Differential diagnosis generators: an evaluation of currently available computer programs.
Background: Differential diagnosis (DDX) generators are computer programs that generate a DDX based on various clinical data.Objective: We identified evaluation criteria through consensus, applied these criteria to describe the features of DDX generators, and tested performance using cases from the...
| Publicado en: | JGIM: Journal of General Internal Medicine Vol. 27; no. 2; pp. 213 - 220 |
|---|---|
| Autores principales: | , , , , , , , , , , , |
| Formato: | research Journal Article |
| Publicado: |
Springer Nature
Feb2012
|
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=104440192&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 104440192 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 08848734 4BF jtl: JGIM: Journal of General Internal Medicine issn: 08848734 maglogo: N pubinfo: dt: Feb2012 vid: 27 iid: 2 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 104440192 NLM21789717 2011750928 10.1007/s11606-011-1804-8 NLM21789717 PMC3270234 104440192 ppf: 213 ppct: 7 formats: tig: atl: Differential diagnosis generators: an evaluation of currently available computer programs. aug: au: Bond WF Schwartz LM Weaver KR Levick D Giuliano M Graber ML Bond, William F Schwartz, Linda M Weaver, Kevin R Levick, Donald Giuliano, Michael Graber, Mark L affil: Department of Emergency Medicine, Lehigh Valley Health Network, Allentown, PA, USA sug: subj: Medical Practice, Evidence-Based Standards Software Standards Diagnosis, Differential Medical Practice, Evidence-Based Methods Human ab: Background: Differential diagnosis (DDX) generators are computer programs that generate a DDX based on various clinical data.Objective: We identified evaluation criteria through consensus, applied these criteria to describe the features of DDX generators, and tested performance using cases from the New England Journal of Medicine (NEJM©) and the Medical Knowledge Self Assessment Program (MKSAP©).Methods: We first identified evaluation criteria by consensus. Then we performed Google® and Pubmed searches to identify DDX generators. To be included, DDX generators had to do the following: generate a list of potential diagnoses rather than text or article references; rank or indicate critical diagnoses that need to be considered or eliminated; accept at least two signs, symptoms or disease characteristics; provide the ability to compare the clinical presentations of diagnoses; and provide diagnoses in general medicine. The evaluation criteria were then applied to the included DDX generators. Lastly, the performance of the DDX generators was tested with findings from 20 test cases. Each case performance was scored one through five, with a score of five indicating presence of the exact diagnosis. Mean scores and confidence intervals were calculated.Key Results: Twenty three programs were initially identified and four met the inclusion criteria. These four programs were evaluated using the consensus criteria, which included the following: input method; mobile access; filtering and refinement; lab values, medications, and geography as diagnostic factors; evidence based medicine (EBM) content; references; and drug information content source. The mean scores (95% Confidence Interval) from performance testing on a five-point scale were Isabel© 3.45 (2.53, 4.37), DxPlain® 3.45 (2.63-4.27), Diagnosis Pro® 2.65 (1.75-3.55) and PEPID™ 1.70 (0.71-2.69). The number of exact matches paralleled the mean score finding.Conclusions: Consensus criteria for DDX generator evaluation were developed. Application of these criteria as well as performance testing supports the use of DxPlain® and Isabel© over the other currently available DDX generators. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
|---|