERGEN. 2002;28(2):86-8. [6] Cervera Barba EJ, Sagredo Pérez J, Martín González MC, Heras Salvat G, Peña Rodríguez E, Suárez del Villar Acebal E, et al. Oficinas de farmacia y centros de salud: podemos trabajar juntos. Una experiencia de colaboración. SEMERGEN. 2004;30(10):491-7. [7] International Classification Committee of WONCA. ICPC-2 International Classification of Primary care (2 ed.). Oxford: Oxford University Press; 1998.
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Bibliography • Pastor Sánchez R, Alberola Gómez-Escolar C, Álvarez de Toledo Saavedra F, Fernández de Cano Martín N, Solá Uthurry N. Clasificación de Derivaciones Fármaco-terapéuticas (CDF). MEDAFAR. Madrid: IMC; 2008. (http:// www.medafar.com/eWebs/GetFicheroDoc.do?doc=580632&ver=581762&fichero=580632_fi_3.pdf& entorno=lectura) ISBN 978-84-691-8426-4 • Álvarez de Toledo Saavedra F, Fernández de Cano Martín N, coordinadores. MEDAFAR Asma. Madrid: IMC; 2007. (http://www.medafar.com/eWebs/GetFicheroDoc.do?doc=580632&ver=581762& fichero=580632_fi_2.pdf&entorno=lectura) • Álvarez de Toledo Saavedra F, Fernández de Cano Martín N, coordinadores. MEDAFAR Hipertensión. Madrid: IMC; 2007. (http://www.medafar.com/eWebs/GetFicheroDoc.do?doc=580632&ver=581758& fichero=580632_fi_1.pdf&entorno=lectura) • Aranaz JM, Aibar C, Vitaller J, Mira JJ, Orozco D, Terol E, Agra Y. Estudio sobre la seguridad de los pacientes en atención primaria de salud (Estudio APEAS). Madrid: Ministerio de Sanidad y Consumo; 2008. (http://www. msc.es/organizacion/sns/planCalidadSNS/docs/estudio_apeas.pdf) • Aranaz JM, Aibar C, Vitaller J, Ruiz P. Estudio Nacional sobre los Efectos Adversos ligados a la Hospitalización. ENEAS 2005. Madrid: Ministerio de Sanidad y Consumo; 2006. (http://www.msc.es/organizacion/sns/ planCalidadSNS/pdf/excelencia/opsc_sp2.pdf) • Criterios de derivación del farmacéutico al médico general/familia, ante mediciones esporádicas de presión arterial. Consenso entre la Sociedad Valenciana de Hipertensión y Riesgo Vascular (SVHTAyFV) y la Sociedad de Farmacia Comunitaria de la Comunidad Valenciana (SFaC-CV). 2007. (http://www.atencionfarmaceutica. org/contenido.php?mod=novedades&id=31) • Fleming DM (ed). The European study of referrals from primary to secondary care. Exeter: Royal College of General Practitioners; 1992. • Foro de Atención Farmacéutica. Documento de consenso 2008. Madrid: MSC, RANF, CGCOF, SEFAP, SEFAC, SEFH, FPCE, GIAFUG. 2008. (http://www.pharmaceutical-care.org/doccontenidos/documentos/ FORO_At_farma.pdf) • García Olmos L. Análisis de la demanda derivada en las consultas de medicina general en España. Tesis doctoral. Madrid: Universidad Autónoma de Madrid; 1993. • Garjón Parra J, Gorricho Mendívil J. Seguridad del paciente: cuidado con los errores de medicación. Boletín de Información Farmacoterapéutica de Navarra. 2010;18(3) (http://www.navarra.es/appsext/DescargarFichero/ default.aspx?codigoAcceso=PortalDeSalud&fichero=bit/Bit_v18n3.pdf) • Gérvas J. Introducción a las classificaciones en Atención Primaria, con una valoración técnica de los "Consensos de Granada". Pharm Care Esp. 2003; 5(2):98-104. • Hospital Ramón y Cajal, Área 4 Atención Primaria de Madrid. Guía Farmacoterapéutica. Madrid; 2005. CD-ROM. • Ley 29/2006, de 26 de julio, de garantías y uso racional de los medicamentos y productos sanitarios. BOE. 2006 julio 27; (178): 28122-65. • Ley 41/2002, de 14 de noviembre, básica reguladora de la autonomía del paciente y de derechos y obligaciones en materia de información y documentación clínica. BOE. 2002 noviembre 15; (274): 40126-32. • Ley Orgánica 15/1999, de 13 de diciembre, de Protección de Datos de Carácter Personal. BOE. 1999 diciembre 14; (298): 43088-99. • Organización Médica Colegial. Código de ética y deontología médica. Madrid: OMC; 1999. • Palacio Lapuente F. Actuaciones para la mejora de la seguridad del paciente en atención primaria [editorial]. FMC. 2008; 15(7): 405-7.
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Classification of Pharmaco-Therapeutic Referrals • Panel de consenso ad hoc. Consenso de Granada sobre Problemas Relacionados con medicamentos. Pharm Care Esp. 1999; 1(2):107-12. • Prado Prieto L, García Olmos L, Rodríguez Salvanés F, Otero Puime A. Evaluación de la demanda derivada en atención primaria. Aten Primaria. 2005; 35:146-51. • Starfield B. Research in general practice: co-morbidity, referrals, and the roles of general practitioners and specialists. SEMERGEN. 2003; 29(Supl 1):7-16. • WONCA Classification Committee. An international glossary for general/family practice. Fam Pract. 1995; 12(3): 341-69. • World Alliance for Patient Safety. International Classification for Patient Safety (ICPS). 2007. (http://www. who.int/patientsafety/taxonomy/en/)
External links • Classification of Pharmaco-Terapeutic Referrals (CPR) (http://www.medafar.com/eWebs/GetFicheroDoc. do?doc=580632&ver=581762&fichero=580632_fi_4.pdf&entorno=lectura) • Clasificación de Derivaciones Fármaco-terapéuticas (CDF) (http://www.medafar.com/eWebs/GetFicheroDoc. do?doc=580632&ver=581762&fichero=580632_fi_3.pdf&entorno=lectura) • • • •
MEDAFAR (http://www.medafar.com/) SEMERGEN (http://www.semergen.es/) Fundación Pharmaceutical Care España (http://www.pharmaceutical-care.org/) ICPC-2e (http://www.kith.no/templates/kith_WebPage____1062.aspx) (by the Norwegian Centre for Informatics in Health and Social Care)] • International Classification of Diseases (ICD) (http://www.who.int/classifications/icd/en/) • ICD-10 (http://apps.who.int/classifications/apps/icd/icd10online/) • Código ATC (Anatomical Therapeutic Chemical drug classification) (http://www.whocc.no/)
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Clinical Context Object Workgroup In the context of Health informatics, CCOW or Clinical Context Object Workgroup is an HL7 standard protocol designed to enable disparate applications to synchronize in real time, and at the user-interface level. It is vendor independent and allows applications to present information at the desktop and/or portal level in a unified way. CCOW is the primary standard protocol in healthcare to facilitate a process called "Context Management." Context Management is the process of using particular "subjects" of interest (e.g., user, patient, clinical encounter, charge item, etc.) to 'virtually' link disparate applications so that the end-user sees them operate in a unified, cohesive way. Context Management can be utilized for both CCOW and non-CCOW compliant applications. The CCOW standard exists to facilitate a more robust, and near "plug-and-play" interoperability across disparate applications. Context Management is often combined with Single Sign On applications in the healthcare environment, but the two are discrete functions. Single Sign On is the process that enables the secure access of disparate applications by a user through use of a single authenticated identifier and password. Context Management augments this by then enabling the user to identify subjects once (e.g., a patient) and have all disparate systems into which the user is granted access to "tune" to this patient simultaneously. As the user further identifies particular "subjects" of interest (e.g., a particular visit), those applications containing information about the selected subject will then automatically and seamlessly to the user "tune" to that information as well. The end result for the user is an aggregated view of all patient information across disparate applications. Use of Context Management, facilitated by CCOW or non-CCOW compliant applications, is valuable for both client-server, and web-based applications. Furthermore, a fully robust Context Manager will enable use for both client-server and web-based applications on a single desktop / kiosk, allowing the user to utilize both types of applications in a "context aware" session. CCOW works for both client-server and web-based applications. The acronym CCOW stands for "Clinical Context Object Workgroup", a reference to the standards committee within the HL7 group that developed the standard.
Purpose The goal of CCOW is seemingly simple, but its implementation is rather complex. CCOW is designed to communicate the name of the active user between various programs on the same machine. The user should only need to log into one application, and the other applications running on the machine will “know” who is logged in. There are a great deal of exceptions and circumstances that make this scenario far more difficult than it appears. In order to accomplish this task, every CCOW compliant application on the machine must login to a central CCOW server called a Vault. The application sends an encrypted application passcode to verify its identity. Once the application is verified, it may change the active user (also called the “context”) on the machine. Each CCOW application also has an application “name” for which there can only be one instance. There is no correct application name (the passcode identifies which application is logging in). There may be multiple instances of the CCOW application connected to the CCOW vault from the same computer, however they must have different names. One name might be “I like HHAM”, while the other might be “I like CCOW”. The names are completely arbitrary. After the application authenticates itself with the CCOW vault, the applications are ready to communicate the context (a.k.a. the active user). Here would be a step-by-step example of a CCOW exchange: 1. The computer boots up and the medical applications load. 2. Each application logs into CCOW using its secret passcode (and unique application name). 3. The compliant application displays a login prompt, and the user logs in as “Mary Jane”. 4. Mary Jane has a “CCOW ID”. Let us assume that her CCOW ID is “MJane”. 5. The compliant application notifies the CCOW vault that “MJane” is now logged in.
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Clinical Context Object Workgroup 6. Once CCOW verifies that “MJane” is a valid CCOW user, the vault notifies all the other applications that “MJane” is logged in. 7. All of the other applications realize that the CCOW ID “MJane” is referring to “Mary Jane” (because they have been configured as such). They login “Mary Jane” automatically. 8. The transaction is complete. All of the applications running on the machine have been automatically logged in as “Mary Jane”. The purpose of the application passcode is to verify that no unauthorized applications can “hack” into CCOW and change the active user (thereby allowing unauthorized access to medical records).
External links • HL7 Introduction [1] • HL7 CCOW Standard [2]
References [1] http:/ / www. HL7. com. au/ FAQ. htm [2] http:/ / www. hl7. com. au/ CCOW. htm
Clinical Data Interchange Standards Consortium The Clinical Data Interchange Standards Consortium (CDISC) is an open, multidisciplinary, neutral, 501(c)(3) non-profit standards developing organization (SDO) that has been working through productive, consensus-based collaborative teams, since its formation in 1997, to develop global standards and innovations to streamline medical research and ensure a link with healthcare. The CDISC mission is "to develop and support global, platform-independent data standards that enable information system interoperability to improve medical research and related areas of healthcare." The CDISC Vision is "informing patient care and safety through higher quality medical research.".The CDISC suite of standards supports medical research of any type from protocol through analysis and reporting of results. They have been shown to decrease resources needed by 60% overall and 70-90% in the start-up stages when they are implemented at the beginning of the research process [citation needed]. They are harmonized through a model that is now not only a CDISC standard but also an HL7 standard on the path to becoming an ISO/CEN standard, thus giving the CDISC standards (harmonized together through BRIDG) international status and accreditation.
CDISC History • • • •
Late 1997 - Started as a Volunteer group Summer 1998 - Invited to form DIA SIAC Feb 2000 - formed an Independent, non-profit organization Dec 2001 - Global participation
CDISC standards • Study Data Tabulation Model (SDTM) • Highlights: recommended for FDA regulatory submissions since 2004. • Study Data Tabulation Model SDTM Implementation Guide (SDTM-IG) • Gives a standardized, predefined collection of submission metadata "Domains" containing extensive variable collections.
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Clinical Data Interchange Standards Consortium • Analysis Data Model (ADaM) • Designed to complement the SDTM submission by detailing the statistical analysis performed on the clinical trial results. • Standard for Exchange of Non-clinical Data (SEND) • The animal trial equivalent of SDTM. • Operational Data Model (ODM) • The highlights of ODM: includes audit trail, utilizes XML technology, machine- and human- readable, all information are independent from databases, storing of ODM is independent from hard- and software. • Laboratory Data Model (LAB) • The Lab standard is used for exchange of laboratory data between labs and CROs • Case Report Tabulation Data Definition Specification (CRT-DDS) • Also referred to as "define.xml", a machine readable version of the regulatory submission "define.pdf". • Clinical Data Acquisition Standards Harmonization (CDASH) • Defines a minimal data collection set for sixteen safety SDTM Domains, harmonizing element names, definitions and metadata. The objective is to establish a standardized data collection baseline across all submissions. • CDISC Terminology • Defines controlled terminology for SDTM and CDASH, provides extensible lists of controlled terms designed to harmonize data collected across submissions.
CDISC registered solutions providers CDISC maintains a list of solutions providers, subject matter experts and consultants deemed to have sufficient knowledge and experience implementing the various CDISC standards.
ODM and EDC integration Electronic Data Capture (EDC) systems can be certified as compliant with the Operational Data Model (ODM) by CDISC. There are two main types of integration, ODM Import and ODM Export.
ODM Import Full import allows importing of ODM-formatted clinical data (MetaData and Data). MetaData only import allows only the importing of MetaData. This is useful for setting up the EDC system to capture data. Basically allows third party software to define the forms, variables etc. used in the EDC system. This provides an EDC vendor-neutral system for defining a study.
ODM Export The EDC system will generate ODM data files for further processing.
