• John E Delzell, Heidi Chumley, Russell Webb, Swapan Chakrabarti, and Anju Relan.
• University of Kansas School of Medicine, 3901 Rainbow Blvd, Mailstop 4010, Kansas City, KS 66160, USA. jdelzell@kumc.edu
• Adv Health Sci Educ Theory Pract. 2009 Dec 1; 14 (5): 697-711.

AbstractDiagnostic errors are an important source of medical errors. Problematic information-gathering is a common cause of diagnostic errors among physicians and medical students. The objectives of this study were to (1) determine if medical students' information-gathering patterns formed clusters of similar strategies, and if so (2) to calculate the percentage of incorrect diagnoses in each cluster. A total of 141 2nd year medical students completed a computer case simulation. Each student's information-gathering pattern included the sequence of history, physical examination, and ancillary testing items chosen from a predefined list. We analyzed the patterns using an artificial neural network and compared percentages of incorrect diagnoses among clusters of information-gathering patterns. We input patterns into a 35 x 35 self organizing map. The network trained for 10,000 epochs. The number of students at each neuron formed a surface that was statistically smoothed into clusters. Each student was assigned to one cluster, the cluster that contributed the largest value to the smoothed function at the student's location in the grid. Seven clusters were identified. Percentage of incorrect diagnoses differed significantly among clusters (Range 0-42%, Chi (2) = 13.62, P = .034). Distance of each cluster from the worst performing cluster was used to rank clusters. This rank was compared to rank determined by percentage incorrect. We found a high positive correlation (Spearman Correlation = .893, P = .007). Clusters closest to the worst performing cluster had the highest percentages of incorrect diagnoses. Patterns of information-gathering were distinct and had different rates of diagnostic error.

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