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Home Research Research Library Machine Learning to Identify Clusters in Family Medicine Diplomate Motivations and Their Relationship to Continuing Certification Exam Outcomes: Findings and Potential Future Implications Machine Learning to Identify Clusters in Family Medicine Diplomate Motivations and Their Relationship to Continuing Certification Exam Outcomes: Findings and Potential Future Implications 2024 Author(s) Price, David W, Wingrove, Peter M, and Bazemore, Andrew W Topic(s) Family Medicine Certification Keyword(s) Continuing Certification Questionnaire Volume Journal of the American Board of Family Medicine Source Journal of the American Board of Family Medicine 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. Methods: Diplomates 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. Results: ML 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. Discussion: This 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. Read More ABFM Research Read all 2019 Research gaps in the organisation of primary healthcare in low-income and middle-income countries and ways to address them: a mixed-methods approach Go to Research gaps in the organisation of primary healthcare in low-income and middle-income countries and ways to address them: a mixed-methods approach 2019 A Certification Board’s Tracking of their Specialty: The American Board of Family Medicine’s Data Collection Strategy Go to A Certification Board’s Tracking of their Specialty: The American Board of Family Medicine’s Data Collection Strategy 2024 Electronic Health Record Usability, Satisfaction, and Burnout for Family Physicians Go to Electronic Health Record Usability, Satisfaction, and Burnout for Family Physicians 2022 Dedicated Time for Education Is Essential to the Residency Learning Environment Go to Dedicated Time for Education Is Essential to the Residency Learning Environment
Author(s) Price, David W, Wingrove, Peter M, and Bazemore, Andrew W Topic(s) Family Medicine Certification Keyword(s) Continuing Certification Questionnaire Volume Journal of the American Board of Family Medicine Source Journal of the American Board of Family Medicine
ABFM Research Read all 2019 Research gaps in the organisation of primary healthcare in low-income and middle-income countries and ways to address them: a mixed-methods approach Go to Research gaps in the organisation of primary healthcare in low-income and middle-income countries and ways to address them: a mixed-methods approach 2019 A Certification Board’s Tracking of their Specialty: The American Board of Family Medicine’s Data Collection Strategy Go to A Certification Board’s Tracking of their Specialty: The American Board of Family Medicine’s Data Collection Strategy 2024 Electronic Health Record Usability, Satisfaction, and Burnout for Family Physicians Go to Electronic Health Record Usability, Satisfaction, and Burnout for Family Physicians 2022 Dedicated Time for Education Is Essential to the Residency Learning Environment Go to Dedicated Time for Education Is Essential to the Residency Learning Environment
2019 Research gaps in the organisation of primary healthcare in low-income and middle-income countries and ways to address them: a mixed-methods approach Go to Research gaps in the organisation of primary healthcare in low-income and middle-income countries and ways to address them: a mixed-methods approach
2019 A Certification Board’s Tracking of their Specialty: The American Board of Family Medicine’s Data Collection Strategy Go to A Certification Board’s Tracking of their Specialty: The American Board of Family Medicine’s Data Collection Strategy
2024 Electronic Health Record Usability, Satisfaction, and Burnout for Family Physicians Go to Electronic Health Record Usability, Satisfaction, and Burnout for Family Physicians
2022 Dedicated Time for Education Is Essential to the Residency Learning Environment Go to Dedicated Time for Education Is Essential to the Residency Learning Environment