Further reading • Rebecca Daniels Kush (2003), eClinical Trials: Planning and Implementation, CenterWatch / Thomson Healthcare, ISBN 1-930624-28-X • A J de Montjoie (2009), 'Introducing the CDISC Standards: New Efficiencies for Medical Research', CDISC Publications
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External links • Official website [1]
References [1] http:/ / www. cdisc. org/
Clinical Document Architecture The HL7 Clinical Document Architecture (CDA) is a XML-based markup standard intended to specify the encoding, structure and semantics of clinical documents for exchange. CDA is an ANSI-certified standard from Health Level Seven (HL7.org). Release 1.0 was published in November, 2000 and Release 2.0 was published with the HL7 2005 Normative Edition. CDA specifies the syntax and supplies a framework for specifying the full semantics of a clinical document. It defines a clinical document as having the following six characteristics: • Persistence • • • • •
Stewardship Potential for authentication Context Wholeness Human readability
A CDA can contain any type of clinical content. Typical CDA documents would be a Discharge Summary, Imaging Report, Admission & Physical, Pathology Report and so on. CDA uses XML, although it allows for a non-XML body (pdf, Word, jpg and so on) for simple implementations. It was developed using the HL7 Development Framework (HDF) and it is based on the HL7 Reference Information Model (RIM) and the HL7 Version 3 Data Types. The CDA specifies that the content of the document consists of a mandatory textual part (which ensures human interpretation of the document contents) and optional structured parts (for software processing). The structured part relies on coding systems (such as from SNOMED and LOINC) to represent concepts. CDA Release 2 has been adopted as an ISO standard, ISO/HL7 27932:2009.
Transport The CDA standard doesn't specify how the documents should be transported. CDA documents can be transported using HL7 version 2 messages, HL7 version 3 messages, IHE protocols such as XDS, as well as by other mechanisms including: DICOM, MIME attachments to email, http or ftp.
Country specific notes In the U.S. the CDA standard is probably best known as the basis for the Continuity of Care Document (CCD) specification, based on the data model as specified by ASTMs Continuity of Care Record. The U.S. Healthcare Information Technology Standards Panel has selected the CCD as one of its standards.
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References External links • • • • • •
Structured Documents Group (http://www.hl7.org/Special/committees/structure/) of HL7 CDA Resource Page (http://hl7book.net/index.php?title=CDA) Introduction to the HL7 Standards (http://www.HL7.com.au/FAQ.htm) Whitepaper: HL7 version 3: message or document? (http://www.ringholm.de/docs/04200_en.htm) About HL7 CDA (http://iehr.eu/standards/hl7-cda/) at iEHR.eu (http://iehr.eu) The CDA Book (http://books.google.com/books?id=rwa6DDB4jY8C) by Keith Boone
Continuity of Care Document The Continuity of Care Document (CCD) specification is an XML-based markup standard intended to specify the encoding, structure, and semantics of a patient summary clinical document for exchange.
Structure The CCD specification is a constraint on the HL7 Clinical Document Architecture (CDA) standard. The CDA specifies that the content of the document consists of a mandatory textual part (which ensures human interpretation of the document contents) and optional structured parts (for software processing). The structured part is based on the HL7 Reference Information Model (RIM) and provides a framework for referring to concepts from coding systems, such as the SNOMED or the LOINC.[citation needed] The patient summary contains a core data set of the most relevant administrative, demographic, and clinical information facts about a patient's healthcare, covering one or more healthcare encounters. It provides a means for one healthcare practitioner, system, or setting to aggregate all of the pertinent data about a patient and forward it to another practitioner, system, or setting to support the continuity of care. Its primary use case is to provide a snapshot in time containing the pertinent clinical, demographic, and administrative data for a specific patient. The CCD specification contains U.S. specific requirements; its use is therefore limited to the U.S. The U.S. Healthcare Information Technology Standards Panel has selected the CCD as one of its standards. CCDs are quickly becoming one of the most ubiquitous and thorough means of transferring health data on patients as each can contain vast amounts of data based on the standard format, in a relatively easy to use and portable file.
Development history CCD was developed by HL7 with consultation and advice from several members of ASTM E31, the technical committee responsible for development and maintenance of the Continuity of Care Record (CCR) standard. In the opinion of HL7 and its members, the CDA CCD combines the benefits of ASTMs Continuity of Care Record (CCR) and the HL7 Clinical Document Architecture (CDA) specifications. It is intended as an alternate implementation to the one specified in ASTM ADJE2369 for those institutions or organizations committed to implementation of the HL7 Clinical Document Architecture.Wikipedia:Verifiability The public library is relatively limited of reference CCDs available for developers to examine how to encode medical data using the structure and format of the CCD. Not surprisingly, different Electronic Health Record vendors have implemented the CCD standard in different and often incompatible ways. The National Institute of Standards and Technology (NIST) has produced a sample CCD with valid data that is available for public download.[1]
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CCD and Meaningful Use As part of U.S. federal incentives for the adoption of electronic health records, known as Meaningful Use, the CCD and Continuity of Care Record (CCR) were both selected as acceptable extract formats for clinical care summaries in the program's first stage. To be certified for this federal program, an Electronic Health Record must be able to generate a CCD (or equivalent CCR) that has the sections of allergies, medications, problems, and laboratory results, in addition to patient header information.[2] Several of these sections also have mandated vocabularies, such as LOINC for laboratory results, according to the federal program.[citation needed] When ambulatory and inpatient care providers attest that they have achieved the first stage of Meaningful Use, they document that they have tested their capability to "exchange clinical information and patient summary record," which is a core objective of the program. Most Electronic Health Record vendors have adopted the CCD rather than the Continuity of Care Record since it is a newer format that harmonizes the Continuity of Care Record and the HL7 Clinical Document Architecture (CDA) specifications. For the proposed second stage of Meaningful Use, the CCD is planned to become the primary extract format for clinical care summaries as part of the Consolidated Clinical Document Architecture.[3]
Competition and Internet Health Industry Standards CCD and Continuity of Care Record (CCR) are often seen as competing standards. The now-defunct Google Health supported a subset of CCR,[4] while Microsoft HealthVault claims to support a subset of both CCR and CCD.
References [1] [2] [3] [4]
http:/ / xreg2. nist. gov/ cda-validation/ downloads/ HITSP_C32_Examples_Jan2010. zip http:/ / healthcare. nist. gov/ docs/ 170. 304. i_ExchangeClinicalinfoPatientSummaryRecordAmb_v1. 1. pdf http:/ / www. gpo. gov/ fdsys/ pkg/ FR-2012-03-07/ pdf/ 2012-4430. pdf http:/ / code. google. com/ apis/ health/ ccrg_reference. html
Bibliography • http://www.corepointhealth.com/whitepapers/ continuity-care-document-ccd-changing-landscape-healthcare-information-exchangeWikipedia:Link rot
External links • • • • •
Structured Documents Group (http://www.hl7.org/Special/committees/structure/struc.htm) of HL7 CDA Resource Page (http://hl7book.net/index.php?title=CDA) CCD XML Instance Examples (http://xreg2.nist.gov/cda-validation/downloads.html) Online CCD instance validation tool (http://xreg2.nist.gov/cda-validation/validation.html) at NIST Introduction to the HL7 Standards (http://www.HL7.com.au/FAQ.htm)
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Clinical Document Architecture The HL7 Clinical Document Architecture (CDA) is a XML-based markup standard intended to specify the encoding, structure and semantics of clinical documents for exchange. CDA is an ANSI-certified standard from Health Level Seven (HL7.org). Release 1.0 was published in November, 2000 and Release 2.0 was published with the HL7 2005 Normative Edition. CDA specifies the syntax and supplies a framework for specifying the full semantics of a clinical document. It defines a clinical document as having the following six characteristics: • • • • • •
Persistence Stewardship Potential for authentication Context Wholeness Human readability
A CDA can contain any type of clinical content. Typical CDA documents would be a Discharge Summary, Imaging Report, Admission & Physical, Pathology Report and so on. CDA uses XML, although it allows for a non-XML body (pdf, Word, jpg and so on) for simple implementations. It was developed using the HL7 Development Framework (HDF) and it is based on the HL7 Reference Information Model (RIM) and the HL7 Version 3 Data Types. The CDA specifies that the content of the document consists of a mandatory textual part (which ensures human interpretation of the document contents) and optional structured parts (for software processing). The structured part relies on coding systems (such as from SNOMED and LOINC) to represent concepts. CDA Release 2 has been adopted as an ISO standard, ISO/HL7 27932:2009.
Transport The CDA standard doesn't specify how the documents should be transported. CDA documents can be transported using HL7 version 2 messages, HL7 version 3 messages, IHE protocols such as XDS, as well as by other mechanisms including: DICOM, MIME attachments to email, http or ftp.
Country specific notes In the U.S. the CDA standard is probably best known as the basis for the Continuity of Care Document (CCD) specification, based on the data model as specified by ASTMs Continuity of Care Record. The U.S. Healthcare Information Technology Standards Panel has selected the CCD as one of its standards.
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References External links • • • • • •
Structured Documents Group (http://www.hl7.org/Special/committees/structure/) of HL7 CDA Resource Page (http://hl7book.net/index.php?title=CDA) Introduction to the HL7 Standards (http://www.HL7.com.au/FAQ.htm) Whitepaper: HL7 version 3: message or document? (http://www.ringholm.de/docs/04200_en.htm) About HL7 CDA (http://iehr.eu/standards/hl7-cda/) at iEHR.eu (http://iehr.eu) The CDA Book (http://books.google.com/books?id=rwa6DDB4jY8C) by Keith Boone
Continuity of Care Record Continuity of Care Record (CCR)[1] is a health record standard specification developed jointly by ASTM International, the Massachusetts Medical Society[2] (MMS), the Healthcare Information and Management Systems Society (HIMSS), the American Academy of Family Physicians (AAFP), the American Academy of Pediatrics[3] (AAP), and other health informatics vendors.
CCR Background and Scope The CCR was generated by health care practitioners based on their views of the data they may want to share in any given situation.Wikipedia:Verifiability The CCR document is used to allow timely and focused transmition of information to other health professionals involved in the patient's care. The CCR aims to increase the role of the patient in managing their health and reduce error while improving continuity of patient care.[4] The CCR standard is a patient health summary standard. It is a way to create flexible documents that contain the most relevant and timely core health information about a patient, and to send these electronically from one caregiver to another. The CCR's intent is also to create a standard of health information transportability when a patient is transferred or referred, or is seen by another healthcare professional. [5]
Development of the CCR The CCR is a unique development effort via a syndicate of the following sponsors: -ASTM International -Massachusetts Medical Society -HIMSS -American Academy of Family Physicians -American Academy of Pediatrics -American Medical Association -Patient Safety Institute -American Health Care Association -National Association for the Support of LTC
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Content of the CCR The CCR data set contains a summary of the patient’s health status including problems, medications, allergies, and basic information about health insurance, care documentation, and the patient’s care plan. These represent a "snapshot" of a patient's health data that can be useful or possibly lifesaving, if available at the time of clinical encounter. The ASTM CCR standard's purpose is to permit easy creation by a physician using an electronic health record (EHR) system at the end of an encounter. More specifically within the CCR, there are mandated core elements in 6 sections. These 6 sections are: - Header - Patient Identifying Information - Patient Financial and Insurance Information - Health Status of the Patient - Care Documentation - Care Plan Recommendation
The CCR Standard and Structure Because it is expressed in the standard data interchange language known as XML, a CCR can potentially be created, read, and interpreted by any EHR or EMR software application. A CCR can also be exported to other formats, such as PDF or Office Open XML (Microsoft Word 2007 format). The Continuity of Care Document (CCD) is an HL7 CDA implementation of the Continuity of Care Record (CCR). A CCR document can generally be converted into CCD using Extensible Stylesheet Language Transformations (XSLT), but it is not always possible to perform the inverse transformation, since some CCD features are not supported in CCR.[6] HITSP provides reference information that demonstrates how CCD and CCR (as HITSP C32) are embedded in CDA.[7] Although the CCR and CCD standards could continue to coexist, with CCR providing for basic information requests and CCD servicing more detailed requests, the newer CCD standard might eventually completely supplant CCR.[8]
Technology and the CCR As mentioned, the CCR standard uses eXtensible Markup Language (XML) as it is aimed at being technology neutral to allow for maximum applicability. This specified XML coding provides flexibility that will allow users to formulate, transfer, and view the CCR in a number of ways, for example, in a browser, in a Health Level 7 (HL7) message, in a secure email, as a PDF file, as an HTML file, or as a word document. This is aimed at producing flexible expression of structured data in avenues such as electronic health record (EHR) systems.[9] In terms of the CCR's transportability, secure carriage and transmission of the electronic file can occur via physical transport media, for example on a USB thumb drive, tablet or phone, CD ROM, or smart card, and in an electronic sense, secure transmission can occur via a network line, or the Internet.
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References [1] ASTM CCR Continuity of Care Record (http:/ / www. astm. org/ cgi-bin/ SoftCart. exe/ DATABASE. CART/ PAGES/ E2369. htm?L+ mystore+ luuu5949+ 1167331453) [2] http:/ / www. massmed. org/ [3] http:/ / www. aap. org/ [4] Kibbe, D. C., Phillips, R. L., & Green, L. A. (2004). The Continuity of Care Record. American Family Physician , 70 (7), 1220-1223. [5] Americal Society for Testing and Materials.(2013). Continuity of Care Record:The Concept Paper of the CCR. [6] http:/ / ofps. oreilly. com/ titles/ 9781449305024/ meaningful_use_interoperability. html [7] http:/ / publicaa. ansi. org/ sites/ apdl/ hitspadmin/ Matrices/ HITSP_09_N_451. pdf [8] http:/ / e-caremanagement. com/ untangling-the-electronic-health-data-exchange/ [9] http:/ / www. nchica. org/ Past/ 06/ presentations/ Kibbe. pdf
External links • ASTM CCR Standard E2369-05 (http://www.astm.org/cgi-bin/SoftCart.exe/DATABASE.CART/ REDLINE_PAGES/E2369.htm?E+mystore) • Medical Records Institute - CCR (http://medrecinst.com/pages/about.asp?id=54) • Center for Health Information Technology (CHiT) (http://www.centerforhit.org/online/chit/home.html) • CCR Java library (http://code.google.com/p/ccr4j)
COSTART The Coding Symbols for a Thesaurus of Adverse Reaction Terms (COSTART) was developed by the United States Food and Drug Administration (FDA) for the coding, filing and retrieving of post-marketing adverse reaction reports. COSTART provides a method to deal with the variation in vocabulary used by those who submit adverse event reports to the FDA. Use of this dictionary allowed for standardization of adverse reaction reporting towards the FDA in a consistent way. Recently COSTART was replaced by the MedDRA.
