Electronic health record: Difference between revisions

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===Research===
===Research===
The electronic health record can provide data for health research. One issue is protecting the privacy of patients.<ref name="pmid17600094">{{cite journal |author=Uzuner O, Luo Y, Szolovits P |title=Evaluating the state-of-the-art in automatic de-identification |journal=Journal of the American Medical Informatics Association : JAMIA |volume=14 |issue=5 |pages=550–63 |year=2007 |pmid=17600094 |doi=10.1197/jamia.M2444}}</ref><ref name="pmid17823086">{{cite journal |author=Szarvas G, Farkas R, Busa-Fekete R |title=State-of-the-art anonymization of medical records using an iterative machine learning framework |journal=Journal of the American Medical Informatics Association : JAMIA |volume=14 |issue=5 |pages=574–80 |year=2007 |pmid=17823086 |doi=10.1197/j.jamia.M2441}}</ref>. It may also be used for creating a [[clinical data warehouse]].
The electronic health record can provide data for health research. One issue is protecting the privacy of patients.<ref name="pmid17600094">{{cite journal |author=Uzuner O, Luo Y, Szolovits P |title=Evaluating the state-of-the-art in automatic de-identification |journal=Journal of the American Medical Informatics Association : JAMIA |volume=14 |issue=5 |pages=550–63 |year=2007 |pmid=17600094 |doi=10.1197/jamia.M2444}}</ref><ref name="pmid17823086">{{cite journal |author=Szarvas G, Farkas R, Busa-Fekete R |title=State-of-the-art anonymization of medical records using an iterative machine learning framework |journal=Journal of the American Medical Informatics Association : JAMIA |volume=14 |issue=5 |pages=574–80 |year=2007 |pmid=17823086 |doi=10.1197/j.jamia.M2441}}</ref>. It may also be used for creating a [[clinical data warehouse]].
==Benefits==
Hospitals with high use of information technology may provide better health care.<ref>{{Cite journal
| doi = 10.1001/archinternmed.2008.520
| volume = 169
| issue = 2
| pages = 108-114
| last = Amarasingham
| first = Ruben
| coauthors = Laura Plantinga, Marie Diener-West, Darrell J. Gaskin, Neil R. Powe
| title = Clinical Information Technologies and Inpatient Outcomes: A Multiple Hospital Study
| journal = Arch Intern Med
| accessdate = 2009-01-27
| date = 2009-01-26
| url = http://archinte.ama-assn.org/cgi/content/abstract/169/2/108
}}</ref>


==Privacy==
==Privacy==

Revision as of 09:40, 27 January 2009

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The electronic health record (EHR) is defined as a "computer-based systems for input, storage, display, retrieval, and printing of information contained in a patient's medical record."[1]

Personal health record (PHR) is a variation in which the patient maintains the data rather than the health care provider maintaining the data.[2][3] Examples of PHR include Dossia (http://www.dossia.org) and Microsoft HealthVault (http://www.healthvault.com).

In the future it is hoped that EHRs across different health care systems will be able to exchange patient information in regional health information organizations (RHIOs); however, this goal has been elusive.[4]

EHRs hopefully save costs after an initial period of cost loss.[5] EHRs cost about $15,000 per privider in 2002 American dollars.

Features

Medical order entry system (CPOE)

For more information, see: Medical order entry system.

Medical order entry systems, also called computerized provider order entry systems (CPOE) are "information systems, usually computer-assisted, that enable providers to initiate medical procedures, prescribe medications, etc. These systems support medical decision-making and error-reduction during patient care."[6]

Clinical decision support

For more information, see: Clinical decision support system.

Links to medical knowledge

For more information, see: information retrieval.

Content in the EHR that is codifiable with a standard taxonomy can be linked to medical knowledge that is indexed with the same taxonomy. As example is Infobuttons that automatically displays links from the EHR to external knowledge sources.[7] One trial studied the effects of adding a feature to the EHR that allows the clinical to request assistance with information retrieval from an informationist.[8]

Natural language processing

Computers can use text mining to analyze text to in order to create structured data. An example is identifying smoking status of patients[9][10], sequences of events[11], categorization of physical examination findings[12] and use of medications for specific diseases.[13]

Interoperability

Ideally, patient data should be able to be transferred across different EHRs as patients move across health care systems. Networked EHRs are call to a health information exchange (HIE) or regional health information organization (RHIO).

