Differential Diagnosis Generators: an Evaluation of Currently Available Computer Programs
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Differential diagnosis (DDX) generators are computer programs that generate a DDX based on various clinical data.
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©).
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.
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.
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.
KEY WORDSdifferential diagnosis clinical decision support systems diagnostic errors evidence-based medicine computer-assisted diagnosis
The authors would like to thank medical students Genine Siciliano, Agnes Nambiro, Grace Garey, and Mary Lou Glazer for entering the findings into the DDX generators for testing.
Sponsors: This study was not funded by an external sponsor.
Presentations: This information was presented in poster format at the Diagnostic Error in Medicine Conference 2010, Toronto, Canada.
Conflict of Interest
- 1.CRICO Harvard Risk Management Foundation. High Risk Areas: 26% of claims are in the category of diagnosis. Accessed May 30th, 2011, at http://www.rmf.harvard.edu/high-risk-areas.
- 3.Croskerry P. Clinical cognition and diagnostic error: applications of a dual process model of reasoning Advances In Health Sciences Education. Theory And Practice. 2009;14(Suppl 1):27–35.Google Scholar
- 4.Schiff, G. D., Kim, S., Abrams, R., Cosby, K., Lambert, B. L., Elstein, A. S., Hasler, S., et al., Diagnosing Diagnosis Errors: Lessons from a Multi-institutional Collaborative Project. Advances in Patient Safety: From Research to Implementation. Volumes 2, AHRQ Publication Nos. 050021 (Vols 1–4). February 2005. Agency for Healthcare Research and Quality, Rockville, MD. Accessed May 30, 2011, at http://www.ahrq.gov/qual/advances/.
- 11.Musen, M. A., Shahar, Y. and Shortliffe, E. H., Clinical Decision Support Systems, In: Shortliffe, E. H. and Cimino, J. J. (eds), Biomedical Informatics: Computer Applications in Health Care and New York: Springer, 2006, pp. 698–736.Google Scholar
- 12.Wyatt, J. and Spiegelhalter, D., Field trials of medical decision-aids: potential problems and solutions. , Proceedings of the Annual Symposium on Computer Application in Medical Care, 1991, pp. 3–7.Google Scholar
- 13.Kassirer J, Wong J, Kopelman R. Learning Clinical Reasoning. Philadelphia: Lippincott Williams and Wilkins; 2009.Google Scholar
- 14.Datena, S., Lifecom DARES Approach to Problem Based Learning and Improvement (Personal Communication of Unpublished White Paper describing the Lifecom DARES System), 2010.Google Scholar