External links • COSTART lookup [1]
References [1] http:/ / hedwig. mgh. harvard. edu/ biostatistics/ files/ costart. html
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Current Procedural Terminology
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Current Procedural Terminology The Current Procedural Terminology (CPT) code set is a medical code set maintained by the American Medical Association through the CPT Editorial Panel.[1] The CPT code set (copyright protected by the AMA) describes medical, surgical, and diagnostic services and is designed to communicate uniform information about medical services and procedures among physicians, coders, patients, accreditation organizations, and payers for administrative, financial, and analytical purposes. New editions are released each October. The current version is the CPT 2013. It is available in both a standard edition and a professional edition. CPT coding is similar to ICD-9 and ICD-10 coding, except that it identifies the services rendered rather than the diagnosis on the claim. ICD code sets also contain procedure codes but these are only used in the inpatient setting. CPT is currently identified by the Centers for Medicare and Medicaid Services (CMS) Care Procedure Coding System.
[2]
as Level 1 of the Health
The Current Procedural Terminology (CPT) was developed by the American Medical Association (AMA)
Types of code There are three types of CPT code: Category I, Category II, and Category III.
Category I Category I CPT Code(s). There are six main sections: Codes for Evaluation and Management: 99201-99499 • • • • • • • • • • • • • • • • • •
(99201 - 99213) office/other outpatient services (99217 - 99220) hospital observation services (99221 - 99239) hospital inpatient services (99241 - 99255) consultations (99281 - 99288) emergency dept services (99291 - 99292) critical care services (99304 - 99318) nursing facility services (99324 - 99337) domiciliary, rest home (boarding home) or custodial care services (99339 - 99340) domiciliary, rest home (assisted living facility), or home care plan oversight services (99341 - 99350) home services (99354 - 99360) prolonged services (99363 - 99368) case management services (99374 - 99380) care plan oversight services (99381 - 99429) preventive medicine services (99441 - 99444) non-face-to-face physician services (99450 - 99457) special evaluation & management services (99460 - 99465) newborn care services (99466 - 99480) inpatient neonatal intensive, and pediatric/neonatal critical, care services
Current Procedural Terminology Codes for Anesthesia: 00100-01999; 99100-99150 • • • • • • • • • • • • • • • •
(00100 - 00222) head (00300 - 00352) neck (00400 - 00474) thorax (00500 - 00580) intrathoracic (00600 - 00670) spine & spinal cord (00700 - 00797) upper abdomen (00800 - 00882) lower abdomen (00902 - 00952) perineum (01112 - 01190) pelvis (except hip) (01200 - 01274) upper leg (except knee) (01320 - 01444) knee & popliteal area (01462 - 01522) lower leg (below knee) (01610 - 01682) shoulder & axillary (01710 - 01782) upper arm & elbow (01810 - 01860) forearm, wrist & hand (01916 - 01936) radiological procedures
• • • • •
(01951 - 01953) burn excisions or debridement (01958 - 01969) obstetric (01990 - 01999) other procedures (99100 - 99140) qualifying circumstances for anesthesia (99143 - 99150) moderate (conscious) sedation
Codes for Surgery: 10021-69990 • • • • • • • • • • • • • • • • •
(10021 - 10022) general (10040 - 19499) integumentary system (20000 - 29999) musculoskeletal system (30000 - 32999) respiratory system (33010 - 37799) cardiovascular system (38100 - 38999) hemic & lymphatic systems (39000 - 39599) mediastinum & diaphragm (40490 - 49999) digestive system (50010 - 53899) urinary system (54000 - 55899) male genital system (55920 - 55980) reproductive system & intersex (56405 - 58999) female genital system (59000 - 59899) maternity care & delivery (60000 - 60699) endocrine system (61000 - 64999) nervous system (65091 - 68899) eye & ocular adnexa (69000 - 69979) auditory system
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Current Procedural Terminology Codes for Radiology: 70010-79999 • • • • • • •
(70010 - 76499) diagnostic imaging (76506 - 76999) diagnostic ultrasound (77001 - 77032) radiologic guidance (77051 - 77059) breast mammography (77071 - 77084) bone/joint studies (77261 - 77799) radiation oncology (78000 - 79999) nuclear medicine
Codes for Pathology & Laboratory: 80047-89398 • • • • • • •
(80047 - 80076) organ or disease-oriented panels (80100 - 80103) drug testing (80150 - 80299) therapeutic drug assays (80400 - 80440) evocative/suppression testing (80500 - 80502) consultations (clinical pathology) (81000 - 81099) urinalysis (82000 - 84999) chemistry
• • • • • • • • • • •
(85002 - 85999) hematology & coagulation (86000 - 86849) immunology (86850 - 86999) transfusion medicine (87001 - 87999) microbiology (88000 - 88099) anatomic pathology (postmortem) (88104 - 88199) cytopathology (88230 - 88299) cytogenetic studies (88300 - 88399) surgical pathology (88720 - 88741) in vivo (transcutaneous) lab procedures (89049 - 89240) other procedures (89250 - 89398) reproductive medicine procedures
Codes for Medicine: 90281-99099; 99151-99199; 99500-99607 • • • • • • • • • • • • • • •
(90281 - 90399) immune globulins, serum or recombinant prods (90465 - 90474) immunization administration for vaccines/toxoids (90476 - 90749) vaccines, toxoids (90801 - 90899) psychiatry (90901 - 90911) biofeedback (90935 - 90999) dialysis (91000 - 91299) gastroenterology (92002 - 92499) ophthalmology (92502 - 92700) special otorhinolaryngologic services (92950 - 93799) cardiovascular (93875 - 93990) noninvasive vascular diagnostic studies (94002 - 94799) pulmonary (95004 - 95199) allergy & clinical immunology (95250 - 95251) endocrinology (95803 - 96020) neurology & neuromuscular procedures
• (96101 - 96125) central nervous system assessments/tests (neuro-cognitive, mental status, speech testing) • (96150 - 96155) health & behavior assessment/intervention
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Current Procedural Terminology • (96360 - 96549) hydration, therapeutic, prophylactic, diagnostic injections & infusions, and chemotherapy & other highly complex drug or highly complex biologic agent administration • (96567 - 96571) photodynamic therapy • (96900 - 96999) special dermatological procedures • (97001 - 97799) physical medicine & rehabilitation • (97802 - 97804) medical nutrition therapy • (97810 - 97814) acupuncture • (98925 - 98929) osteopathic manipulative treatment • (98940 - 98943) chiropractic manipulative treatment • (98960 - 98962) education & training for patient self-management • (98966 - 98969) non-face-to-face nonphysician services • (99000 - 99091) special services, procedures and reports • (99170 - 99199) other services & procedures • (99500 - 99602) home health procedures/services • (99605 - 99607) medication therapy management services
Category II • Category II CPT Code(s) – Performance Measurement (optional) (Category II codes: 0001F-7025F)
Category III • Category III CPT Code(s) – Emerging Technology (Category III codes: 0016T-0207T[3])
Major Psychotherapy Revisions The CPT code revisions that effect counselors are simple and straightforward. Here is a list of psychotherapy CPT codes that will be retired, and their 2013 comparables: 90801 –> 90791 (diagnostic evaluation without medical services) 90804 –> 90832 (was 20–30 minutes psychotherapy, now 30 minutes) 90806 –> 90834 (was 45–50 minutes psychotherapy, now 45 minutes) 90808 –> 90837 (was 75–80 minutes psychotherapy, now 60 minutes) Family therapy codes (90847 and 90846) will remain unchanged, as will codes for psychological testing.
Copyright CPT is a registered trademark of the American Medical Association. The AMA holds the copyright for the CPT coding system.[4] Despite the copyrighted nature of the CPT code sets, the use of the code is mandated by almost all health insurance payment and information systems, including the Centers for Medicare and Medicaid Services (CMS) and HIPAA, and the data for the code sets appears in the Federal Register. As a result, it is necessary for most users of the CPT code (principally providers of services) to pay license fees for access to the code.[5]
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Current Procedural Terminology
Limited CPT search offered by the AMA The AMA offers a limited search of the CPT manual for personal, non-commercial use on its web site.[6]
References [1] [2] [3] [4] [5] [6]
AMA (CPT) CPT Process (http:/ / www. ama-assn. org/ ama/ pub/ category/ 3112. html) Centers for Medicare & Medicaid Services (http:/ / www. cms. hhs. gov/ ) CPT 2010 AMA (CPT) CPT Licensing (http:/ / www. ama-assn. org/ ama/ pub/ category/ 3657. html) http:/ / www. ama-assn. org/ ama1/ pub/ upload/ mm/ 37/ 2009-annual-report. pdf AMA (2012). "cpt® Code/Relative Value Search". Retrieved from https:/ / ocm. ama-assn. org/ OCM/ CPTRelativeValueSearch. do.
External links • Official site (http://www.ama-assn.org/ama/pub/category/3113.html) by the AMA* Description of the three sections (http://www.ama-assn.org/ama/pub/category/12888.html) from the AMA • CPT® Process - How a Code Becomes a Code (http://www.ama-assn.org/ama/pub/physician-resources/ solutions-managing-your-practice/coding-billing-insurance/cpt/cpt-process-faq/code-becomes-cpt.page) from the AMA • Q&A (http://www.aafp.org/fpm/accessories/browse?op=get_documents_via_heading_id&heading_id=46) from the American Academy of Family Physicians
Diagnosis-related group Diagnosis-related group (DRG) is a system to classify hospital cases into one of originally 467 groups.[citation needed] The 467th group was "Ungroupable". This system of classification was developed as a collaborative project by Robert B Fetter, PhD, of the Yale School of Management, and John D Thompson, MPH, of the Yale School of Public Health.[1] The system is also referred to as "the DRGs", and its intent was to identify the "products" that a hospital provides. One example of a "product" is an appendectomy. The system was developed in anticipation of convincing Congress to use it for reimbursement, to replace "cost based" reimbursement that had been used up to that point. DRGs are assigned by a "grouper" program based on ICD (International Classification of Diseases) diagnoses, procedures, age, sex, discharge status, and the presence of complications or comorbidities. DRGs have been used in the US since 1982 to determine how much Medicare pays the hospital for each "product", since patients within each category are clinically similar and are expected to use the same level of hospital resources.[2] DRGs may be further grouped into Major Diagnostic Categories (MDCs). DRGs are also standard practice for establishing reimbursements for other Medicare related reimbursements such as to home healthcare providers. [citation needed]
Purpose The original objective of diagnosis related groups (DRG) was to develop a classification system that identified the "products" that the patient received. Since the introduction of DRGs in the early 1980s, the healthcare industry has evolved and developed an increased demand for a patient classification system that can serve its original objective at a higher level of sophistication and precision.[3] To meet those evolving needs, the objective of the DRG system had to expand in scope. Today, there are several different DRG systems that have been developed in the US. They include:[citation needed] • Medicare DRG (CMS-DRG & MS-DRG) • Refined DRGs (R-DRG) • All Patient DRGs (AP-DRG)
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Diagnosis-related group • • • •
Severity DRGs (S-DRG) All Patient, Severity-Adjusted DRGs (APS-DRG) All Patient Refined DRGs (APR-DRG) International-Refined DRGs (IR-DRG)
History The system was createdWikipedia:Manual of Style/Dates and numbers#Chronological itemsWikipedia:Please clarify by Robert Barclay Fetter and John D. Thompson at Yale University with the material support of the former Health Care Financing Administration (HCFA), now called the Centers for Medicare & Medicaid Services (CMS).[citation needed]
DRGs were first implemented in New Jersey, beginning in 1980 with a small number of hospitals partitioned into three groups according to their budget positions - surplus, breakeven, and deficit - prior to the imposition of DRG payment.[4] The New Jersey experiment continued for three years, with additional cadres of hospitals being added to the number of institutions each year until all hospitals in New Jersey were dealing with this prospective payment system.[citation needed]
DRGs were designed to be homogeneous units of hospital activity to which binding prices could be attached. A central theme in the advocacy of DRGs was that this reimbursement system would, by constraining the hospitals, oblige their administrators to alter the behavior of the physicians and surgeons comprising their medical staffs. Hospitals were forced to leave the “nearly risk-free world of cost reimbursement”[5] and face the uncertain financial consequences associated with the provision of health care.[6] Moreover, DRGs were designed to provide practice pattern information that administrators could use to influence individual physician behavior. DRGs were intended to describe all types of patients in an acute hospital setting. The DRGs encompassed elderly patients as well as newborn, pediatric and adult populations.[citation needed] The prospective payment system implemented as DRGs had been designed to limit the share of hospital revenues derived from the Medicare program budget, and in spite of doubtful results in New Jersey, it was decided in 1983 to impose DRGs on hospitals nationwide.[citation needed] In that year, HCFA assumed responsibility for the maintenance and modifications of these DRG definitions. Since that time, the focus of all Medicare DRG modifications instituted by HCFA/CMS has been on problems relating primarily to the elderly population.[citation needed] In 1987, New York state passed legislation instituting DRG-based payments for all non-Medicare patients. This legislation required that the New York State Department of Health (NYS DOH) evaluate the applicability of Medicare DRGs to a non-Medicare population. This evaluation concluded that the Medicare DRGs were not adequate for a non-Medicare population. Based on this evaluation, the NYS DOH entered into an agreement with 3M to research and develop all necessary DRG modifications. The modifications resulted in the initial APDRG, which differed from the Medicare DRG in that it provided support for transplants, high-risk obstetric care, nutritional disorders, and pediatrics along with support for other populations. One challenge in working with the APDRG groupers is that there is no set of common data/formulas that is shared across all states as there is with CMS. Each state maintains its own information.[citation needed] In 1991, the top 10 DRGs overall were: normal newborn, vaginal delivery, heart failure, psychoses, cesarean section, neonate with significant problems, angina pectoris, specific cerebrovascular disorders, pneumonia, and hip/knee replacement. These DRGs comprised nearly 30 percent of all hospital discharges. The history, design, and classification rules of the DRG system, as well as its application to patient discharge data and updating procedures, are presented in the CMS DRG Definitions Manual (Also known as the Medicare DRG
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Diagnosis-related group
508
Definitions Manual and the Grouper Manual). A new version generally appears every October. The 20.0 version appeared in 2002.[citation needed] In 2007, author Rick Mayes described DRGs as: ...the single most influential postwar innovation in medical financing: Medicare's prospective payment system (PPS). Inexorably rising medical inflation and deep economic deterioration forced policymakers in the late 1970s to pursue radical reform of Medicare to keep the program from insolvency. Congress and the Reagan administration eventually turned to the one alternative reimbursement system that analysts and academics had studied more than any other and had even tested with apparent success in New Jersey: prospective payment with diagnosis-related groups (DRGs). Rather than simply reimbursing hospitals whatever costs they charged to treat Medicare patients, the new model paid hospitals a predetermined, set rate based on the patient's diagnosis. The most significant change in health policy since Medicare and Medicaid's passage in 1965 went virtually unnoticed by the general public. Nevertheless, the change was nothing short of revolutionary. For the first time, the federal government gained the upper hand in its financial relationship with the hospital industry. Medicare's new prospective payment system with DRGs triggered a shift in the balance of political and economic power between the providers of medical care (hospitals and physicians) and those who paid for it - power that providers had successfully accumulated for more than half a century."
