OBJECTIVES: This study positions surgeon gap time, defined as the interval between consecutive surgeries performed by the same surgeon, as a surgeon-level metric of efficiency. Understanding gap time requires accounting for a surgeon's operative workload, yet no objective electronic health record (EHR)-derived measure exists. We conceptualize surgical case demand as an EHR-derived surrogate for operative workload and examine its association with surgeon gap time. MATERIALS AND METHODS: We analyzed 86 480 surgeries in 14 specialties performed between 2020 and 2023 at a US Medical Center. Surgical case demand was operationalized using patient and surgery features and clustered into demand types. Clinical implications were assessed by comparing postoperative care location and length of stay (LOS) across demand types. A classification tree was developed to assign demand types for future cases. Regression models were used to identify predictors of gap time. RESULTS: Clustering identified 3 demand types with distinct postoperative profiles. High-level recovery was required for 5.9%, 6.5%, and 17.2% of demand types 1, 2, and 3 cases, respectively (P < .001), with median LOS of 0.09, 0.97, and 1.80 days (P < .001). Demand type, case priority, surgical specialty, and surgical care location were significant predictors of gap time. DISCUSSION: Surgical case demand serves as an EHR-derived surrogate for operative workload, enabling structured analysis of surgeon gap time and the factors associated with it. CONCLUSION: Surgeon gap time is an objective metric that can be operationalized from EHR data, providing insights for scheduling, resource allocation, and overall health system efficiency.
Akhagbosu et al. (2026) studied this question.