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IMPORTANCE Diagnostic errors are common and harmful, but difficult to define and measure. Measurement of diagnostic errors often depends on retrospective medical record reviews, frequently resulting in reviewer disagreement. OBJECTIVES We aimed to test the accuracy of an instrument to help detect presence or absence of diagnostic error through record reviews. DESIGN We gathered questions from several previously used instruments for diagnostic error measurement, then developed and refined our instrument. We tested the accuracy of the instrument against a sample of patient records (n = 389), with and without previously identified diagnostic errors (n = 129 and n = 260, respectively). RESULTS The final version of our instrument (titled Safer Dx Instrument) consisted of 11 questions assessing diagnostic processes in the patient–provider encounter and a main outcome question to determine diagnostic error. In comparison with the previous sample, the instrument yielded an overall accuracy of 84 %, sensitivity of 71 %, specificity of 90 %, negative predictive value of 86 %, and positive predictive value of 78 %. All 11 items correlated significantly with the instrument’s error outcome question (all p values ≤ 0.01). Using factor analysis, the 11 questions clustered into two domains with high internal consistency (initial diagnostic assessment, and performance and interpretation of diagnostic tests) and a patient factor domain with low internal consistency (Cronbach’s alpha coefficients 0.93, 0.92, and 0.38, respectively). CONCLUSIONS The Safer Dx Instrument helps quantify the likelihood of diagnostic error in primary care visits, achieving a high degree of accuracy for measuring their presence or absence. This instrument could be useful to identify high-risk cases for further study and quality improvement. Electronic supplementary material The online version of this article (doi:10.1007/s11606-016-3601-x) contains supplementary material, which is available to authorized users.

BACKGROUND Clinical decision support systems can improve medical diagnosis and reduce diagnostic errors. Older systems, however, were cumbersome to use and had limited success in identifying the correct diagnosis in complicated cases. OBJECTIVE To measure the sensitivity and speed of “Isabel” (Isabel Healthcare Inc., USA), a new web-based clinical decision support system designed to suggest the correct diagnosis in complex medical cases involving adults. METHODS We tested 50 consecutive Internal Medicine case records published in the New England Journal of Medicine. We first either manually entered 3 to 6 key clinical findings from the case (recommended approach) or pasted in the entire case history. The investigator entering key words was aware of the correct diagnosis. We then determined how often the correct diagnosis was suggested in the list of 30 differential diagnoses generated by the clinical decision support system. We also evaluated the speed of data entry and results recovery. RESULTS The clinical decision support system suggested the correct diagnosis in 48 of 50 cases (96%) with key findings entry, and in 37 of the 50 cases (74%) if the entire case history was pasted in. Pasting took seconds, manual entry less than a minute, and results were provided within 2–3 seconds with either approach. CONCLUSIONS The Isabel clinical decision support system quickly suggested the correct diagnosis in almost all of these complex cases, particularly with key finding entry. The system performed well in this experimental setting and merits evaluation in more natural settings and clinical practice.