“
”
CMS DRG version 25 revision As of October 1, 2007, with version 25, the CMS DRG system resequenced the groups, so that for instance "Ungroupable" is no longer 470 but is now 999.[citation needed] To differentiate it, the newly resequenced DRG are now known as MS-DRG.[citation needed] Before the introduction of version 25, many CMS DRG classifications were "paired" to reflect the presence of complications or comorbidities (CCs). A significant refinement of version 25 was to replace this pairing, in many instances, with a trifurcated design that created a tiered system of the absence of CCs, the presence of CCs, and a higher level of presence of Major CCs. As a result of this change, the historical list of diagnoses that qualified for membership on the CC list was substantially redefined and replaced with a new standard CC list and a new Major CC list.[citation needed] Another planning refinement was not to number the DRGs in strict numerical sequence as compared with the prior versions. In the past, newly created DRG classifications would be added to the end of the list. In version 25, there are gaps within the numbering system that will allow modifications over time, and also allow for new MS-DRGs in the same body system to be located more closely together in the numerical sequence.[citation needed]
MS-DRG version 26 revision MS-DRG Grouper version 26 took effect as of October 1, 2008 with one main change: implementation of Hospital Acquired Conditions (HAC). Certain conditions are no longer considered complications if they were not present on admission (POA), which will cause reduced reimbursement from Medicare for conditions apparently caused by the hospital.[citation needed]
MS-DRG version 27 revision MS-DRG Grouper version 27 took effect as of October 1, 2009. Changes involved are mainly related to Influenza A virus subtype H1N1.[citation needed]
Diagnosis-related group
References [1] Fetter RB, Shin Y, Freeman JL, Averill RF, Thompson JD (1980) Case mix definition by diagnosis related groups. Medical Care 18(2):1–53 [2] Fetter RB, Freeman JL (1986) Diagnosis related groups: product linemanagement within hospitals. Academy of Management Review 11(1):41–54 [3] Baker JJ (2002) Medicare payment system for hospital inpatients: diagnosis related groups. Journal of Health Care Finance 28(3):1–13 [4] Lessons of the New Jersey DRG Payment System (http:/ / content. healthaffairs. org/ cgi/ reprint/ 5/ 2/ 32. pdf) [5] Eastaugh SR (1999) Managing risk in a risky world. Journal of Health Care Finance 25(3):10 [6] Kuntz L, Scholtes S, Vera A (2008) DRG Cost Weight Volatility and Hospital Performance. OR Spectrum 30(2): 331-354
External links • Official CMS website (http://cms.hhs.gov) • DRG codes for FY2005, also referred to as version 23 (http://www.cms.hhs.gov/ MedicareFeeforSvcPartsAB/Downloads/DRGDesc05.pdf) • DRG codes for FY2010, also referred to as version 27 (http://www.cms.hhs.gov/AcuteInpatientPPS/ downloads/FY_2010_FR_Table_5.zip) • Agency for Healthcare Research and Quality (AHRQ) (http://www.ahrq.gov). • DRG definition (http://www.ahrq.gov/data/hcup/94drga.htm). • Most Frequent Diagnoses and Procedures for DRGs (http://www.hcup-us.ahrq.gov/reports/natstats.jsp). • DRG and ICD (http://www.umanitoba.ca/centres/mchp/concept/dict/drg/DRG_overview.html) (Canada) from the MCHP research unit of the University of Manitoba's Faculty of Medicine. • Diagnosis Related Groups (DRGs) and the Medicare Program (http://govinfo.library.unt.edu/ota/Ota_4/ DATA/1983/8306.PDF) - Implications for Medical Technology (PDF format). A 1983 document found in the "CyberCemetery: OTA Legacy" section of University of North Texas Libraries Government Documents department. • Mayes, Rick, "The Origins, Development, and Passage of Medicare's Revolutionary Prospective Payment System" (http://muse.jhu.edu/login?uri=/journals/journal_of_the_history_of_medicine_and_allied_sciences/ v062/62.1mayes.html) Journal of the History of Medicine and Allied Sciences Volume 62, Number 1, January 2007, pp. 21-55
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Digital Imaging and Communications in Medicine
Digital Imaging and Communications in Medicine Digital Imaging and Communications in Medicine (DICOM) is a standard for handling, storing, printing, and transmitting information in medical imaging. It includes a file format definition and a network communications protocol. The communication protocol is an application protocol that uses TCP/IP to communicate between systems. DICOM files can be exchanged between two entities that are capable of receiving image and patient data in DICOM format. The National Electrical Manufacturers Association (NEMA) holds the copyright to this standard.[1] It was developed by the DICOM Standards Committee, whose members[2] are also partly members of NEMA.[3] DICOM enables the integration of scanners, servers, workstations, printers, and network hardware from multiple manufacturers into a picture archiving and communication system (PACS). The different devices come with DICOM conformance statements which clearly state which DICOM classes they support. DICOM has been widely adopted by hospitals and is making inroads in smaller applications like dentists' and doctors' offices. DICOM is known as NEMA standard PS3, and as ISO standard 12052:2006 "Health informatics -- Digital imaging and communication in medicine (DICOM) including workflow and data management".
Parts of the standard The DICOM standard is divided into related but independent parts: The links below are to the 2011 version. Additions to the standard (Supplements and Change Proposals) since that publication are available through the DICOM Web site [4]. • • • • • • • • • • • • • • • • • • • •
PS 3.1: Introduction and Overview [5]PDF(241KB) PS 3.2: Conformance [6]PDF(6.46MB) PS 3.3: Information Object Definitions [7]PDF(6.96MB) PS 3.4: Service Class Specifications [8]PDF(1.07MB) PS 3.5: Data Structure and Encoding [9]PDF(588KB) PS 3.6: Data Dictionary [10]PDF(7.32MB) PS 3.7: Message Exchange [11]PDF(1.97MB) PS 3.8: Network Communication Support for Message Exchange [12]PDF(901KB) PS 3.9: Retired (formerly Point-to-Point Communication Support for Message Exchange) PS 3.10: Media Storage and File Format for Media Interchange [13]PDF(406KB) PS 3.11: Media Storage Application Profiles [14]PDF(398KB) PS 3.12: Media Formats and Physical Media for Media Interchange [15]PDF(302KB) PS 3.13: Retired (formerly Print Management Point-to-Point Communication Support) PS 3.14: Grayscale Standard Display Function [16]PDF(478KB) PS 3.15: Security and System Management Profiles [17]PDF(753KB) PS 3.16: Content Mapping Resource [18]PDF(5.03MB) PS 3.17: Explanatory Information [19]PDF(11.40MB) PS 3.18: Web Access to DICOM Persistent Objects (WADO) [20]PDF(203KB) PS 3.19: Application Hosting [21]PDF(1.22MB) PS 3.20: Transformation of DICOM to and from HL7 Standards [22]PDF(570KB)
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History DICOM is the First version of a standard developed by American College of Radiology (ACR) and National Electrical Manufacturers Association (NEMA). In the beginning of the 1980s, it was very difficult for anyone other than manufacturers of computed tomography or magnetic resonance imaging devices to decode the images that the machines generated. Radiologists and medical physicists wanted to use the images for dose-planning for radiation therapy. ACR and NEMA joined forces and formed a standard committee in 1983. Their first standard, ACR/NEMA 300, was released in 1985. Very soon after its release, it became clear that improvements were needed. The text was vague and had internal contradictions. In 1988 the second version was released. This version gained more acceptance among vendors. The image transmission was specified as Front page of ACR/NEMA 300, version 1.0, over a dedicated 2 pair cable (EIA-485). The first demonstration of which was released in 1985 ACR/NEMA V2.0 interconnectivity technology was held at Georgetown University, May 21–23, 1990. Six companies participated in this event, DeJarnette Research Systems, General Electric Medical Systems, Merge Technologies, Siemens Medical Systems, Vortech (acquired by Kodak that same year) and 3M. Commercial equipment supporting ACR/NEMA 2.0 was presented at the annual meeting of the Radiological Society of North America (RSNA) in 1990 by these same vendors. Many soon realized that the second version also needed improvement. Several extensions to ACR/NEMA 2.0 were created, like Papyrus (developed by the University Hospital of Geneva, Switzerland) and SPI (Standard Product Interconnect), driven by Siemens Medical Systems and Philips Medical Systems. The first large-scale deployment of ACR/NEMA technology was made in 1992 by the US Army and Air Force, as part of the MDIS (Medical Diagnostic Imaging Support) [23] program run out of Ft. Detrick, Maryland. Loral Aerospace and Siemens Medical Systems led a consortium of companies in deploying the first US military PACS (Picture Archiving and Communications System) at all major Army and Air Force medical treatment facilities and teleradiology nodes at a large number of US military clinics. DeJarnette Research Systems and Merge Technologies provided the modality gateway interfaces from third party imaging modalities to the Siemens SPI network. The Veterans Administration and the Navy also purchased systems off this contract. In 1993 the third version of the standard was released. Its name was then changed to "DICOM" so as to improve the possibility of international acceptance as a standard. New service classes were defined, network support added and the Conformance Statement was introduced. Officially, the latest version of the standard is still 3.0. However, it has been constantly updated and extended since 1993. Instead of using the version number, the standard is often version-numbered using the release year, like "the 2007 version of DICOM". While the DICOM standard has achieved a near universal level of acceptance amongst medical imaging equipment vendors and healthcare IT organizations, the standard has its limitations. DICOM is a standard directed at addressing technical interoperability issues in medical imaging. It is not a framework or architecture for achieving a useful clinical workflow. RSNA's Integrating the Healthcare Enterprise (IHE) initiative layered on top of DICOM (and HL-7) provides this final piece of the medical imaging interoperability puzzle.
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Derivations There are some derivations from the DICOM standard into other application areas. These include: • DICONDE - Digital Imaging and Communication in Nondestructive Evaluation, was established in 2004 as a way for nondestructive testing manufacturers and users to share image data.[24] • DICOS - Digital Imaging and Communication in Security was established in 2009 to be used for image sharing in airport security.[25]
Data format DICOM differs from some, but not all, data formats in that it groups information into data sets. That means that a file of a chest x-ray image, for example, actually contains the patient ID within the file, so that the image can never be separated from this information by mistake. This is similar to the way that image formats such as JPEG can also have embedded tags to identify and otherwise describe the image. A DICOM data object consists of a number of attributes, including items such as name, ID, etc., and also one special attribute containing the image pixel data (i.e. logically, the main object has no "header" as such: merely a list of attributes, including the pixel data). A single DICOM object can have only one attribute containing pixel data. For many modalities, this corresponds to a single image. But note that the attribute may contain multiple "frames", allowing storage of cine loops or other multi-frame data. Another example is NM data, where an NM image, by definition, is a multi-dimensional multi-frame image. In these cases, three- or four-dimensional data can be encapsulated in a single DICOM object. Pixel data can be compressed using a variety of standards, including JPEG, JPEG Lossless, JPEG 2000, and Run-length encoding (RLE). LZW (zip) compression can be used for the whole data set (not just the pixel data), but this has rarely been implemented. DICOM uses three different Data Element encoding schemes. With Explicit Value Representation (VR) Data Elements, for VRs that are not OB, OW, OF, SQ, UT, or UN, the format for each Data Element is: GROUP (2 bytes) ELEMENT (2 bytes) VR (2 bytes) LengthInByte (2 bytes) Data (variable length). For the other Explicit Data Elements or Implicit Data Elements, see section 7.1 of Part 5 of the DICOM Standard. The same basic format is used for all applications, including network and file usage, but when written to a file, usually a true "header" (containing copies of a few key attributes and details of the application which wrote it) is added.
Image display To promote identical grayscale image display on different monitors and consistent hard-copy images from various printers, the DICOM committee developed a lookup table to display digitally assigned pixel values. To use the DICOM grayscale standard display function (GSDF),[26] images must be viewed (or printed) on devices that have this lookup curve or on devices that have been calibrated to the GSDF curve.6[27]
Value representations Extracted from Chapter 6.2 of • PS 3.5: Data Structure and Encoding [28]PDF(1.43MiB)
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Value Representation
Description
AE
Application Entity
AS
Age String
AT
Attribute Tag
CS
Code String
DA
Date
DS
Decimal String
DT
Date/Time
FL
Floating Point Single (4 bytes)
FD
Floating Point Double (8 bytes)
IS
Integer String
LO
Long String
LT
Long Text
OB
Other Byte
OF
Other Float
OW
Other Word
PN
Person Name
SH
Short String
SL
Signed Long
SQ
Sequence of Items
SS
Signed Short
ST
Short Text
TM
Time
UI
Unique Identifier
UL
Unsigned Long
UN
Unknown
US
Unsigned Short
UT
Unlimited Text
In addition to a Value Representation, each attribute also has a Value Multiplicity to indicate the number of data elements contained in the attribute. For character string value representations, if more than one data element is being encoded, the successive data elements are separated by the backslash character "\".
Digital Imaging and Communications in Medicine
Services DICOM consists of many different services, most of which involve transmission of data over a network, and the file format below is a later and relatively minor addition to the standard.
Store The DICOM Store service is used to send images or other persistent objects (structured reports, etc.) to a picture archiving and communication system (PACS) or workstation.
Storage commitment The DICOM storage commitment service is used to confirm that an image has been permanently stored by a device (either on redundant disks or on backup media, e.g. burnt to a CD). The Service Class User (SCU: similar to a client), a modality or workstation, etc., uses the confirmation from the Service Class Provider (SCP: similar to a server), an archive station for instance, to make sure that it is safe to delete the images locally.
