• David W Price, Peter Wingrove, and Andrew Bazemore.
• From the American Board of Family Medicine, Lexington, KY (DWP, PW, AB); Department of Family Medicine, University of Colorado Anschutz School of Medicine, Aurora, CO (DWP); Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh PA (AB).
• J Am Board Fam Med. 2024 Mar 1; 37 (2): 279289279-289.

BackgroundThe potential for machine learning (ML) to enhance the efficiency of medical specialty boards has not been explored. We applied unsupervised ML to identify archetypes among American Board of Family Medicine (ABFM) Diplomates regarding their practice characteristics and motivations for participating in continuing certification, then examined associations between motivation patterns and key recertification outcomes.MethodsDiplomates responding to the 2017 to 2021 ABFM Family Medicine continuing certification examination surveys selected motivations for choosing to continue certification. We used Chi-squared tests to examine difference proportions of Diplomates failing their first recertification examination attempt who endorsed different motivations for maintaining certification. Unsupervised ML techniques were applied to generate clusters of physicians with similar practice characteristics and motivations for recertifying. Controlling for physician demographic variables, we used logistic regression to examine the effect of motivation clusters on recertification examination success and validated the ML clusters by comparison with a previously created classification schema developed by experts.ResultsML clusters largely recapitulated the intrinsic/extrinsic framework devised by experts previously. However, the identified clusters achieved a more equal partitioning of Diplomates into homogenous groups. In both ML and human clusters, physicians with mainly extrinsic or mixed motivations had lower rates of examination failure than those who were intrinsically motivated.DiscussionThis study demonstrates the feasibility of using ML to supplement and enhance human interpretation of board certification data. We discuss implications of this demonstration study for the interaction between specialty boards and physician Diplomates.© Copyright 2024 by the American Board of Family Medicine.

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