Electronic health record

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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]

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.[3]

Features

Computerized provider order entry (CPOE)

CPOE is defined as "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."[4]

CPOE is one of the four recommendations by the Leapfrog Group.[5]

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.[6] 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.[7]

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[8][9], sequences of events[10], categorization of physical examination findings[11] and use of medications for specific diseases.[12]

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.[3] 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)[13][14], the Massachusetts eHealth Collaborative (MAeHC)[15] funded by $50 million dollars from Blue Cross Blue Shield of Massachusetts[16], and Inland Northwest Health Services (Spokane).[17]

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.[18][19]

Failed implementations

  • Kaiser - Hawaii[20]
  • Limpopo (Northern) Province, South Africa[21]

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[22] This may be due to computer interfaces that are not intuitive to use.[23]

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.[24] 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[25], but not all studies.[26][27]

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.[28]

Research

The electronic health record can provide data for health research. One issue is protecting the privacy of patients.[29][30]

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. ↑ 3.0 3.1 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
  4. ↑ Medical Order Entry Systems. \aurhot=National Library of Medicine. Retrieved on 2007-11-01.
  5. ↑ The Leapfrog Group Fact Sheet. Retrieved on 2007-11-01.
  6. ↑ 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
  7. ↑ 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.
  8. ↑ 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.
  9. ↑ 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.
  10. ↑ 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.
  11. ↑ 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).
  12. ↑ 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.
  13. ↑ Foundation for eHealth Initiative. Indiana Health Information Exchange (Indiana Health Information Exchange). Retrieved on 2007-11-01.
  14. ↑ 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.
  15. ↑ Foundation for eHealth Initiative. Massachusetts eHealth Collaborative (Massachusetts eHealth Collaborative). Retrieved on 2007-11-01.
  16. ↑ Massachusetts eHealth Collaborative. About Us - Mission Statement. Retrieved on 2007-11-01.
  17. ↑ Foundation for eHealth Initiative. Inland Northwest Health Services(Inland Northwest Health Services). Retrieved on 2007-11-01.
  18. ↑ 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]
  19. ↑ 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]
  20. ↑ 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.
  21. ↑ 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.
  22. ↑ 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.
  23. ↑ Nielsen, Jakob (April 11, 2005). Medical Usability: How to Kill Patients Through Bad Design (Jakob Nielsen's Alertbox). Retrieved on 2007-10-23.
  24. ↑ 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". Journal of the American Medical Informatics Association : JAMIA 14 (4): 415–23. DOI:10.1197/jamia.M2373. PMID 17460127. Research Blogging.
  25. ↑ 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.
  26. ↑ 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.
  27. ↑ 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.
  28. ↑ 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.
  29. ↑ 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.
  30. ↑ 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.

See also

External links

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