Query/Retrieve This enables a workstation to find lists of images or other such objects and then retrieve them from a picture archiving and communication system.
Modality worklist This enables a piece of imaging equipment (a modality) to obtain details of patients and scheduled examinations electronically, avoiding the need to type such information multiple times (and the mistakes caused by retyping).
Modality performed procedure step A complementary service to Modality Worklist, this enables the modality to send a report about a performed examination including data about the images acquired, beginning time, end time, and duration of a study, dose delivered, etc. It helps give the radiology department a more precise handle on resource (acquisition station) use. Also known as MPPS, this service allows a modality to better coordinate with image storage servers by giving the server a list of objects to send before or while actually sending such objects.
Printing The DICOM Printing service is used to send images to a DICOM Printer, normally to print an "X-Ray" film. There is a standard calibration (defined in DICOM Part 14) to help ensure consistency between various display devices, including hard copy printout.
Off-line media (files) The off-line media files correspond to Part 10 of the DICOM standard. It describes how to store medical imaging information on removable media. Except for the data set containing, for example, an image and demography, it's also mandatory to include the File Meta Information. DICOM restricts the filenames on DICOM media to 8 characters (some systems wrongly use 8.3, but this does not conform to the standard). No information must be extracted from these names (PS3.10 Section 6.2.3.2). This is a common source of problems with media created by developers who did not read the specifications carefully. This is a historical requirement to maintain compatibility with older existing systems. It also mandates the presence of a media directory, the DICOMDIR file, which provides index and summary information for all the DICOM files on the media. The DICOMDIR information provides substantially greater information about each file than any filename could, so there is less need for meaningful file names.
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DICOM files typically have a .dcm file extension if they are not part of a DICOM media (which requires them to be without extension). The MIME type for DICOM files is defined by RFC 3240 as application/dicom. The Uniform Type Identifier type for DICOM files is org.nema.dicom. There is also an ongoing media exchange test and "connectathon" process for CD media and network operation that is organized by the IHE organization. MicroDicom is free Windows software for reading DICOM data.
Application areas Modality
Description
AS
Modality of type Angioscopy - Retired
BI
Modality of type Biomagnetic Imaging
CD
Modality of type Color Flow Doppler - Retired 2008
CF
Modality of type Cinefluorography - Retired
CP
Modality of type Colposcopy - Retired
CR
Modality of type Computed Radiography
CS
Modality of type Cystoscopy - Retired
CT
Modality of type Computed Tomography
DD
Modality of type Duplex Doppler - Retired 2008
DG
Modality of type Diaphanography
DM
Modality of type Digital Microscopy - Retired
DS
Modality of type Digital Subtraction Angiography - Retired
DX
Modality of type Digital Radiography
EC
Modality of type Echocardiography - Retired
ECG
Modality of type Electrocardiograms
EM
Modality of type Electron Microscope
ES
Modality of type Endoscopy
FA
Modality of type Fluorescein Angiography - Retired
FS
Modality of type Fundoscopy - Retired
GM
Modality of type General Microscopy
HC
Modality of type Hard Copy
LP
Modality of type Laparoscopy - Retired
LS
Modality of type Laser Surface Scan
MA
Modality of type Magnetic Resonance Angiography (retired)
MG
Modality of type Mammography
MR
Modality of type Magnetic Resonance
MS
Modality of type Magnetic Resonance Spectroscopy - Retired
NM
Modality of type Nuclear Medicine
OP
Modality of type Ophthalmic Photography
OPM
Modality of type Ophthalmic Mapping
Digital Imaging and Communications in Medicine
OPR
Modality of type Ophthalmic Refraction
OPV
Modality of type Ophthalmic Visual Field
OT
Modality of type Other
PT
Modality of type Positron Emission Tomography (PET)
PX
Modality of type Panoramic X-Ray
RD
Modality of type Radiotherapy Dose (a.k.a. RTDOSE)
RF
Modality of type Radio Fluoroscopy
RG
Modality of type Radiographic Imaging (conventional film screen)
RTIMAG Modality of type Radiotherapy Image RP
Modality of type Radiotherapy Plan (a.k.a. RTPLAN)
RS
Modality of type Radiotherapy Structure Set (a.k.a. RTSTRUCT)
RT
Modality of type Radiation Therapy
SC
Modality of type Secondary Capture
SM
Modality of type Slide Microscopy
SR
Modality of type Structured Reporting
ST
Modality of type Single-Photon Emission Computed Tomography (retired 2008)
TG
Modality of type Thermography
US
Modality of type Ultrasound
VF
Modality of type Videofluorography - Retired
VL
Modality of type Visible Light
XA
Modality of type X-Ray Angiography
XC
Modality of type External Camera (Photography)
Port numbers over IP DICOM have reserved the following TCP and UDP port numbers by the Internet Assigned Numbers Authority (IANA): • 104 well-known port for DICOM over Transmission Control Protocol (TCP) or User Datagram Protocol (UDP). Since 104 is in the reserved subset, many operating systems require special privileges to use it. • 2761 registered port for DICOM using Integrated Secure Communication Layer (ISCL) over TCP or UDP • 2762 registered port for DICOM using Transport Layer Security (TLS) over TCP or UDP • 11112 registered port for DICOM using standard, open communication over TCP or UDP The standard recommends but does not require the use of these port numbers.
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Disadvantages According to a paper presented at an international symposium in 2008, the DICOM standard has problems related to data entry. "A major disadvantage of the DICOM Standard is the possibility for entering probably too many optional fields. This disadvantage is mostly showing in inconsistency of filling all the fields with the data . Some image objects are often incomplete because some fields are left blank and some are filled with incorrect data."
HL7 DICOM is a standard for handling, storing, printing, and transmitting information in medical imaging. The communication protocol is an application protocol that uses TCP/IP to communicate between systems. DICOM files can be exchanged between two entities that are capable of receiving image and patient data in DICOM format. The National Electrical Manufacturers Association (NEMA) holds the copyright to this standard. It was developed by the DICOM Standards Committee, whose members are also partly members of NEMA.[29] Health Level Seven (HL7), is a non-profit organization involved in the development of international healthcare informatics interoperability standards.[1] "HL7" also refers to some of the specific standards created by the organization (e.g., HL7 v2.x, v3.0, HL7 RIM). The HL7 Strategic Initiatives document is a business plan for our products and services and was designed specifically to meet the business needs of our members and stakeholders. Derived from collaborative efforts with our members, government and non-government agencies and other standards development organizations, the Strategic Initiatives are five high-level organizational strategies that are supported by a detailed tactical plan with clearly defined objectives, milestones, and metrics for success.[30] Both of the standards are focused on the data exchange and the data compatibility. Among many standards for the syntax, HL7 and DICOM are most successful. However, everything could not be handled by HL7 solely. DICOM is good for radiology images, but, other clinical images are already handled by other ‘lighter’ data formats like JPEG, TIFF. So, it is not realistic to use only one standard for every area of clinical information. Opening the HL7 and DICOM standards in order to foster the integrated use of persistent health information objects is proposed as a step towards the creation of the health information infrastructure.
IHE Integrating the Healthcare Enterprise (IHE) was founded in 1997 by members of the Radiological Society of North America (RSNA) and the Healthcare Information and Management Systems Society for the purpose of improving interoperability between information systems. The IHE initiative was charged with the task of using existing standards of health care data communication such as DICOM and HL7 to improve exchange of medical information beyond the radiology department at the hospital level or health systems level. Just as radiologists were confronted in the past with imaging connectivity incompatibilities, entire health systems are continually faced with the task of connecting multiple disparate information systems in which the only reliable communications pathway is the paper printout. The IHE working group is a panel made up of industry representatives from medical informatics and imaging vendors as well as medical professionals. Their primary focus is to develop a common information model of medical information exchange. The devised IHE technical framework consists of a common lexicon that defines specific medical information transactions using the existing standards of medical information exchange (DICOM and HL7). The specifics of these transactions have been worked out in great detail so that vendors have been free to independently develop solutions to meet the goals of the technical framework. In the year 2001 to 2002, 30 companies took part in the testing and implementation of the IHE demonstrations.[31]
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References [1] DICOM brochure (http:/ / medical. nema. org/ dicom/ geninfo/ Brochure. pdf), nema.org. [2] MEMBERS of the DICOM STANDARDS COMMITTEE (http:/ / medical. nema. org/ members. pdf) [3] http:/ / www. nema. org/ About/ Pages/ Members. aspx [4] http:/ / dicom. nema. org [5] http:/ / medical. nema. org/ Dicom/ 2011/ 11_01pu. pdf [6] http:/ / medical. nema. org/ Dicom/ 2011/ 11_02pu. pdf [7] http:/ / medical. nema. org/ Dicom/ 2011/ 11_03pu. pdf [8] http:/ / medical. nema. org/ Dicom/ 2011/ 11_04pu. pdf [9] http:/ / medical. nema. org/ Dicom/ 2011/ 11_05pu. pdf [10] http:/ / medical. nema. org/ Dicom/ 2011/ 11_06pu. pdf [11] http:/ / medical. nema. org/ Dicom/ 2011/ 11_07pu. pdf [12] http:/ / medical. nema. org/ Dicom/ 2011/ 11_08pu. pdf [13] http:/ / medical. nema. org/ Dicom/ 2011/ 11_10pu. pdf [14] http:/ / medical. nema. org/ Dicom/ 2011/ 11_11pu. pdf [15] http:/ / medical. nema. org/ Dicom/ 2011/ 11_12pu. pdf [16] http:/ / medical. nema. org/ Dicom/ 2011/ 11_14pu. pdf [17] http:/ / medical. nema. org/ Dicom/ 2011/ 11_15pu. pdf [18] http:/ / medical. nema. org/ Dicom/ 2011/ 11_16pu. pdf [19] http:/ / medical. nema. org/ Dicom/ 2011/ 11_17pu. pdf [20] http:/ / medical. nema. org/ Dicom/ 2011/ 11_18pu. pdf [21] http:/ / medical. nema. org/ Dicom/ 2011/ 11_19pu. pdf [22] http:/ / medical. nema. org/ Dicom/ 2011/ 11_20pu. pdf [23] http:/ / www. ncbi. nlm. nih. gov/ pubmed/ 7612705?dopt=Abstract [24] http:/ / www. astm. org: If a Picture Is Worth 1,000 Words, then Pervasive, Ubiquitous Imaging Is Priceless (http:/ / www. astm. org/ SNEWS/ OCTOBER_2003/ voelker_oct03. html) [25] http:/ / www. nema. org: Industrial Imaging and Communications Section (http:/ / www. nema. org/ prod/ security/ indust-Img. cfm) [26] http:/ / medical. nema. org/ Dicom/ 2011/ 11_14pu. pdf [27] Shiroma, J. T. (2006). An introduction to DICOM. Veterinary Medicine, , 19-20. Retrieved from http:/ / 0-search. proquest. com. alpha2. latrobe. edu. au/ docview/ 195482647?accountid=12001 [28] http:/ / medical. nema. org/ dicom/ 2007/ 07_05pu. pdf [29] http:/ / www. dicombuzz. blogspot. in/ p/ dicom. html [30] http:/ / www. hl7. org/ about/ index. cfm [31] Flanders, A.E., Carrino, J.A., 2003. Understanding DICOM and IHE. Seminars in Roentgenology 38, 270–281.
External links • DICOM standard (http://dabsoft.ch/dicom/index.html), Dabsoft.ch. • The latest DICOM specification (http://medical.nema.org/standard.html) • DICOM Standard Status (approved and proposed changes) (http://www.dclunie.com/dicom-status/status. html) • Brief introduction to DICOM (http://www.cabiatl.com/mricro/dicom/index.html) • Introduction to DICOM using OsiriX (http://www.saravanansubramanian.com/Saravanan/ Articles_On_Software/Entries/2010/2/10_Introduction_to_the_DICOM_Standard.html) • Introduction to DICOM using RZDCX (http://dicomiseasy.blogspot.co.il/p/introduction-to-dicom.html) • Medical Image FAQ part 2 (http://www.dclunie.com/medical-image-faq/html/part2.html) - Standard formats including DICOM. • Medical Image FAQ part 8 (http://www.dclunie.com/medical-image-faq/html/part8.html) - Contains a long list DICOM software. • Collection of DICOM images (clinical images and technical testpatterns) (http://www.aycan.de/main/lp/ sample_dicom_images.html) • Example of an applet based DICOM Viewer (http://legeneraliste.perso.sfr.fr/?p=dicom_eng) • DICOM is a standard (http://www.dicombuzz.blogspot.in/p/dicom.html) - For Medical Images handling, storing, printing, and transmitting information
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DOCLE DOCLE (Doctor Command Language), is a non-numeric health coding and medical classification system. The Docle system is used in Health Communication Network's electronic medical record and patient management software package, Medical Director. Medical Director is the most widely used electronic medical record system by Australian primary health care providers. DOCLE has been modelled on the Linnaean biological classification system since 1995. Docle generates clinical codes from ubiquitous health language using an algorithm, hence it is a human readable clinical coding system. The design principles of Docle, as enumerated by the author in the www.docle.com [1] website include: • • • • •
Docle codes being meaningful and intentional Docle codes are derived from ubiquitous health language Docle codes grew with evolving order and speciation of large scale structures in a linnean manner. Docle codes are designed to strap together and form clinical structures using joiner codes The author of Docle, Dr. Y Kuang Oon, has likened clinical codes to "neuron's" and joiner codes as the "glia"
References • "Docle coding and classification system browser" [2] (HTML). Retrieved 2008-03-29. • "Medical Director - Product Details" [3] (HTML). Health Communication Network. Retrieved 2008-04-04. • "Docle Systems" [4] (HTML). Retrieved 2008-07-02.
