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-Apr;37(2):279-289. doi: 10.3122/jabfm.2023.230369R1.
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.
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.
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.
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.
机器学习（ML）有可能提高医学专业委员会的效率，但尚未对此进行探讨。我们应用无监督 ML 来识别美国家庭医学委员会（ABFM）院士在实践特征和参与持续认证的动机方面的典型特征，然后研究动机模式与关键重新认证结果之间的关联。
对 2017 年至 2021 年 ABFM 家庭医学持续认证考试调查做出回应的院士选择继续认证的动机。我们使用卡方检验来检验在首次重新认证考试尝试中失败的院士中，不同动机的比例差异。应用无监督 ML 技术生成具有相似实践特征和重新认证动机的医生集群。在控制医生人口统计学变量的情况下，我们使用逻辑回归来检验动机集群对重新认证考试成功的影响，并通过与专家先前开发的分类方案进行比较来验证 ML 集群。
ML 集群在很大程度上再现了专家之前提出的内在/外在框架。然而，所确定的集群实现了对院士更均匀的同质分组。在 ML 和人类集群中，主要出于外在或混合动机的医生的考试失败率低于内在动机的医生。
本研究证明了使用 ML 来补充和增强对委员会认证数据的人为解释的可行性。我们讨论了这项示范研究对专业委员会和医生院士之间相互作用的影响。
