Statistical method to evaluate management strategies to decrease variability in operating room utilization: Application of linear statistical modeling and Monte Carlo simulation to operating room management

Background: Operating room (OR) managers seeking to maximize labor producti vity in their OR suite may attempt to reduce day-to-day variability in hour s of OR time for which there are staff but for which there are no cases ("u nderutilized time"). The authors developed a method to analyze data from su rgical services information systems to evaluate which management interventi ons can most effectively decrease variability in underutilized time. Methods: The method uses seven summary statistics of daily workload in a su rgical suite: daily allocated hours of OR time, estimated hours of elective cases, actual hours of elective cases, estimated hours of add-on cases, ac tual hours of add-on cases, hours of turnover time, and hours of underutili zed time. Simultaneous linear statistical equations (a structural equation model) specify the relationship among these variables. Estimated coefficien ts are used in Monte Carlo simulations. Results: The authors applied the analysis they developed to two OR suites: a tertiary care hospital's suite and an ambulatory surgery center. At both suites, the most effective strategy to decrease variability in underutilize d OR time was to choose optimally the day on which to do each elective case so as to best fill the allocated hours. Eliminating all (1) errors in pred icting how long elective or add-on cases would last, (2) variability in tur nover or delays between cases, or (3) day-to-day variation in hours of add- on cases would have a small effect. Conclusions: This method can be used for decision support to determine how to decrease variability in underutilized OR time.