BACKGROUND: Operating room (OR) managers seeking to maximize labor productivity in their OR suite may attempt to reduce day-today variability in hours of OR time for which there are staff but for which there are no cases ("underutilized time"). The authors developed a method to analyze data from surgical services information systems to evaluate which management interventions can most effectively decrease variability in underutilized time. METHODS: The method uses seven summary statistics of daily workload in a surgical suite: daily allocated hours of OR time, estimated hours of elective cases, actual hours of elective cases, estimated hours of add-on cases, actual hours of add-on cases, hours of turnover time, and hours of underutilized time. Simultaneous linear statistical equations (a structural equation model) specify the relationship among these variables. Estimated coefficients 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 underutilized 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 predicting how long elective or add-on cases would last, (2) variability in turnover 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.
BACKGROUND: Operating room (OR) managers seeking to maximize labor productivity in their OR suite may attempt to reduce day-today variability in hours of OR time for which there are staff but for which there are no cases ("underutilized time"). The authors developed a method to analyze data from surgical services information systems to evaluate which management interventions can most effectively decrease variability in underutilized time. METHODS: The method uses seven summary statistics of daily workload in a surgical suite: daily allocated hours of OR time, estimated hours of elective cases, actual hours of elective cases, estimated hours of add-on cases, actual hours of add-on cases, hours of turnover time, and hours of underutilized time. Simultaneous linear statistical equations (a structural equation model) specify the relationship among these variables. Estimated coefficients 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 underutilized 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 predicting how long elective or add-on cases would last, (2) variability in turnover 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.