In 1999 the Santa Barbara County Care Data Exchange was initially funded by $10 million dollars from the California HealthCare Foundation in order to be HIE demonstration project.[4] By fall 2006, two organizations within the HIE were able to exchange some information. However, in December 2006 the project's board decided to close the project due to funding problems.

Other RHIOs include The Indiana network for patient care (INPC)[14][15][16], the Massachusetts eHealth Collaborative (MAeHC)[17] funded by $50 million dollars from Blue Cross Blue Shield of Massachusetts[18], and Inland Northwest Health Services (Spokane).[19]

In the United States, the Department of Veteran Affairs and the Department of Defense are creating data exchange between their EHRs.[20]

Uses

Clinical care

Successful implementations

The United States Department of Veterans Affairs has successfully implemented an electronic health record system, "VistA", across a very large health care system.[21][22]

Failed implementations

  • Kaiser - Hawaii[23]
  • Limpopo (Northern) Province, South Africa[24]

Unintended consequences

Unintended consequences, that are a mix of positive and negative, may occur to computerized provider order entry.[25]

Adverse effects

Most all of the adverse effects are due to just the computerized provider order entry component of the electronic medical record.

Implementation of the computerized provider order entry has been associated with medication errors[26] This may be due to computer interfaces that are not intuitive to use.[27]

Computerized provider order entry has been associated with causing a number of unintended consequences with "new work/more work, workflow, system demands, communication, emotions, and dependence on the technology" being most severe.[25] In this study, shifts in power ("The presence of a system that enforces specific clinical practices through mandatory data entry fields changes the power structure of organizations. Often the power or autonomy of physicians is reduced, while the power of the nursing staff, information technology specialists, and administration is increased") were also observed.

The introduction of computerized provider order entry has been associated with increased hospital mortality in some[28], but not all studies.[29][30]

Quality management

There have been very few studies assessing the quality content [1].

Rapid system learning

The electronic health record may allow rapid system learning for events such as disease outbreaks.[31]

Research

The electronic health record can provide data for health research. One issue is protecting the privacy of patients.[32][33]. It may also be used for creating a clinical data warehouse.

Benefits

Hospitals with high use of information technology may provide better health care.[34]

Privacy

Maintaining privacy of personal health information (PHI) is important goal of the Health Insurance Portability and Accountability Act (HIPAA). Various attempts at automated the de-identification of records are ongoing.[35]

