Skip to main content
Emergency Medicine Journal : EMJ logoLink to Emergency Medicine Journal : EMJ
. 2003 Sep;20(5):426–428. doi: 10.1136/emj.20.5.426

How well does decision support software perform in the emergency department?

M Graber 1, D VanScoy 1
PMCID: PMC1726199  PMID: 12954680

Abstract

Objective: To determine how well general decision support systems perform given the data collected in an emergency department (ED).

Methods: A convenience sample of 25 patients was selected from those patients having a diagnostic question on presentation to the ED. All interactions with the patients were audiotaped and abstracted into a structured data form. All other data such as written notes, laboratory, and EKG results were also abstracted. All data were entered into two general diagnostic decision support programs (Quick Medical Reference (QMR Version 3.82, Knowledge Base 10–07–1998 Copyright University of Pittsburgh and The Hearst Corporation) and Iliad (Version 4.5 Copyright 1996 Applied Medical Informatics)). The diagnoses generated by the computer programs were compared with the final diagnoses of the ED attending.

Results: The final ED diagnosis was found in the differential diagnosis generated by Iliad and QMR 72% and 52% of the time respectively. The final ED diagnosis was found in the top 10 diagnoses 51% and 44% of the time and in the top five diagnoses 36% and 32% of the time for each program respectively. This approximates to the performance of these programs in other clinical settings.

Conclusions: Diagnostic decision support software has the same success in finding the "correct" diagnosis in the ED as in other clinical settings where more extensive clinical data are available. The accuracy is not sufficiently high to permit the use of these programs as an arbiter in any individual case. However, they may be useful, prompting additional investigation in particularly difficult cases.

Full Text

The Full Text of this article is available as a PDF (72.0 KB).

Selected References

These references are in PubMed. This may not be the complete list of references from this article.

  1. Bankowitz R. A., McNeil M. A., Challinor S. M., Miller R. A. Effect of a computer-assisted general medicine diagnostic consultation service on housestaff diagnostic strategy. Methods Inf Med. 1989 Nov;28(4):352–356. [PubMed] [Google Scholar]
  2. Berner E. S., Webster G. D., Shugerman A. A., Jackson J. R., Algina J., Baker A. L., Ball E. V., Cobbs C. G., Dennis V. W., Frenkel E. P. Performance of four computer-based diagnostic systems. N Engl J Med. 1994 Jun 23;330(25):1792–1796. doi: 10.1056/NEJM199406233302506. [DOI] [PubMed] [Google Scholar]
  3. Elstein A. S., Friedman C. P., Wolf F. M., Murphy G., Miller J., Fine P., Heckerling P., Miller T., Sisson J., Barlas S. Effects of a decision support system on the diagnostic accuracy of users: a preliminary report. J Am Med Inform Assoc. 1996 Nov-Dec;3(6):422–428. doi: 10.1136/jamia.1996.97084515. [DOI] [PMC free article] [PubMed] [Google Scholar]
  4. Friedman C. P., Elstein A. S., Wolf F. M., Murphy G. C., Franz T. M., Heckerling P. S., Fine P. L., Miller T. M., Abraham V. Enhancement of clinicians' diagnostic reasoning by computer-based consultation: a multisite study of 2 systems. JAMA. 1999 Nov 17;282(19):1851–1856. doi: 10.1001/jama.282.19.1851. [DOI] [PubMed] [Google Scholar]
  5. Gozum M. E. Emulating cognitive diagnostic skills without clinical experience: a report of medical students using Quick Medical Reference and Iliad in the diagnosis of difficult clinical cases. Proc Annu Symp Comput Appl Med Care. 1994:991–991. [PMC free article] [PubMed] [Google Scholar]
  6. Grum C. M., Miller J. G., Wolf F. M. Computer-based problem solving for primary-care diagnosis in an internal medicine clerkship. Acad Med. 1994 May;69(5):429–430. [PubMed] [Google Scholar]
  7. Jonsbu J., Aase O., Rollag A., Liestøl K., Erikssen J. Prospective evaluation of an EDB-based diagnostic program to be used in patients admitted to hospital with acute chest pain. Eur Heart J. 1993 Apr;14(4):441–446. doi: 10.1093/eurheartj/14.4.441. [DOI] [PubMed] [Google Scholar]
  8. Massel D., Dawdy J. A., Melendez L. J. Strict reliance on a computer algorithm or measurable ST segment criteria may lead to errors in thrombolytic therapy eligibility. Am Heart J. 2000 Aug;140(2):221–226. doi: 10.1067/mhj.2000.108240. [DOI] [PubMed] [Google Scholar]
  9. Qamar A., McPherson C., Babb J., Bernstein L., Werdmann M., Yasick D., Zarich S. The Goldman algorithm revisited: prospective evaluation of a computer-derived algorithm versus unaided physician judgment in suspected acute myocardial infarction. Am Heart J. 1999 Oct;138(4 Pt 1):705–709. doi: 10.1016/s0002-8703(99)70186-9. [DOI] [PubMed] [Google Scholar]
  10. Schriger D. L., Baraff L. J., Rogers W. H., Cretin S. Implementation of clinical guidelines using a computer charting system. Effect on the initial care of health care workers exposed to body fluids. JAMA. 1997 Nov 19;278(19):1585–1590. [PubMed] [Google Scholar]
  11. de Dombal F. T., Dallos V., McAdam W. A. Can computer aided teaching packages improve clinical care in patients with acute abdominal pain? BMJ. 1991 Jun 22;302(6791):1495–1497. doi: 10.1136/bmj.302.6791.1495. [DOI] [PMC free article] [PubMed] [Google Scholar]

Articles from Emergency Medicine Journal : EMJ are provided here courtesy of BMJ Publishing Group

RESOURCES