Development and Validation of a Model to Predict Milestone Levels Based on Entrustable Professional Activity Entrustment-Supervision Levels.

IF 5.3 2区 教育学 Q1 EDUCATION, SCIENTIFIC DISCIPLINES
Daniel J Schumacher, Benjamin Kinnear, Catherine Michelson, David A Stewart, Bruce E Herman, Adam D Wolfe, Ariel Winn, Jaclyn Boulais, David A Turner, Heather B Howell, Alan Schwartz
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引用次数: 0

Abstract

Purpose: Residency programs are increasingly interested in or required to assess residents using Accreditation Council for Graduate Medical Education (ACGME) milestones and specialty-defined entrustable professional activities (EPAs). The authors aimed to develop a model to predict individual residents' milestone levels based on their assigned EPA entrustment-supervision levels.

Method: During 3 academic years from 2021 to 2024, the authors conducted a multisite prospective cohort study at 48 U.S. pediatric residency programs. Programs collected entrustment-supervision levels for the 17 general pediatrics EPAs and milestone levels for the 22 ACGME pediatric milestones for every resident biannually. EPA and milestone ratings were assigned by clinical competency committees. The first 4 of 6 biannual data reporting cycles were used to fit multilevel structural equation models and produce equations to generate, for each resident, predicted milestone levels based on EPA entrustment-supervision levels. They developed 2 models: one using 17 EPAs and one using 12 EPAs.

Results: Data used for modeling represented 4,328 residents, with 164,886 total entrustment-supervision levels across the general pediatrics EPAs and 243,949 total milestone levels across the pediatric milestones. The fit of the round 1 to 4 model to the round 1 to 4 data (internal prediction) was excellent for both models, with comparative fit indexes of 0.982 (17 EPAs) and 0.981 (12 EPAs). The ability of the round 1 to 4 model to predict milestones for reporting cycles 5 and 6 (external prediction) was similar to the internal predictions, with correlation coefficients of 0.68 (17 EPAs) and 0.69 (12 EPAs) for round 5 and 0.72 (17 EPAs) and 0.68 (12 EPAs) for round 6.

Conclusions: This study demonstrates a strong ability to predict milestone levels based on EPA entrustment-supervision levels in a manner that enables meaningful use of EPAs and milestones in assessment efforts at residency programs.

基于可委托专业活动委托监督水平的里程碑水平预测模型的开发与验证。
目的:住院医师项目对使用研究生医学教育认证委员会(ACGME)里程碑和专业定义的可信赖专业活动(EPAs)评估住院医师越来越感兴趣或被要求。作者的目的是开发一个模型,以预测个人居民的里程碑水平基于他们指定的EPA委托监督水平。方法:在2021年至2024年的3个学年中,作者在48个美国儿科住院医师项目中进行了一项多地点前瞻性队列研究。项目每半年收集17个普通儿科EPAs的委托监督水平和22个ACGME儿科里程碑的里程碑水平。EPA和里程碑评级由临床能力委员会分配。6个两年一次的数据报告周期中的前4个用于拟合多层结构方程模型,并生成方程,以根据EPA委托监督水平为每个居民生成预测的里程碑水平。他们开发了两种模型:一种使用17个epa,另一种使用12个epa。结果:用于建模的数据代表了4,328名居民,在普通儿科EPAs中有164,886个总委托监督水平,在儿科里程碑中有243,949个总里程碑水平。第1 ~ 4轮模型与第1 ~ 4轮数据(内部预测)的拟合均较好,比较拟合指数分别为0.982 (17 EPAs)和0.981 (12 EPAs)。第1到第4轮模型预测报告周期5和6的里程碑(外部预测)的能力与内部预测相似,第5轮的相关系数为0.68(17个EPAs)和0.69(12个EPAs),第6轮的相关系数为0.72(17个EPAs)和0.68(12个EPAs)。结论:本研究证明了基于EPA委托监督水平预测里程碑水平的强大能力,从而使EPA和里程碑在住院医师计划的评估工作中得以有意义的使用。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Academic Medicine
Academic Medicine 医学-卫生保健
CiteScore
7.80
自引率
9.50%
发文量
982
审稿时长
3-6 weeks
期刊介绍: Academic Medicine, the official peer-reviewed journal of the Association of American Medical Colleges, acts as an international forum for exchanging ideas, information, and strategies to address the significant challenges in academic medicine. The journal covers areas such as research, education, clinical care, community collaboration, and leadership, with a commitment to serving the public interest.
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