External links • Docle Systems Web Site [4]
References [1] [2] [3] [4]
http:/ / www. docle. com http:/ / www. docle. com. au/ publ/ Hic99. htm http:/ / www. hcn. net. au/ products/ md/ md. asp http:/ / www. docle. com. au/
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Electronic Common Technical Document
Electronic Common Technical Document The electronic Common Technical Document (eCTD) is an interface for the pharmaceutical industry to agency transfer of regulatory information. The content is based on the Common Technical Document (CTD) format. It was developed by the International Conference on Harmonisation (ICH) Multidisciplinary Group 2 Expert Working Group (ICH M2 EWG). As of January 1, 2008, the U.S. Food and Drug Administration announced that the eCTD is the preferred format for electronic submissions. To date, over 98,000 eCTD sequences have been submitted to the FDA. Although the agency has not released an expected target date, the FDA revealed during the 2009 DIA Annual Meeting that it is looking at draft legislation to require eCTD.
Pharmaceutical point of view The eCTD has five modules: 1. Administrative Information and Prescribing Information 2. Common Technical Document Summaries 3. Quality 4. Nonclinical Study Reports 5. Clinical Study Reports A full table of contents quite large.
[1]
could be
There are two categories of modules: • Regional module: 1 (different for each region; i.e., country) • Common modules: 2-5 (common to all the regions) The CTD defines the content only of the common modules. The contents of the Regional Module 1 are defined by each of the ICH regions (USA, Europe and Japan).
IT point of view eCTD (data structure) The eCTD is a message specification for the transfer of files and metadata from a submitter to a receiver. The primary technical components are: • A high level folder structure (required) • An XML "backbone" file which provides metadata about content files and lifecycle instructions for the receiving system • An optional lower level folder structure (recommended folder names are provided in Appendix 4 of the eCTD specification) • Associated Document Type Definitions (DTDs) and stylesheets. Each submission message constitutes one "sequence". A cumulative eCTD consists of one or more sequences. While a single sequence may be viewed with web browser and the ICH stylesheet provided, viewing a cumulative eCTD requires specialized eCTD viewers. The top part of the directory structure is as follows:
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Electronic Common Technical Document ctd-123456/0000/index.xml ctd-123456/0000/index-md5.txt ctd-123456/0000/m1 ctd-123456/0000/m2 ctd-123456/0000/m3 ctd-123456/0000/m4 ctd-123456/0000/m5 ctd-123456/0000/util The string ctd-123456/0000 is just an example. Backbone (header) This is the file index.xml in the submission sequence number folder. For example: ctd-123456/0000/index.xml The purpose of this file is twofold: • Manage meta-data for the entire submission • Constitute a comprehensive table of contents and provide corresponding navigation aid. Stylesheets Stylesheets that support the presentation and navigation should be included. They must be placed in the directory: ctd-123456/0000/util/style See entry 377 in Appendix 4. DTDs DTDs must be placed in the directory: ctd-123456/0000/util/dtd See entry 371 to 376 in Appendix 4. They must follow a naming convention. The DTD of the backbone is in Appendix 8. It must be placed in the above directory.
Business process (protocol) The business process to be supported can be described as follows: Industry Message Agency The lifecycle management is composed at least of: • Initial submission: should be self-contained. • Incremental updates: with its sequence number.
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References [1] http:/ / www. fda. gov/ downloads/ Drugs/ DevelopmentApprovalProcess/ FormsSubmissionRequirements/ ElectronicSubmissions/ UCM163175. pdf
External links • • • •
AquilaSolutions.us - eCTD Support and Freeware eCTD Viewer (http://www.AquilaSolutions.us/) eCTDBlog.com (http://www.ectdblog.com/) eCTDOffice.com (http://www.ectdoffice.com/) Electronic Common Technical Document (eCTD) (http://www.fda.gov/Drugs/DevelopmentApprovalProcess/ FormsSubmissionRequirements/ElectronicSubmissions/ucm153574.htm) (FDA) • EUDRALEX Volume 2 - Pharmaceutical Legislation : Notice to Applicants (http://ec.europa.eu/enterprise/ pharmaceuticals/eudralex/homev2.htm) (EU legislation, contains section on eCTD) • Exalon eCTD News - free and strictly neutral eCTD related news from regulators (http://www.exalon.com/ ectd-news.html) • EXTEDO.com - free eCTD validator EURSvalidator used by EMA and many other regulatory authorities (http:// www.extedo.com/) • ICH eCTD Specification V 3.2.2 (http://estri.ich.org/eCTD/eCTD_Specification_v3_2_2.pdf) (The specification) • ICH M2 ESTRI Main (http://estri.ich.org) • IT Pharma Validation Europe (Organization: CSV Validation Network) (http://www.it-validation.eu) • Liquent.com (http://www.Liquent.com/) • LORENZ.cc (http://www.Lorenz.cc/) • nPeopleSoftware.com - eCTD Software & Service Provider (http://www.npeoplesoftware.com/)
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EUDRANET
EUDRANET EUDRANET, the European Telecommunication Network in Pharmaceuticals (European Union Drug Regulating Authorities Network), is an IT platform to facilitate the exchange of information between regulatory partners and industry during submission and evaluation of applications. The aim of EUDRANET is to provide appropriate secure services for inter-Administration data interchange and for exchanges between Administrations and industry. EUDRANET is based on the TESTA backbone infrastructure provided by the IDA Programme. The processes which EUDRANET supports include: • The submission and evaluation of marketing authorisation applications by pharmaceutical companies; • The pharmacovigilance of products on the market to ensure the maintenance of high standards of quality as well as adhering to European national and regional regulations; • The dissemination of relevant information to industry, scientific experts and regulators.
External links • EUDRANET [1] • Projects of Common Interest for Administrations [2] (European Union) • e-Health [3] (European Union)
References [1] http:/ / ec. europa. eu/ idabc/ en/ document/ 2291 [2] http:/ / ec. europa. eu/ idabc/ en/ chapter/ 565 [3] http:/ / ec. europa. eu/ information_society/ eeurope/ 2005/ all_about/ ehealth/ index_en. htm
General Data Format for Biomedical Signals The General Data Format for Biomedical Signals is a scientific and medical data file format. The aim of GDF is to combine and integrate the best features of all biosignal file formats [1] into a single file format. GDF v1 uses a binary header, and uses an event table. GDF v2 [2] added fields for additional subject-specific information (gender, age, etc.), and utilizes several standard codes (for storing physical units and other properties). GDF is used mostly in brain–computer interface research; however, GDF provides a superset of features from many different file formats, it could be also used for many other domains. The free and open source software library BioSig [3] provides implementations for reading and writing of GDF in Octave/Matlab and C/C++. A lightweight C++ library called libGDF [4] is also available and implements version 2 of the GDF format. The binary nature of the meta-information might not be suitable for all applications. Therefore, a new format called XDF [5] was developed with the aim to provide a flexible and extensible format for all kinds of data streams, but in particular for biosignals.
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External links • • • •
BioSig [3] libGDF [4] GDF v2.0 [2] XDF [5]
References [1] [2] [3] [4] [5]
http:/ / pub. ist. ac. at/ ~schloegl/ matlab/ eeg/ http:/ / arxiv. org/ abs/ cs. DB/ 0608052 http:/ / biosig. sf. net/ http:/ / sourceforge. net/ projects/ libgdf/ http:/ / code. google. com/ p/ xdf/
Health Level 7 Health Level Seven (HL7), is a non-profit organization involved in the development of international healthcare informatics interoperability standards. "HL7" also refers to some of the specific standards created by the organization (e.g., HL7 v2.x, v3.0, HL7 RIM). HL7 and its members provide a framework (and related standards) for the exchange, integration, sharing, and retrieval of electronic health information. The 2.x versions of the standards, which support clinical practice and the management, delivery, and evaluation of health services, are the most commonly used in the world.
Organization HL7 is an international community of healthcare subject matter experts and information scientists collaborating to create standards for the exchange, management and integration of electronic healthcare information. HL7 promotes the use of such informatics standards within and among healthcare organizations to increase the effectiveness and efficiency of healthcare information delivery for the benefit of all.[citation needed] The HL7 community is organized in the form of a global organization (Health Level Seven, Inc.) and country-specific affiliate organizations: • Health Level Seven, Inc. (HL7, Inc. ) is headquartered in Ann Arbor, Michigan. • HL7 affiliate organizations, not-for-profit organizations incorporated in local jurisdictions, exist in over 40 countries. The first affiliate organization was created in Germany in 1993.[citation needed] The organizational structure of HL7 Inc. is as follows:[citation needed] • The organization is managed by a Board of Directors, which comprises 10 elected positions and three appointed positions. • The Chief Executive Officer (currently Charles Jaffe, MD, PhD) serves as an ex officio member of and reports to the Board of Directors. The Chief Technology Officer (currently John Quinn); and the Chief Operations Officer (currently Mark McDougall) report to the CEO and also serve as ex officio members on the Board of Directors. • Members of HL7 are known collectively as "The Working Group". The Working Group is responsible for defining the HL7 standard protocol and is composed of Standing Administrative Committees and Working Groups. • Standing Administrative committees focus on organizational or promotional activities, such as Education, Implementation, Marketing, Outreach Committee for Clinical Research, Publishing and Process Improvement and Tooling.
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Health Level 7 • Working groups are directly responsible for the content of the Standards, framing the actual language of the specifications.
Origin HL7 was founded in 1987 to produce a standard for hospital information systems. HL7, Inc. is a standards organization that was accredited in 1994 by the American National Standards Institute (ANSI).[1] HL7 is one of several American National Standards Institute accredited Standards Developing Organizations (SDOs) operating in the healthcare arena. Most of these SDOs produce standards (sometimes called specifications or protocols) for a particular healthcare domain such as pharmacy, medical devices, imaging or insurance (claims processing) transactions. Health Level Seven’s domain is clinical and administrative data. Today, HL7 has been adopted by several national SDOs outside the United States. Those SDOs are consequently not accredited by ANSI. However, HL7 is now adopted by ISO as a centre of gravity in international standardization and accredited as a partnering organization for mutual issuing of standards. The first mutually published standard is ISO/HL7 21731:2006 Health informatics—HL7 version 3—Reference information model—Release 1. The name "Health Level-7" The name "Health Level-7" is a reference to the seventh layer of the ISO OSI Reference model also known as the application layer. The name indicates that HL7 focuses on application layer protocols for the health care domain, independent of lower layers. HL7 effectively considers all lower layers merely as tools.
Collaboration HL7 collaborates with other standards development organizations and national and international sanctioning bodies (e.g. ANSI and ISO), in both the healthcare and information infrastructure domains to promote the use of supportive and compatible standards. HL7 collaborates with healthcare information technology users to ensure that HL7 standards meet real-world requirements, and that appropriate standards development efforts are initiated by HL7 to meet emergent requirements.[citation needed] About 45% of the global membership (of either HL7 Inc. or an HL7 affiliate) is located in Europe, 35% in North America, 15% in Asia-Oceania and 5% elsewhere.
HL7 standards HL7 encompasses the complete life cycle of a standards specification including the development, adoption, market recognition, utilization, and adherence.[citation needed] Prior to April 2013, HL7 International asserted that business use of the HL7 standards required a paid organizational membership in HL7, Inc. HL7 Members could access standards for free, and non-members could buy the standards from HL7, ANSI, or for some standards, ISO. Most HL7 standards can now be deemed Open Standards, as since April 2013 they are available for free download. On Sept 4, 2012, the HL7 Board of Directors had announced its intention to relax the HL7 IP policy and allow free access and implementation to promote adoption and interoperability, as described in their press release. The revenue model and business plan of HL7 is discussed in HL7's Strategic Initiatives and Implementation Proposal. However, since the earlier policy as described in the Bylaws of October 2002 placed the HL7 protocol specifications in the Public Domain, and under 17 USC § 102 there is no copyright protection for an "idea, procedure, process, system, method of operation, concept, principle, or discovery", this revised policy may not be enforceable. Hospitals and other healthcare provider organizations typically have many different computer systems used for everything from billing records to patient tracking. All of these systems should communicate with each other (or
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Health Level 7 "interface") when they receive new information but not all do so. HL7 specifies a number of flexible standards, guidelines, and methodologies by which various healthcare systems can communicate with each other. Such guidelines or data standards are a set of rules that allow information to be shared and processed in a uniform and consistent manner. These data standards are meant to allow healthcare organizations to easily share clinical information. Theoretically, this ability to exchange information should help to minimize the tendency for medical care to be geographically isolated and highly variable.[citation needed] HL7 develops conceptual standards (e.g., HL7 RIM), document standards (e.g., HL7 CDA), application standards (e.g., HL7 CCOW), and messaging standards (e.g., HL7 v2.x and v3.0). Messaging standards are particularly important because they define how information is packaged and communicated from one party to another. Such standards set the language, structure and data types required for seamless integration from one system to another. The Reference Information Model (RIM) and the HL7 Development Framework (HDF) are the basis of the HL7 Version 3 standards development process. RIM is the representation of the HL7 clinical data (domains) and the life cycle of messages or groups of messages. HDF is a project to specify the processes and methodology used by all the HL7 committees for project initiation, requirements analysis, standard design, implementation, standard approval process, etc. HL7 standards: • Version 2.x Messaging Standard – an interoperability specification for health and medical transactions • Version 3 Messaging Standard – an interoperability specification for health and medical transactions, based on RIM • Version 3 Rules/GELLO – a standard expression language used for clinical decision support • Arden Syntax – a grammar for representing medical conditions and recommendations as a Medical Logic Module (MLM) • Clinical Context Object Workgroup (CCOW) – an interoperability specification for the visual integration of user applications • Claims Attachments – a Standard Healthcare Attachment to augment another healthcare transaction • Clinical Document Architecture (CDA) – an exchange model for clinical documents, based on HL7 Version 3 • Electronic Health Record (EHR) / Personal Health Record (PHR) – in support of these records, a standardized description of health and medical functions sought for or available • Structured Product Labeling (SPL) – the published information that accompanies a medicine, based on HL7 Version 3
HL7 version 2.x The HL7 version 2 standard (also known as Pipehat) has the aim to support hospital workflows. It was originally created in 1989. V2.x Messaging HL7 version 2 defines a series of electronic messages to support administrative, logistical, financial as well as clinical processes. Since 1987 the standard has been updated regularly, resulting in versions 2.1, 2.2, 2.3, 2.3.1, 2.4, 2.5, 2.5.1 and 2.6. The v2.x standards are backward compatible (e.g., a message based on version 2.3 will be understood by an application that supports version 2.6). HL7 v2.x messages use a human-readable (ASCII), non-XML encoding syntax based on segments (lines) and one-character delimiters. Segments have composites (fields) separated by the composite delimiter. A composite can have sub-composites (subcomponents) separated by the sub-composite delimiter, and sub-composites can have sub-sub-composites (subcomponents) separated by the sub-sub-composite delimiter. The default delimiters are vertical bar or pipe (|) for the field separator, caret (^) for the component separator, and ampersand (&) for the subcomponent separator. The tilde (~) is the default repetition separator. The first field (composite) in each segment contains the 3-character segment name. Each segment of the message contains one specific category of information.
