Promotion in Place: A Model for Competency-Based, Time-Variable Graduate Medical Education

Abstract

Problem Competency-based medical education is increasingly regarded as a preferred framework for physician training, but implementation is limited. U.S. residency programs remain largely time based, with variable assessments and limited opportunities for individualization. Gaps in graduates’ readiness for unsupervised care have been noted across specialties. Logistical barriers and regulatory requirements constrain movement toward competency-based, time-variable (CBTV) graduate medical education (GME), despite its theoretical benefits. Approach The authors describe a vision for CBTV-GME and an implementation model that can be applied across specialties. Termed “Promotion in Place” (PIP), the model relies on enhanced assessment, clear criteria for advancement, and flexibility to adjust individuals’ responsibilities and time in training based on demonstrated competence. PIP allows a resident’s graduation to be advanced or delayed accordingly. Residents deemed competent for early graduation can transition to attending physician status within their training institution and benefit from a period of “sheltered independence” until the standard graduation date. Residents who need extended time to achieve competency have graduation delayed to incorporate additional targeted education. Outcomes A proposal to pilot the PIP model of CBTV-GME received funding through the American Medical Association’s “Reimagining Residency” initiative in 2019. Ten of 46 residency programs in a multihospital system expressed interest and pursued initial planning. Seven programs withdrew for reasons including program director transitions, uncertainty about resident reactions, and the COVID-19 pandemic. Three programs petitioned their specialty boards for exemptions from time-based training. One program was granted the needed exemption and launched a PIP pilot, now in year 4, demonstrating the feasibility of implementing this model. Implementation tools and templates are described. Next Steps Larger-scale implementation with longer-term assessment is needed to evaluate the impact and generalizability of this CBTV-GME model.