Fraher Erin P, Knapton Andy, Holmes George M
Cecil G. Sheps Center for Health Services Research and Department of Family Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC.
SMAP Limited, Winchester, UK.
Health Serv Res. 2017 Feb;52 Suppl 1(Suppl 1):508-528. doi: 10.1111/1475-6773.12649.
To outline a methodology for allocating graduate medical education (GME) training positions based on data from a workforce projection model.
Demand for visits is derived from the Medical Expenditure Panel Survey and Census data. Physician supply, retirements, and geographic mobility are estimated using concatenated AMA Masterfiles and ABMS certification data. The number and specialization behaviors of residents are derived from the AAMC's GMETrack survey.
We show how the methodology could be used to allocate 3,000 new GME slots over 5 years-15,000 total positions-by state and specialty to address workforce shortages in 2026.
We use the model to identify shortages for 19 types of health care services provided by 35 specialties in 50 states.
The new GME slots are allocated to nearly all specialties, but nine states and the District of Columbia do not receive any new positions.
This analysis illustrates an objective, evidence-based methodology for allocating GME positions that could be used as the starting point for discussions about GME expansion or redistribution.
概述一种基于劳动力预测模型数据分配研究生医学教育（GME）培训岗位的方法。
就诊需求来自医疗支出面板调查和人口普查数据。医生供应、退休情况和地理流动性使用合并的美国医学协会主文件和美国医学专业委员会认证数据进行估算。住院医师的数量和专业行为来自美国医学协会医学院协会（AAMC）的GMETrack调查。
我们展示了该方法如何用于在5年内分配3000个新的GME名额（共15000个岗位），按州和专业分配，以解决2026年的劳动力短缺问题。
我们使用该模型识别50个州35个专业提供的19种医疗服务的短缺情况。
新的GME名额分配给了几乎所有专业，但有9个州和哥伦比亚特区没有获得任何新岗位。
该分析说明了一种客观、基于证据的GME岗位分配方法，可作为关于GME扩展或重新分配讨论的起点。
