Diplomate Spotlight Opening Doors with Board Certification: A Conversation with Long Standing Diplomate Joseph Cook Read Opening Doors with Board Certification: A Conversation with Long Standing Diplomate Joseph Cook
Phoenix Newsletter - July 2025 Available Now: 2026 5-Year Cycle Registration Read Available Now: 2026 5-Year Cycle Registration
Home Research Research Library Changes in Primary Care Graduate Medical Education Are Not Correlated With Indicators of Need: Are States Missing an Opportunity to Strengthen Their Primary Care Workforce? Changes in Primary Care Graduate Medical Education Are Not Correlated With Indicators of Need: Are States Missing an Opportunity to Strengthen Their Primary Care Workforce? 2017 Author(s) Coutinho, Anastasia J, Klink, Kathleen, Wingrove, Peter M, Petterson, Stephen M, Phillips, Robert L, and Bazemore, Andrew W Volume Academic Medicine Source Academic Medicine PURPOSE: Federal and state graduate medical education (GME) funding exceeds $15 billion annually. It is critical to understand mechanisms to align undergraduate medical education (UME) and GME to meet workforce needs. This study aimed to determine whether states’ primary care GME (PCGME) trainee growth correlates with indicators of need. METHOD: Data from the American Medical Association Physician Masterfile, the Association of American Medical Colleges, the American Association of the Colleges of Osteopathic Medicine, and the U.S. Census were analyzed to determine how changes between 2002 and 2012 in PCGME trainees-a net primary care physician (PCP) production estimate-correlated with state need using three indicators: (1) PCP-to-population ratio, (2) change in UME graduates, and (3) population growth. RESULTS: Nationally, PCGME trainees declined by 7.1% from the net loss of 679 trainees (combined loss of 54 postgraduate year 1 trainees in internal medicine, family medicine, and pediatrics and addition of 625 fellowship trainees in those specialties). The median state PCGME decline was 2.7%. There was no correlation between the percent change in states’ PCGME trainees and PCP-to-population ratio (r = -0.06) or change in UME graduates (r = 0.17). Once adjusted for population growth, PCGME trainees declined by 15.3% nationally; the median state decline was 9.7%. CONCLUSIONS: There is little relationship between PCGME trainee growth and state need indicators. States should capitalize on opportunities to create explicit linkages between UME, GME, and population need; strategically allocate Medicaid GME funds; and monitor the impact of workforce policies and training institution outputs. ABFM Research Read all 2025 Reclaiming Medical Professionalism In An Era Of Corporate Healthcare Go to Reclaiming Medical Professionalism In An Era Of Corporate Healthcare 2025 Leveraging Large Language Models to Advance Certification, Physician Learning, and Diagnostic Excellence Go to Leveraging Large Language Models to Advance Certification, Physician Learning, and Diagnostic Excellence 2025 Validating 8 Area-Based Measures of Social Risk for Predicting Health and Mortality Go to Validating 8 Area-Based Measures of Social Risk for Predicting Health and Mortality 2025 Natural Language Processing Improves Reliable Identification of COVID-19 Compared to Diagnostic Codes Alone Go to Natural Language Processing Improves Reliable Identification of COVID-19 Compared to Diagnostic Codes Alone
Author(s) Coutinho, Anastasia J, Klink, Kathleen, Wingrove, Peter M, Petterson, Stephen M, Phillips, Robert L, and Bazemore, Andrew W Volume Academic Medicine Source Academic Medicine
ABFM Research Read all 2025 Reclaiming Medical Professionalism In An Era Of Corporate Healthcare Go to Reclaiming Medical Professionalism In An Era Of Corporate Healthcare 2025 Leveraging Large Language Models to Advance Certification, Physician Learning, and Diagnostic Excellence Go to Leveraging Large Language Models to Advance Certification, Physician Learning, and Diagnostic Excellence 2025 Validating 8 Area-Based Measures of Social Risk for Predicting Health and Mortality Go to Validating 8 Area-Based Measures of Social Risk for Predicting Health and Mortality 2025 Natural Language Processing Improves Reliable Identification of COVID-19 Compared to Diagnostic Codes Alone Go to Natural Language Processing Improves Reliable Identification of COVID-19 Compared to Diagnostic Codes Alone
2025 Reclaiming Medical Professionalism In An Era Of Corporate Healthcare Go to Reclaiming Medical Professionalism In An Era Of Corporate Healthcare
2025 Leveraging Large Language Models to Advance Certification, Physician Learning, and Diagnostic Excellence Go to Leveraging Large Language Models to Advance Certification, Physician Learning, and Diagnostic Excellence
2025 Validating 8 Area-Based Measures of Social Risk for Predicting Health and Mortality Go to Validating 8 Area-Based Measures of Social Risk for Predicting Health and Mortality
2025 Natural Language Processing Improves Reliable Identification of COVID-19 Compared to Diagnostic Codes Alone Go to Natural Language Processing Improves Reliable Identification of COVID-19 Compared to Diagnostic Codes Alone