References

  1. ↑ National Library of Medicine. MeSH Descriptor Data. Retrieved on 2007-10-23.
  2. ↑ Halamka J, Mandl KD, Tang P (2007). "Early Experiences with Personal Health Records". J Am Med Inform Assoc. DOI:10.1197/jamia.M2562. PMID 17947615. Research Blogging.
  3. ↑ Steinbrook, R. (2008). Personally Controlled Online Health Data -- The Next Big Thing in Medical Care? N Engl J Med, 358(16), 1653-1656. Template:DOI
  4. ↑ 4.0 4.1 Miller RH, Miller BS (2007). "The Santa Barbara County Care Data Exchange: what happened?". Health affairs (Project Hope) 26 (5): w568–80. DOI:10.1377/hlthaff.26.5.w568. PMID 17670775. Research Blogging. Cite error: Invalid <ref> tag; name "pmid17670775" defined multiple times with different content
  5. ↑ Wang SJ, Middleton B, Prosser LA, et al (2003). "A cost-benefit analysis of electronic medical records in primary care". Am. J. Med. 114 (5): 397–403. PMID 12714130. [e]
  6. ↑ Anonymous (2024), Medical order entry systems (English). Medical Subject Headings. U.S. National Library of Medicine.
  7. ↑ Cimino JJ (2006). "Use, usability, usefulness, and impact of an infobutton manager". AMIA Annu Symp Proc: 151–5. PMID 17238321. [e] Full text at PubMed Central
  8. ↑ Jerome RN, Giuse NB, Rosenbloom ST, Arbogast PG (2008). "Exploring clinician adoption of a novel evidence request feature in an electronic medical record system". J Med Libr Assoc 96 (1): 34–41. DOI:10.3163/1536-5050.96.1.34. PMID 18219379. Research Blogging.
  9. ↑ Clark C, Good K, Jezierny L, Macpherson M, Wilson B, Chajewska U (2007). "Identifying Smokers with a Medical Extraction System". J Am Med Inform Assoc. DOI:10.1197/jamia.M2442. PMID 17947619. Research Blogging.
  10. ↑ Savova GK, Ogren PV, Duffy PH, Buntrock JD, Chute CG (2007). "Mayo Clinic NLP System for Patient Smoking Status Identification". J Am Med Inform Assoc. DOI:10.1197/jamia.M2437. PMID 17947622. Research Blogging.
  11. ↑ Zhou L, Parsons S, Hripcsak G (2007). "The Evaluation of a Temporal Reasoning System in Processing Clinical Discharge Summaries". J Am Med Inform Assoc. DOI:10.1197/jamia.M2467. PMID 17947618. Research Blogging.
  12. ↑ Serguei V.S. Pakhomov et al., “Automatic Classification of Foot Examination Findings using Statistical Natural Language Processing and Machine Learning,” J Am Med Inform Assoc (December 20, 2007), http://www.jamia.org/cgi/content/abstract/M2585v1 (accessed December 21, 2007).
  13. ↑ Chen ES, Hripcsak G, Xu H, Markatou M, Friedman C (2007). "Automated Acquisition of Disease-Drug Knowledge from Biomedical and Clinical Documents: An Initial Study". J Am Med Inform Assoc. DOI:10.1197/jamia.M2401. PMID 17947625. Research Blogging.
  14. ↑ Foundation for eHealth Initiative. Indiana Health Information Exchange (Indiana Health Information Exchange). Retrieved on 2007-11-01.
  15. ↑ Zafar A, Dixon BE (2007). "Pulling back the covers: technical lessons of a real-world health information exchange". Medinfo 12 (Pt 1): 488–92. PMID 17911765. [e]
  16. ↑ McDonald CJ, Overhage JM, Barnes M, et al (2005). "The Indiana network for patient care: a working local health information infrastructure. An example of a working infrastructure collaboration that links data from five health systems and hundreds of millions of entries". Health affairs (Project Hope) 24 (5): 1214–20. DOI:10.1377/hlthaff.24.5.1214. PMID 16162565. Research Blogging.
  17. ↑ Foundation for eHealth Initiative. Massachusetts eHealth Collaborative (Massachusetts eHealth Collaborative). Retrieved on 2007-11-01.
  18. ↑ Massachusetts eHealth Collaborative. About Us - Mission Statement. Retrieved on 2007-11-01.
  19. ↑ Foundation for eHealth Initiative. Inland Northwest Health Services(Inland Northwest Health Services). Retrieved on 2007-11-01.
  20. ↑ Bouhaddou, O., Warnekar, P., Parrish, F., Do, N., Mandel, J., Kilbourne, J., et al. (2008). Exchange of computable patient data between the department of veterans affairs (VA) and the department of defense (DOD): terminology mediation strategy, J Am Med Inform Assoc, 15(2), 174-183. doi: 10.1197/jamia.M2498.
