In a master surgery scheduling (MSS) problem, a hospital's operating room (OR) capacity is assigned to different medical specialties. This task is critical since the risk of assigning too much or too little OR time to a specialty is associated with overtime or deficit hours of the staff, deferral or delay of surgeries, and unsatisfied—or even endangered—patients. Most MSS approaches in the literature focus only on the OR while neglecting the impact on downstream units or reflect a simplified version of the real‐world situation. We present the first prediction model for the integrated OR scheduling problem based on machine learning. Our three‐step approach focuses on the intensive care unit (ICU) and reflects elective and urgent patients, inpatients and outpatients, and all possible paths through the hospital. We provide an empirical evaluation of our method with surgery data for Universitätsklinikum Augsburg, a German tertiary care hospital with 1700 beds. We show that our model outperforms a state‐of‐the‐art model by 43% in number of predicted beds. Our model can be used as supporting tool for hospital managers or incorporated in an optimization model. Eventually, we provide guidance to support hospital managers in scheduling surgeries more efficiently.
With increasing organizational and financial pressure on hospitals, each individual surgical treatment has to be reviewed and planned thoroughly. Apart from the expensive operating room facilities, proper staffing and planning of downstream units, like the wards or the intensive care units (ICUs), should be considered as well. In this article, we outline the relationship between a master surgery schedule (MSS), i.e., the assignment of surgical blocks to medical specialties, and the bed demand in the downstream units using an analytical model. By using historical data retrieved from the clinical information system and a patient flow model, we applied a recently developed algorithm for predicting bed demand based on the MSSs for patients of 3 surgical subspecialties of a hospital. Simulations with 3 different MSSs were performed. The impact on the required amount of beds in the downstream units was analyzed. We show the potential improvements of the current MSS considering 2 main goals: leveling workload among days and reduction of weekend utilization. We discuss 2 different MSSs, one decreasing the weekend ICU utilization by 20% and the other one reducing maximum ward bed demand by 7%. A test with 12 months of real-life data validates the results. The application of the algorithm provides detailed insights for the hospital into the impact of MSS designs on the bed demand in downstream units. It allowed creating MSSs that avoid peaks in bed demand and high weekend occupancy levels in the ICU and the ward.
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