Track: Healthcare Operations and Healthcare Engineering

Abstract

Surgical operating rooms are critical in the total hospital costs, while surgical care accounts for one-third of hospital costs. Thus, successful and impression operating room management and scheduling can bring significant benefits. In this study, we develop Operating Room (OR) scheduling problem integrated with a Post-Anesthesia Care Unit (PACU) by considering emergency surgeries during the COVID-19 outbreak. Accurate prediction of surgery duration and required PACU time for each surgery are critical for operating room scheduling. Due to the inherent uncertainty in surgery duration and PACU time, we develop supervised machine learning to estimate surgery duration and PACU time. Finally, based on discrete event simulation, we compare our proposed surgery scheduling model to the available scheduling by using statics and data from Montreal’s hospitals. We could show that our scheduling model can significantly increase operating room utilization with the issue of PACU congestion.