  21. ↑ Brown SH, Lincoln MJ, Groen PJ, Kolodner RM (2003). "VistA--U.S. Department of Veterans Affairs national-scale HIS". International journal of medical informatics 69 (2-3): 135–56. PMID 12810119. [e]
  22. ↑ Fletcher RD, Dayhoff RE, Wu CM, Graves A, Jones RE (2001). "Computerized medical records in the Department of Veterans Affairs". Cancer 91 (8 Suppl): 1603–6. PMID 11309758. [e]
  23. ↑ Scott JT, Rundall TG, Vogt TM, Hsu J (2005). "Kaiser Permanente's experience of implementing an electronic medical record: a qualitative study". BMJ 331 (7528): 1313–6. DOI:10.1136/bmj.38638.497477.68. PMID 16269467. Research Blogging.
  24. ↑ Littlejohns P, Wyatt JC, Garvican L (2003). "Evaluating computerised health information systems: hard lessons still to be learnt". BMJ 326 (7394): 860–3. DOI:10.1136/bmj.326.7394.860. PMID 12702622. Research Blogging.
  25. ↑ 25.0 25.1 Ash JS, Sittig DF, Poon EG, Guappone K, Campbell E, Dykstra RH (2007). "The extent and importance of unintended consequences related to computerized provider order entry". J Am Med Inform Assoc 14 (4): 415–23. DOI:10.1197/jamia.M2373. PMID 17460127. Research Blogging. Cite error: Invalid <ref> tag; name "pmid17460127" defined multiple times with different content
  26. ↑ Koppel R, Metlay JP, Cohen A, et al (2005). "Role of computerized physician order entry systems in facilitating medication errors". JAMA 293 (10): 1197–203. DOI:10.1001/jama.293.10.1197. PMID 15755942. Research Blogging.
  27. ↑ Nielsen, Jakob (April 11, 2005). Medical Usability: How to Kill Patients Through Bad Design (Jakob Nielsen's Alertbox). Retrieved on 2007-10-23.
  28. ↑ Han YY, Carcillo JA, Venkataraman ST, et al (2005). "Unexpected increased mortality after implementation of a commercially sold computerized physician order entry system". Pediatrics 116 (6): 1506–12. DOI:10.1542/peds.2005-1287. PMID 16322178. Research Blogging.
  29. ↑ Keene A, Ashton L, Shure D, Napoleone D, Katyal C, Bellin E (2007). "Mortality before and after initiation of a computerized physician order entry system in a critically ill pediatric population". Pediatric critical care medicine : a journal of the Society of Critical Care Medicine and the World Federation of Pediatric Intensive and Critical Care Societies 8 (3): 268–71. DOI:10.1097/01.PCC.0000260781.78277.D9. PMID 17417119. Research Blogging.
  30. ↑ Del Beccaro MA, Jeffries HE, Eisenberg MA, Harry ED (2006). "Computerized provider order entry implementation: no association with increased mortality rates in an intensive care unit". Pediatrics 118 (1): 290–5. DOI:10.1542/peds.2006-0367. PMID 16818577. Research Blogging.
  31. ↑ Reis BY, Kirby C, Hadden LE, et al (2007). "AEGIS: a robust and scalable real-time public health surveillance system". Journal of the American Medical Informatics Association : JAMIA 14 (5): 581–8. DOI:10.1197/jamia.M2342. PMID 17600100. Research Blogging.
  32. ↑ Uzuner O, Luo Y, Szolovits P (2007). "Evaluating the state-of-the-art in automatic de-identification". Journal of the American Medical Informatics Association : JAMIA 14 (5): 550–63. DOI:10.1197/jamia.M2444. PMID 17600094. Research Blogging.
  33. ↑ Szarvas G, Farkas R, Busa-Fekete R (2007). "State-of-the-art anonymization of medical records using an iterative machine learning framework". Journal of the American Medical Informatics Association : JAMIA 14 (5): 574–80. DOI:10.1197/j.jamia.M2441. PMID 17823086. Research Blogging.
  34. ↑ Amarasingham, Ruben; Laura Plantinga, Marie Diener-West, Darrell J. Gaskin, Neil R. Powe (2009-01-26). "Clinical Information Technologies and Inpatient Outcomes: A Multiple Hospital Study". Arch Intern Med 169 (2): 108-114. DOI:10.1001/archinternmed.2008.520. Retrieved on 2009-01-27. Research Blogging.
  35. ↑ Morrison, Frances P.; Li Li, Albert M. Lai, George Hripcsak (2008-10-24). "Repurposing the clinical record: can an existing natural language processing system de-identify clinical notes?". J Am Med Inform Assoc: M2862. DOI:10.1197/jamia.M2862. Retrieved on 2008-10-27. Research Blogging.

See also

External links

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