Tillman, Aimee L.

Abstract Background: The dynamic nature of surgical demand poses significant challenges in planning and scheduling resources to staff the surgical suite. Uncertainty in workload often leads to conservative upstream resource allocation, resulting in staffing misallocation. We propose a novel metric, the daily maximum number of open operating rooms (ORs), as an interpretable resource demand proxy to support staff planning. Methods: Using historical surgical data from Mayo Clinic in Rochester, Minnesota, from May 5, 2018 through October 8, 2023 (332,904 surgical cases), we developed a deep learning framework based on a wide-and-deep architecture to predict the daily maximum number of open ORs. The model incorporates features capturing historic OR use trends, seasonality effects, and anesthesia division–specific characteristics. Predictions are generated for forecast horizons of up to 90 business days. Results: Our results demonstrate strong predictive performance, with campus-level root mean square error values ranging from 5 to 7 available ORs across all forecast horizons. Validation across 9 anesthesia divisions confirms the frame- work’s capability to capture complex, nonlinear dependencies within longitudinal surgical datasets. Conclusions: This study introduces a novel operational metric and a robust predictive framework for perioperative resource management. By accurately fore- casting daily maximum open ORs, health care managers can better align staff capacity with anticipated demand, potentially enhancing staff use and improving the patient care experience by minimizing delays.