A predictive and prescriptive healthcare analytics framework for operating room planning and scheduling

This study presents a data-driven framework for operating room (OR) planning and scheduling that integrates machine learning-based surgery duration prediction with an integer linear programming (ILP) scheduling model under realistic hospital constraints. Efficient OR management remains a major operational challenge due to the stochastic nature of surgical durations and the complexity of coordinating hospital resources. The proposed approach is based on a rolling-horizon planning methodology for elective surgeries. Surgery durations are predicted using machine learning models trained on a large real-world dataset containing more than 90,000 surgeries across 540 procedures and 744 surgeons. The predicted durations are then incorporated into an ILP model that considers surgeon availability, equipment limitations, and OR-specific operational constraints. The framework is evaluated against the hospital’s current duration-estimation approach as well as a benchmark schedule based on actual surgery durations. Results show substantial operational improvements, including a reduction of more than 60% in unused OR capacity with only a modest increase in overtime. In addition, the gap from the benchmark schedule is reduced by 71% compared with the hospital’s current estimation method. The results further demonstrate an important operational trade-off: a modest increase in overtime can lead to a substantial reduction in unused OR capacity. This trade-off provides hospital managers with practical flexibility in balancing OR utilization and operational risk according to institutional priorities. Overall, the study highlights the value of integrating predictive analytics and optimization methods to support data-driven OR planning in large-scale hospital environments.