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Every message has MSH as its first segment, which includes a field that identifies the message type. The message type determines the expected segment names in the message. The segment names for a particular message type are specified by the segment grammar notation used in the HL7 standards. The following is an example of an admission record. MSH is the header record, PID the Patient Identity, etc. The 5th field for the PID record is the patient's name.
MSH|^~\&|MegaReg|XYZHospC|SuperOE|XYZImgCtr|20060529090131-0500||ADT^A01^ADT_A01|01052901 EVN||200605290901||||200605290900 PID|||56782445^^^UAReg^PI||KLEINSAMPLE^BARRY^Q^JR||19620910|M||2028-9^^HL70005^RA99113^^X GOODWIN CREST DRIVE^^BIRMINGHAM^AL^35 209^^M~NICKELL’S PICKLES^10000 W 100TH AVE^BIRMINGHAM^AL^35200^^O |||||||0105I30001^^^99DEF^AN PV1||I|W^389^1^UABH^^^^3||||12345^MORGAN^REX^J^^^MD^0010^UAMC^L||678 90^GRAINGER^LUCY^X^^^MD^0010^UAMC^L|MED|||||A0||13579^POTTER^SHER MAN^T^^^MD^0010^UAMC^L|||||||||||||||||||||||||||200605290900 OBX|1|NM|^Body Height||1.80|m^Meter^ISO+|||||F OBX|2|NM|^Body Weight||79|kg^Kilogram^ISO+|||||F AL1|1||^ASPIRIN DG1|1||786.50^CHEST PAIN, UNSPECIFIED^I9|||A HL7 v2.x has allowed for the interoperability between electronic Patient Administration Systems (PAS), Electronic Practice Management (EPM) systems, Laboratory Information Systems (LIS), Dietary, Pharmacy and Billing systems as well as Electronic Medical Record (EMR) or Electronic Health Record (EHR) systems. Currently, HL7’s v2.x messaging standard is supported by every major medical information systems vendor in the United States.
HL7 version 3 The HL7 version 3 standard has the aim to support all healthcare workflows. Development of version 3 started around 1995, resulting in an initial standard publication in 2005. The v3 standard, as opposed to version 2, is based on a formal methodology (the HDF) and object-oriented principles. RIM - ISO/HL7 21731 The Reference Information Model (RIM) is the cornerstone of the HL7 Version 3 development process and an essential part of the HL7 V3 development methodology. RIM expresses the data content needed in a specific clinical or administrative context and provides an explicit representation of the semantic and lexical connections that exist between the information carried in the fields of HL7 messages. The RIM is essential to increase precision and reduce implementation costs. Models are available. HL7 Development Framework - ISO/HL7 27931 The HL7 Version 3 Development Framework (HDF) is a continuously evolving process that seeks to develop specifications that facilitate interoperability between healthcare systems. The HL7 RIM, vocabulary specifications, and model-driven process of analysis and design combine to make HL7 Version 3 one methodology for development of consensus-based standards for healthcare information system interoperability. The HDF is the most current edition of the HL7 V3 development methodology. The HDF not only documents messaging, but also the processes, tools, actors, rules, and artifacts relevant to development of all HL7 standard specifications. Eventually, the HDF will encompass all of the HL7 standard specifications, including any new standards resulting from analysis of electronic health record architectures and requirements. HL7 specifications draw upon codes and vocabularies from a variety of sources. The V3 vocabulary work ensures that the systems implementing HL7 specifications have an unambiguous understanding of the code sources and code value domains they are using.
Health Level 7 V3 Messaging The HL7 version 3 messaging standard defines a series of electronic messages (called interactions) to support all healthcare workflows. HL7 v3 messages are based on an XML encoding syntax. 'COMMENT: Please insert sample HL7 version 3 Data Markup Example Here: '
Clinical Document Architecture - ISO/HL7 27932 The HL7 Clinical Document Architecture (CDA) is an XML-based markup standard intended to specify the encoding, structure and semantics of clinical documents for exchange.
Methods applied by HL7 Services Aware Interoperability Framework The HL7 Services-Aware Enterprise Architecture Framework (SAIF) provides consistency between all HL7 artifacts, and enables a standardized approach to Enterprise Architecture (EA) development and implementation, and a way to measure the consistency. SAIF is a way of thinking about producing specifications that explicitly describe the governance, conformance, compliance, and behavioral semantics that are needed to achieve computable semantic working interoperability. The intended information transmission technology might use a messaging, document exchange, or services approach. SAIF is the framework that is required to rationalize interoperability of other standards. SAIF is an architecture for achieving interoperability, but it is not a whole-solution design for enterprise architecture management.
Arden syntax The Arden syntax is a language for encoding medical knowledge. HL7 adopted and oversees the standard beginning with Arden syntax 2.0. These Medical Logic Modules (MLMs) are used in the clinical setting as they can contain sufficient knowledge to make single medical decisions.[citation needed] They can produce alerts, diagnoses, and interpretations along with quality assurance function and administrative support. An MLM must run on a computer that meets the minimum system requirements and has the correct program installed. Then, the MLM can give advice for when and where it is needed.
MLLP A large portion of HL7 messaging is transported by Minimal Lower Layer Protocol (MLLP), also known as Lower Layer Protocol (LLP). For transmitting via TCP/IP, header and trailer characters are added to the message to identify the beginning and ending of the message because TCP/IP is a continuous stream of bytes. Hybrid Lower Layer Protocol (HLLP) is a variation of MLLP that includes a checksum to help verify message integrity. Amongst other software vendors, MLLP is supported by Microsoft and Oracle.
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CCOW CCOW, or "Clinical Context Object Workgroup," is a standard protocol designed to enable disparate applications to share user context and patient context in real-time, and at the user-interface level. CCOW implementations typically require a CCOW vault system to manage user security between applications.
Functional EHR and PHR specifications Functional specifications for an electronic health record.
Country specific aspects Australia HL7 Australia was established in 1998. Australia was an early adopter of the HL7 V2.x standards, which are now used ubiquitously in Australian public and private healthcare organisations. The localisation of the HL7 standards is undertaken in cooperation with the national standards body, Standards Australia. HL7 Australia closely cooperates with the National E-Health Transition Authority (NEHTA). • HL7 Australia • Standards Australia (IT-014 "Health Informatics") • National E-Health Transition Authority (NEHTA) Australia has been selected to host the International HL7 Interoperability Conference in 2013. This will be a two day event in Sydney from 28 – 30 October 2013. Proposed themes for the event include: • HL7/FHIR (Fast Healthcare Interoperability Resources), • CDA implementation and innovation, • Leveraging HL7v2.x investment, and • Emerging developments: CIMI, clinical terminology & semantic web
Canada HL7 within Canada is supported by the HL7 Canadian Constituency, which is hosted by the Canadian Health Infoway Standards Collaborative. Among its support for domestic and international activities are: • • • • • • •
Liaising with HL7 International; Supporting communication and correspondence domestically and international for specific HL7 items; Responding to inquiries from HL7 International; Planning and execution of HL7 Canada Constituency roles and responsibilities; Supporting domestic and international HL7 Canada meetings; Providing support to domestic input on international HL7 ballot and comment items; and Administering the HL7 Canada Constituency forum to facilitate member collaboration.
Notable HL7 implementations currently include Prince Edward Island's drug information system (DIS).
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Czech republic Czech republic has a national HL7 organization since 2001. In addition to HL7 standards, EHR systems in the Czech republic use a national interface (called DASTA) endorsed by the Ministry of Health (Czech Republic)
Finland National Health Archive http://www.kanta.fi/en The Social Insurance Institution of Finland, together with other health and welfare bodies in Finland have been rolling out a national health care database based on HL7 CDA R2 and V3 messages. The system is known by the acronym KanTa which is an abbreviation of KANsallinen TerveysArkisto (National Health Archive). The word KANTA in Finnish means "base", "stem" or "heel". Currently (as at February 2013) the system holds details of all electronic prescriptions and all pharmacies in Finland are connected to the database. The system enables prescriptions to be written by any medically qualified doctor into the system and enables the patient to collect prescriptions from any pharmacy nationwide. Citizens can log into the system via the internet using an authentication service and see their own record. There is a choice of using a government controlled authorization system or using the same authentication service used for on-line banking. These services use one-time (i.e. non repeating log in identifiers) which minimizes the risk of phishing or other intercepts. By logging into the system, the patient can see what medicines have been prescribed, which doctor prescribed them and when, what prescriptions have been collected from which pharmacy and on which date, and what the price paid was. The latter is useful for patients taking expensive medications because there is a national ceiling on patient medical costs so that the government will 100% of costs over the ceiling under the social insurance program. The record also shows what the dose should be, the name of the doctor who prescribed the medication, and what quantities of prescribed medications are still available for collection, subject to controls around the amount of medication is allowed to store at home. The system alerts doctors and pharmacies to prevent simultaneous use of incompatible medicines. For regular prescriptions, when the last prescription is fulfilled, the pharmacist can ask the patient if he would like the doctor to be asked to repeat issue the prescription. If yes, the pharmacy issues the request electronically and the doctor must either renew, modify, or terminate the prescription within 8 days. The patient can choose to receive a text message to his/her mobile phone advising of the outcome. Still being rolled out is the second phase of the project which is to enable the national database to be used to store other patient medical records. This is subject to the patient consenting for the transfer of data from his home health service station into the national archive. Patients thus have control over their own records, though most are expected to see the benefits of having their records in a permanently available national register. Version 2 HL7 messaging is widely deployed in most hospitals and healthcare centers. CDA R1 is used in sharing patient records on a regional level. • HL7 Finland national affiliate
Germany The German chapter of HL7 was founded in 1993. This is an entity for benefit to the public and registered as an association as HL7 e. V. Due to federal structure of operational administration in German healthcare, the standardization aspect is much behind the possibilities of the public economy and due to competitive interests in industry of low normative impact to healthcare information systems development. Currently HL7 in Germany operates as an informal cluster and collaborates with the national standardization body. The voluntary membership in HL7 relies on personal interest and engagement of clinical users and mainly on industrial interest: Clinical memberships are in minority. There is low membership of governmental administration and thus low contribution to ongoing discussion e.g. on patient data records (EPA = elektronische Patientenakte)
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Health Level 7 balancing data availability vs. data security. Impact on productivity for hospital information systems (called KIS = Krankenhaus-Informationssysteme in German) is of no importance yet, as the variability of HIS systems has not been challenged by HL7. Governmental funding for a broader adoption of HL7 does yet not exist in Germany, neither on federal level nor in most of the regional governments.
India HL7 India is active in conducting e-learning courses for HL7 and HL7 certification examinations. • HL7 India
Italy HL7 Italia was established in 2003 and it is responsible for the localization of this standard in the Italian context. The aim is to stimulate and direct the regional and national contributions towards the standard development and to promote the modernization of Italian health IT. HL7 Italia members are suppliers of health IT market. Some Italian regions, regional In-Houses, Governative Agencies, Public Research Institutes, local Hospital departments and individual IT professionals are HL7 Italia members. • HL7 Italia
The Netherlands AORTA - National Healthcare ICT infrastructure AORTA is the Dutch national infrastructure for the exchange of data between healthcare providers. AORTA uses HL7 version 3 messages and documents as its core mechanism for information exchange. The initial specifications were created in 2003. The Dutch Ministry of Health is working on a virtual national Electronic Patient Records (EPR) which will enable healthcare providers to share patient data. This development takes place in close collaboration with Nictiz, the National Information and Communication Technology Institute for Healthcare. Nictiz coordinates its efforts with regard to the usage of HL7 version 3 with the volunteers of HL7 the Netherlands. Almost all hospitals use HL7 version 2 to support hospital-internal workflows. EDIfact is being used to support workflows involving general practitioners (GPs/PCPs). EDIfact will be gradually replaced by HL7 version 3. • HL7 the Netherlands • National Information and Communication Technology Institute for Healthcare (Nictiz) • AORTA whitepaper
Pakistan HLH - Health Life Horizon HLH (a national project) is being carried out at School of Electrical Engineering and Computer Sciences of National University of Sciences and Technology and is supported by Government of Pakistan with funds provided by National ICT R&D Fund. HLH basic theme is to target health care information exchange and interoperability using HL7 version 3. Though the penetration and use of HL7 in Pakistan is lower than the countries like US and that of Europe. But its getting popularity among the stakeholders in Pakistan. HLH Team members have been participating in HL7 International Working Group meetings and conferences for last two years. On Oct 1, 2010 HL7 International formally announced the Pakistan Chapter at the 24th Plenary and WGM in Cambridge, MA, USA. Pakistan is now the 37th affiliate of HL7 International around the world. HL7 Pakistan is now officially authorized to give memberships to local organizations along with other privileges like certification, training, etc.
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Health Level 7 • Health Life Horizon (HLH) • HL7 Pakistan
USA Laika The Certification Commission for Healthcare Information Technology (CCHIT) has developed an open-source program called Laika to test EHR software for compliance with CCHIT interoperability standards. HIPAA HL7’s initial involvement in the Health Insurance Portability and Accountability Act (HIPAA) legislation began in 1996 with the formation of the Attachments special interest group to standardize the supplemental information needed to support health care insurance, and other e-commerce transactions. The initial deliverable of this group was a set of six recommended Claims Attachments for the Notice of Proposed Rule Making (NPRM) process. Future attachment projects include, but are not limited to, Home Health, Skilled Nursing Facility, durable medical equipment (DME), end stage renal disease (ESRD), and Pre-Authorization and Referrals. The Attachment special interest group is responsible for implementing the Administrative Simplification provisions of HIPAA mandates, providing on-going support, and representing HL7 in the Designated Standards Maintenance Organization (DSMO) efforts. Its purpose is to encourage the use of HL7 for uniform implementation of this supplemental information. This SIG coordinates industry input to produce and maintain guides for HL7 messages that can stand alone or be embedded within ANSI X12 transactions. ARRA and HITECH The American Recovery and Reinvestment Act of 2009 (ARRA) and Health Information Technology for Economic and Clinical Health Act (HITECH) legislation specified HL7 versions 2.3.1 and 2.5.1, and the HL7 Continuity of Care Document (CCD), as the healthcare standards to meet certain certification requirements.
