The operating room is a major cost and revenue center. More effective operating room management and scheduling can provide significant benefits. The post-anesthesia care unit (PACU), where patients go to recover after their surgical procedures, is at times a bottleneck. If the PACU reaches capacity, operating room procedures are delayed or canceled. We present a data-driven approach to determine the order of procedures in the operating rooms that minimizes the likelihood of PACU holds. Specifically, we use a machine learning approach to estimate the required PACU time for each type of surgical procedure; we develop and solve integer programming models to schedule procedures in the operating rooms so as to minimize maximum PACU occupancy; and we use discrete event simulation to compare our optimized schedule to the existing schedule.
Using data from Lucile Packard Children’s Hospital Stanford, we show that the scheduling system can significantly reduce operating room delays caused by PACU congestion while still keeping operating room utilization high: simulation of the second half of 2016 shows that our model could have reduced total PACU holds by 76% without decreasing operating room utilization. We are currently working on implementing the scheduling system at the hospital.