Development and Evaluation of an Operative Case Length Prediction Model in Adult Surgical Patients

Objective: To develop a machine learning model that predicts surgical case length and benchmark its performance against an embedded electronic health record (EHR) model. Background: Surgical care accounts for one-third of U.S. healthcare expenditure. Current case length prediction models are generally overly simplistic and inaccurate or too specialized to have a broad impact, contributing to operating room (OR) inefficiency and dissatisfaction for patients and providers. Methods: Retrospective analysis of 55,495 surgical cases performed by 299 surgeons between January 2022 and April 2024 at a metropolitan, quaternary care hospital. The dataset was split temporally for training (46,767 cases) and holdout validation (8728 cases). Three separate machine learning models predicted preprocedure, operative, and postprocedure times using patient and provider characteristics, operation details, and hospital features available at least 1 day before surgery. Approximately 22% of cases lacked historical time averages and relied on procedural time heuristics. Results: The machine learning model significantly outperformed the embedded EHR model, achieving lower root mean squared error (61.0 vs 91.0 minutes; P < 0.01), lower mean average error (39.6 vs 51.8 minutes; P < 0.01), and higher R 2 (0.78 vs 0.50; P < 0.01). The model predicted 213 more cases within ±30 minutes of actual duration. In cases without historical time averages, the model increased cases within ±30 minutes of actual duration (35% vs 29%; P < 0.01). Conclusions: A machine learning model leveraging comprehensive preoperative data significantly improved surgical case length prediction compared to an embedded EHR model. Future implementation has the potential to improve OR efficiency and patient and provider satisfaction.