Phoenix Newsletter - October 2025 President’s Message: Enduring Commitments in a Time of Change Read President’s Message: Enduring Commitments in a Time of Change
Home Research Research Library Tectonic shifts are needed in graduate medical education to ensure today’s trainees are prepared to practice as tomorrow’s physicians Tectonic shifts are needed in graduate medical education to ensure today’s trainees are prepared to practice as tomorrow’s physicians 2014 Author(s) Phillips, Robert L, and Bitton, Asaf Topic(s) Education & Training, Role of Primary Care, Achieving Health System Goals, and What Family Physicians Do Keyword(s) Cost Of Care, Graduate Medical Education, Imprinting Of Training, and Shortage Areas Volume Academic Medicine Source Academic Medicine Most U.S. institutions that sponsor graduate medical education (GME) programs are struggling to commit to a non-volume-based care business model while, at the same time, working to sustain or expand a fee-for-service status quo.1 The Association of Academic Health Centers and some of its member institutions contend that there is a viable business case to be made for a population-based care model that seeks to resolve environmental, social, and behavioral determinants of health. As teaching hospitals struggle with these tectonic shifts in their business models and social contracts, they are also contending with how to prepare young physicians for practice in the resulting new models of care. Here, we offer key steps that academic health centers (AHCs) can take to position their GME programs at the leading edge of change. ABFM Research Read all 2019 Family Medicine Residency Graduates’ Preparation for Quality Improvement Leadership Go to Family Medicine Residency Graduates’ Preparation for Quality Improvement Leadership 2022 Multinational primary health care experiences from the initial wave of the COVID-19 pandemic: A qualitative analysis Go to Multinational primary health care experiences from the initial wave of the COVID-19 pandemic: A qualitative analysis 2023 A Comparative Effectiveness Study on Opioid Use Disorder Prediction Using Artificial Intelligence and Existing Risk Models Go to A Comparative Effectiveness Study on Opioid Use Disorder Prediction Using Artificial Intelligence and Existing Risk Models 2024 Measuring Primary Healthcare Spending Go to Measuring Primary Healthcare Spending
Author(s) Phillips, Robert L, and Bitton, Asaf Topic(s) Education & Training, Role of Primary Care, Achieving Health System Goals, and What Family Physicians Do Keyword(s) Cost Of Care, Graduate Medical Education, Imprinting Of Training, and Shortage Areas Volume Academic Medicine Source Academic Medicine
ABFM Research Read all 2019 Family Medicine Residency Graduates’ Preparation for Quality Improvement Leadership Go to Family Medicine Residency Graduates’ Preparation for Quality Improvement Leadership 2022 Multinational primary health care experiences from the initial wave of the COVID-19 pandemic: A qualitative analysis Go to Multinational primary health care experiences from the initial wave of the COVID-19 pandemic: A qualitative analysis 2023 A Comparative Effectiveness Study on Opioid Use Disorder Prediction Using Artificial Intelligence and Existing Risk Models Go to A Comparative Effectiveness Study on Opioid Use Disorder Prediction Using Artificial Intelligence and Existing Risk Models 2024 Measuring Primary Healthcare Spending Go to Measuring Primary Healthcare Spending
2019 Family Medicine Residency Graduates’ Preparation for Quality Improvement Leadership Go to Family Medicine Residency Graduates’ Preparation for Quality Improvement Leadership
2022 Multinational primary health care experiences from the initial wave of the COVID-19 pandemic: A qualitative analysis Go to Multinational primary health care experiences from the initial wave of the COVID-19 pandemic: A qualitative analysis
2023 A Comparative Effectiveness Study on Opioid Use Disorder Prediction Using Artificial Intelligence and Existing Risk Models Go to A Comparative Effectiveness Study on Opioid Use Disorder Prediction Using Artificial Intelligence and Existing Risk Models