References [1] http:/ / www. ansi. org/ ANSI
External links • • • • • • • • • • •
HL7 Standards (http://hl7messaging.com/hl7-standards) HL7.org site (http://www.HL7.org/) Introduction to HL7 (http://www.HL7.com.au/FAQ.htm) How does HL7 work? (Video Introduction) (http://blog.interfaceware.com/hl7/how-does-hl7-work/) HL7.org EHR Page (http://www.HL7.org/EHR) Fast Healthcare Interoperability Resources (FHIR) (http://www.HL7.org/FHIR) What does HL7 enablement means? (http://www.ehrmarket.com/blog/2009/10/ what-does-hl7-enablement-means/) HL7 is a member of the Joint Initiative on SDO Global Health Informatics Standardization (http://www. global-ehealth-standards.com/) HL7 Tools Page (http://www.HL7.com.au/HL7-Tools.htm) Australian Healthcare Messaging Laboratory (AHML) - Online HL7 Message Testing and Certification (http:// www.AHML.au.org/) Comprehensive Implementation of HL7 v3 Specifications in Java (http://aurora.regenstrief.org/javasig)
• HL7 Programming Tutorial using Java (http://www.saravanansubramanian.com/Saravanan/ Articles_On_Software/Entries/2009/2/21_HL7_Programming_using_Java_A_Short_Tutorial.html) • HL7 101: A Beginner’s Guide (http://www.fortherecordmag.com/archives/ftr_01082007p22.shtml)
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International HL7 Experts Consortium (http://www.HL7Experts.com/) The Healthcare IT Interoperability Blog (http://www.ehrguy.com/) NIST HL7 Conformance Testing Framework (http://www.cs.duke.edu/~jgm/files/nist.2006.ppt) ICH-HL7 Regulated Product Submissions (http://globalsubmit.com/home/LearningCenter/ HL7RegulatedProductSubmissions/tabid/252/Default.aspx)
Critical reviews • HL7 RIM: An Incoherent Standard (http://ontology.buffalo.edu/hl7/doublestandards.pdf) and The HL7 RIM Under Scrutiny - rebuttal (http://amisha.pragmaticdata.com/~schadow/Schadow-MIE06-r3.pdf) • HL7 Watch – Blog; critical review of HL7 (http://hl7-watch.blogspot.com/). See also the posted comments for attempted rebuttals to some of the earlier posts.
Open source tools There are a number of FOSS based tools that can foster worldwide adoption of the HL7 standards • The HL7 Tools Directory lists many FOSS tools and resources (http://www.HL7.com.au/HL7-Tools.htm) • Mirth Connect, an Open Source HL7 Integration Engine for HL7 V2/V3, DICOM, NCPDP and X12. Freely available under MPL 1.1 license (http://www.mirthcorp.com/community/overview) • The open health tools consortium (http://www.openhealthtools.org) • The HL7 Java Special Interest Group's implementation of HL7 v3 (http://aurora.regenstrief.org/javasig) • The Eclipse OHF project - v2 and v3 implementations (IDE type tools and run-time libraries) (http://www. eclipse.org/ohf) • HAPI, An open-source, object-oriented HL7 2.x parser for Java (http://hl7api.sourceforge.net/) • The Perl HL7 Toolkit, an Open-Source HL7 Perl module (http://hl7toolkit.sourceforge.net/) • Pear (PHP) HL7 messaging API module (http://pear.php.net/package/Net_HL7) • The eXcessively Simple HL7 v2 Processing Platform using XML and XSLT (http://aurora.regenstrief.org/ xhl7) • The Ruby HL7 Library (http://rubyforge.org/projects/ruby-hl7) • HL7 Java binding (http://xmlbeans.googlepages.com/) • nule.org HL7 Utilities (http://nule.org/wp/?page_id=55) • nHAPI open-source library for .NET (http://nhapi.sourceforge.net/home.php) • HL7v3 Messaging Framework for .NET (http://everest.marc-hi.ca/) • HL7 Analyst for .NET (http://hl7analyst.codeplex.com) • HL7Kit Free Sender, HL7 Message Editor and Sender (http://www.hl7kit.com/hl7senderOpenSource.html)
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Healthcare Common Procedure Coding System
Healthcare Common Procedure Coding System The Healthcare Common Procedure Coding System (HCPCS, often pronounced by its acronym as "hick picks") is a set of health care procedure codes based on the American Medical Association's Current Procedural Terminology (CPT).
History The acronym HCPCS originally stood for HCFA Common Procedure Coding System, as the Centers for Medicare and Medicaid (CMS) was previously (before 2001) known as the Health Care Financing Administration (HCFA). The Healthcare Common Procedure Coding System (HCPCS) was established in 1978 to provide a standardized coding system for describing the specific items and services provided in the delivery of health care. Such coding is necessary for Medicare, Medicaid, and other health insurance programs to ensure that insurance claims are processed in an orderly and consistent manner. Initially, use of the codes was voluntary, but with the implementation of the Health Insurance Portability and Accountability Act of 1996 (HIPAA) use of the HCPCS for transactions involving health care information became mandatory.[1]
Levels of codes HCPCS includes three levels of codes: • Level I consists of the American Medical Association's Current Procedural Terminology (CPT) and is numeric. • Level II codes are alphanumeric and primarily include non-physician services such as ambulance services and prosthetic devices,[2] and represent items and supplies and non-physician services, not covered by CPT-4 codes (Level I). • Level III codes, also called local codes, were developed by state Medicaid agencies, Medicare contractors, and private insurers for use in specific programs and jurisdictions. The use of Level III codes was discontinued on December 31, 2003, in order to adhere to consistent coding standards.[3]
References [1] at page 1 cms.hhs.gov (http:/ / www. cms. hhs. gov/ MedHCPCSGenInfo/ Downloads/ HCPCSReform. pdf) [2] HCPCS Level II Codes (http:/ / www. asha. org/ about/ publications/ leader-online/ b-line/ bl040608/ ) [3] HCPCS Background Information (http:/ / www. cms. hhs. gov/ MedHCPCSGenInfo/ )
External links • Official site (http://www.cms.hhs.gov/MedHCPCSGenInfo/) • HCPCS Level II alphanumeric procedure and modifier codes (http://www.cms.hhs.gov/ HCPCSReleaseCodeSets/01_Overview.asp#TopOfPage) • NDC-HCPCS crosswalk data files (http://www.cms.hhs.gov/McrPartBDrugAvgSalesPrice/01a_2008aspfiles. asp) • Free online HCPCS Level 2 Codes Search Engine from drchrono (https://drchrono.com/billing/medical_codes/ ?code_type=hcpcs_level_2&search_text=Enter+search+here.&Submit=Search) • Searchable HCPCS codes and NDC numbers (http://www.commondatahub.com/hcpcs_source.jsp)
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Healthcare Information Technology Standards Panel
Healthcare Information Technology Standards Panel The American National Standards Institute (ANSI) Healthcare Information Technology Standards Panel (HITSP) was created in 2005 as part of efforts by the Office of the National Coordinator for Health Information Technology (ONC, part of the United States Department of Health and Human Services) to promote interoperability in health care by harmonizing health information technology standards. HITSP is chaired by John Halamka, MD, CIO of Harvard Medical School.
Membership Membership is by organization and there is currently no cost to join. Volunteers commit time to working on standards harmonization efforts as prioritized by the American Health Information Community (AHIC).
Goals According to their website, HITSP's mission is to "serve as a cooperative partnership between the public and private sectors for the purpose of achieving a widely accepted and useful set of standards specifically to enable and support widespread interoperability among healthcare software applications, as they will interact in a local, regional and national health information network for the United States." HITSP is generally organized around Use Cases, which are profiles of specific interoperability needs that have been identified by AHIC as being important national priorities. The initial 2006 Use Cases were: • Consumer Empowerment • Registration Summary • Medication History • Electronic Health Records (EHRs) • Laboratory Result Reporting • Biosurveillance • • • •
Visit Utilization Clinical Data Lab and Radiology
The 2007 Use Cases are: • Consumer Access to Clinical Information • Access to Clinical Data • Provider Permissions • Personal Health Record (PHR) Transfer • Emergency Responder EHR • On-site Care • Emergency Care • Definitive Care • Provider Authentication and Authorization • Medication Management • Medication Reconciliation • Ambulatory Prescriptions
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Healthcare Information Technology Standards Panel • Contraindications • Quality • Hospital Measurement and Reporting • Clinician Measurement and Reporting • Feedback to Clinicians
External links • Healthcare Information Technology Standards Panel website [1]
References [1] http:/ / www. hitsp. org
Health Informatics Service Architecture The European Committee for Standardization (CEN) Standard Architecture for Healthcare Information Systems (ENV 12967), Health Informatics Service Architecture or HISA is a standard that provides guidance on the development of modular open information technology (IT) systems in the healthcare sector. Broadly, architecture standards outline frameworks which can be used in the development of consistent, coherent applications, databases and workstations. This is done through the definition of hardware and software construction requirements and outlining of protocols for communications.[1] The HISA standard provides a formal standard for a Service Oriented Architecture (SOA), specific for the requirements of health services, based on the principles of Open Distributed Processing.[2] The HISA standard evolved from previous work on healthcare information systems architecture commenced by Reseau d’Information et de Communication Hospitalier Europeen (RICHE) in 1989, and subsequently built upon by a number of organizations across Europe.[3]
Development of Health Informatics Service Architecture EN/ISO 12967 The HISA standard was developed by CEN Technical Committee (TC) 251, the technical committee for Health Informatics within the federation of European national standards bodies (CEN).[4] The CEN/TC 251 was made up of four working groups, covering: information models; systems of concepts and terminology; security; and technologies for interoperable communication.[5] Working Group I were responsible for information models and completed the specifications that became the HISA standard. Working Group I worked with experts from across Europe, plus contributors from Australia and the United States in the development and finalization of ENV 12967. The CEN HISA standard was adopted by the International Organization for Standardization (ISO) in 2009, with the stated aim of ISO 12967 being to provide guidance on: • the description, planning and development of new electronic health systems; and • the integration of existing electronic health systems, both intra- and inter-organizationally, through architecture that integrates common data and business logic into middleware, which is then made available throughout whole information systems.
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The Standard EN/ISO 12967 is broken down into three parts: Enterprise Viewpoint; Information Viewpoint; and Computational Viewpoint, all of which deal with different aspects of ensuring service architecture supports openness and vendor-independence.[6] Part One: Enterprise Viewpoint The Enterprise Viewpoint component of EN/ISO 12967 provides health services with guidance in describing, planning and developing new IT systems, utilizing an open distributed processing approach. In addition to this it provides direction for the integration of existing information systems, within the one enterprise and across different healthcare organizations. Part one of the standard sets forth the common enterprise-level requirements (e.g. workflows, authorizations) that must be supported through the HISA, which integrates the common data and business logic into a specific architectural layer (i.e. the middleware), accessible throughout the whole information system of the health service.[7] Part Two: Information Viewpoint The Information Viewpoint component of EN/ISO 12967 sets forth the fundamental characteristics of the information model to be implemented by the middleware to provide comprehensive, integrated storage of the common enterprise data and to support the fundamental business processes of the healthcare organisation, as defined in ISO 12967 Part One. The specifications were designed to be universally relevant, whilst being sufficiently specific to allow implementers to derive an efficient design of the system for their organisation. This specification does not aim to provide a fixed, complete specification of all possible data that may be necessary for any given health service. It specifies only a set of characteristics, in terms of overall organisation and individual information objects, identified as fundamental and common to all healthcare organizations.[8] Part Three: Computational Viewpoint The Computational Viewpoint component of EN/ISO 12967 provides details on the fundamental characteristics of the computational model to be implemented by the middleware, to provide a comprehensive and integrated interface to the common, fundamental business processes of the health service. The computational model, like the information model is designed to be universally relevant, whilst still being sufficiently specific to allow implementers to derive an efficient design of the system for their organisation, irrespective of the specifics of the pre-existing information technology environment in which it will be implemented.[9]
Use of common services The implementation of a modular, open architecture in healthcare IT systems (as specified by the HISA) relies upon disparate heterogeneous applications interacting and communicating through a middleware layer, made up of common services. In the case of the HISA, these common services are divided into Healthcare-related Common Services and Generic Common Services.[10] Healthcare-related Common Services (HCS) Healthcare-related Common Services are those middleware components responsible for supporting the functionalities and information relevant to the healthcare business domain, including subject of care, activities, resources, authorization, health characteristics and concepts.[11] Generic Common Services (GCS) Generic Common Services are those middleware components are those middleware components responsible for supporting the generic functionality and information requirements that are non-specific to the healthcare domain, and may be broadly relevant to any information system in the business domain.[12]
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References [1] Engel, Kjeld. Blobel, Bernd. Pharow, Peter. (2006) Standards for Enabling Health Informatics Interoperability, Ubiquity: Technologies for Better Health in Aging Societies IOS Press [2] Klein, Gunnar. Sottile, Pier Angelo. Endsleff, Frederik. (2007) Another HISA - The new standard: Health Informatics - Service Architecture,Studies in Health Technology and Informatics,129(Pt 1):478-82 [3] Kalra,Dipak. (2002) Clinical Foundations and Information Architecture for the Implementation of a Federated Health Record Service, University College, London [4] Klein, Gunnar. Sottile, Pier Angelo. Endsleff, Frederik. (2007) Another HISA - The new standard: Health Informatics - Service Architecture,Studies in Health Technology and Informatics,129(Pt 1):478-82 [5] Kalra,Dipak. (2002) Clinical Foundations and Information Architecture for the Implementation of a Federated Health Record Service, University College, London [6] Klein, Gunnar. Sottile, Pier Angelo. Endsleff, Frederik. (2007) Another HISA - The new standard: Health Informatics - Service Architecture,Studies in Health Technology and Informatics,129(Pt 1):478-82 [7] European Committee for Standardization: http:/ / www. cen. eu/ cen/ Pages/ default. as