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 Challenges Meeting Training Requirements in the Care of Children in Family Medicine Residency Programs: A CERA Study Challenges Meeting Training Requirements in the Care of Children in Family Medicine Residency Programs: A CERA Study 2023 Author(s) Krugman, Scott, Hodo, Laura Nell, Morgan, Zachary J, and Eden, Aimee R Volume Family Medicine Source Family Medicine Background and Objective: In 2014, the Accreditation Council for Graduate Medical Education (ACGME) implemented numeric requirements for family medicine (FM) pediatric patient encounters. Impact on residency programs is unclear. We aimed to identify any difficulties faced by FM program directors (PDs) meeting these numeric requirements. Methods: Questions about pediatric training in family medicine residencies were included in a survey of PDs conducted by the Council of Academic Family Medicine Educational Research Alliance (CERA). We performed univariate analysis of the demographic and program characteristics. We then used χ2 tests of independence to test for bivariate associations between these characteristics and our primary outcome: the most difficult ACGME pediatric care requirement to meet. Results: Most programs reported the hospital as the primary location of training (n=131, 46%) and their family medicine practice (FMP) patient population consisted of over 20% pediatric patients (n=153, 56%). Over 80% of program directors reported challenges meeting FM requirements for the care of children. Challenges meeting pediatric requirements were associated with fewer than 20% FMP patients under 19 years of age (P<.0001), fewer than 50% of core FM faculty caring for sick children (P=.0128), and primary location of pediatric training in a family health center (P=.0006). Conclusion: Difficulty meeting ACGME requirements for the care of children in FM residency programs is common, especially for programs with fewer than 20% FMP patients under 19 years of age. Further research is needed to determine how best to assure FM resident competencies in the care of children and adolescents. 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) Krugman, Scott, Hodo, Laura Nell, Morgan, Zachary J, and Eden, Aimee R Volume Family Medicine Source Family 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