Efficient management of operating room (OR) schedules is important as the OR is the largest cost and revenue center in a hospital and can substantially impact its staffing and finances. A major problem associated with developing OR schedules for elective surgeries is the schedule disruption from uncertainty inherent in the duration of surgical services. Another problem is the cascaded impact on overall system performance of facilities and resources upstream and downstream to the OR. Using a manufacturing system analytical approach, the peri-operative process is modeled as a transfer line with three machines and two buffers by a discrete time Markov chain. Uncertain surgical and recovery duration is quantified probabilistically and incorporated in the Markov chain model with multistate geometrical machines. Model predictive control (MPC) to pace patient release into the ORs is then applied to control system transient performance. With this model and empirical studies of surgery and recovery duration, guidance can be given to OR managers on how to dynamically schedule and reschedule patients throughout an OR's day that minimizes cost for a given workload. The proposed predictive control model can also control other transient performance metrics such as OR and recovery room (RR) utilization, patient flow, and cost.

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