Machine Learning to Identify Clusters in Family Medicine Diplomate Motivations and Their Relationship to Continuing Certification Exam Outcomes: Findings and Potential Future Implications.

Journal: Journal of the American Board of Family Medicine : JABFM
PMID: 38740475

Abstract

BACKGROUND: The 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.

Authors

  • David W Price
    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).
  • Peter Wingrove
    University of Pittsburgh, School of Medicine, Pittsburgh, Pennsylvania pmw27@pitt.edu.
  • Andrew Bazemore
    Robert Graham Center, Washington, DC.

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