A Methodology for Using Workforce Data to Decide Which Specialties and States to Target for Graduate Medical Education Expansion. (February 2017)

• Record Type: Journal Article
• Authors: Fraher, Erin P.; Knapton, Andy; Holmes, George M.
• Other Names: Washko Michelle guestEditor.; Fennell Mary guestEditor.
• Is Part Of: Health services research. Volume 52(2017)Supplement 1
• Page Start: 508
• Page End: 528
• Publication Date: 2017-02
• Subjects: Health policy/politics/law/regulation -- health workforce distribution/incomes/training -- medicare; Medical care -- Periodicals; Medical care -- Evaluation -- Periodicals; Hospital care -- Periodicals; Health services administration -- Periodicals; 362
• DOI: 10.1111/1475-6773.12649
• Languages: English
• ISSNs: 0017-9124

Abstract

Abstract : Objective: To outline a methodology for allocating graduate medical education (GME) training positions based on data from a workforce projection model. Data Sources: 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. Design: 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. Extraction Methods: We use the model to identify shortages for 19 types of health care services provided by 35 specialties in 50 states. Principal Findings: The new GME slots are allocated to nearly all specialties, but nine states and the District of Columbia do not receive any new positions. Conclusions: 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.