Study by Michael L. Barnett et al in JAMA: “Is a collective intelligence approach of pooling multiple clinician and medical student diagnoses associated with improvement in diagnostic accuracy in online, structured clinical cases?
Findings This cross-sectional study analyzing data from the Human Diagnosis Project found that, across a broad range of medical cases and common presenting symptoms, independent differential diagnoses of multiple physicians combined into a weighted list significantly outperformed diagnoses of individual physicians with groups as small as 2, and accuracy increased with larger groups up to 9 physicians. Groups of nonspecialists also significantly outperformed individual specialists solving cases matched to the individual specialist’s specialty….
Main Outcomes and Measures The primary outcome was diagnostic accuracy, assessed as a correct diagnosis in the top 3 ranked diagnoses for an individual; for groups, the top 3 diagnoses were a collective differential generated using a weighted combination of user diagnoses with a variety of approaches. A version of the McNemar test was used to account for clustering across repeated solvers to compare diagnostic accuracy.
Conclusions and Relevance A collective intelligence approach was associated with higher diagnostic accuracy compared with individuals, including individual specialists whose expertise matched the case diagnosis, across a range of medical cases. Given the few proven strategies to address misdiagnosis, this technique merits further study in clinical settings….(More)”.