Study Objective: To determine whether using only previous cases' surgical times for predicting accurately surgical times of future cases is likely to reduce the average length of time cases finish late (after their scheduled finish times).Design: Computer simulation.Measurements and Main Results: Data from an operating room (OR) information system for two surgical suites were analyzed. For each case performed in fiscal year 1996, we searched backward for 1 year and counted the number of previous cases that were the same type of procedure performed by the same surgeon. Then, for each suite, surgical times were fitted to a statistical model estimating the effect of the type of procedure and who the surgeon was on surgical time. The estimated 'variance components' were used in Monte-Carlo computer simulations to evaluate whether a hypothetical increase in the number of previous cases available to estimate the next case's surgical time would improve scheduling accuracy. Predictions of how long newly scheduled cases should take were impaired because 36.5% ± 0.4% (mean ± SE) of cases at a tertiary surgical suite and 28.6% ± 0.7% of cases at an ambulatory surgery center did not have any cases in the previous year with the same procedure type and surgeon. Computer simulation was used to generate additional hypothetical cases. Using this data, even having many previous cases on which to base predictions of future surgical times would only decrease the average length of time that cases finish late by a few minutes.Conclusion: An OR manager considering using only historical surgical times to estimate future surgical times should first investigate, using data from their own surgical suite, what percentage of cases do not have historical data. Even if there are sufficient historical data to estimate future surgical times accurately, relying solely on historical times is probably an ineffective strategy to have future cases finish on time. Copyright (C) 1999 Elsevier Science Inc.
- Monte-Carlo simulation
- Operating room: economics of, management of
- Random effect
- Variance component
ASJC Scopus subject areas
- Anesthesiology and Pain Medicine