Feasibility of Algorithmic Analysis of Resident General Anesthesia Case Experience Using Multicenter Electronic Health Record Registry Data

Algorithmic analysis of electronic health record (EHR) data offers an objective, scalable approach to quantifying resident clinical experience. We tested whether Multicenter Perioperative Outcomes Group registry data could be used to accurately determine which pseudonymized IDs correspond to graduated anesthesiology residents. Algorithmic determination was compared with rosters of 338 graduated residents from seven residency programs. The algorithm demonstrated 91% sensitivity and 97% positive predictive value for determining which IDs are of graduated residents. The algorithm was applied across 29 institutions to analyze resident general anesthesia cases, demonstrating the feasibility of multicenter EHR registry data use in graduate medical education.