Transforming Orthopaedic Trauma Care: Forecasting Operating Room Demand by Harnessing Time-Series Analysis and Machine Learning

The authors aim to improve the efficiency of operating room (OR) scheduling in trauma centers by developing time-series and machine learning models to accurately forecast daily orthopaedic trauma caseloads. By integrating historical data with external factors, their model significantly outperforms traditional forecasting methods, achieving a mean absolute error of 1.80 hours compared to higher errors from rolling averages and intuitive models. This predictive framework is designed to enhance resource allocation and minimize surgical delays in trauma care settings.