Statistical Modeling to Predict Elective Surgery Time

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Abstract
Background: Accurate estimation of operating times is a prerequisite for the efficient scheduling of the operating suite. The authors, in this study, sought to compare surgeons' time estimates for elective cases with those of commercial scheduling software, and to ascertain whether improvements could be made by regression modeling. Methods: The study was conducted at the University of Washington Medical Center in three phases. Phase 1 retrospectively reviewed surgeons' time estimates and the scheduling system's estimates throughout 1 yr. In phase 2, data were collected prospectively from participating surgeons by means of a data entry form completed at the time of scheduling elective cases. Data included the procedure code, estimated operating time, estimated case difficulty, and potential factors that might affect the duration. In phase 3, identical data were collected from five selected surgeons by personal interview. Results: In phase 1, 26 of 43 surgeons provided significantly better estimates than did the scheduling system (P < 0.01), and no surgeon was significantly worse, although the absolute errors were large (34% of 157 min average case length). In phase 2, modeling improved the accuracy of the surgeons' estimates by 11.5%, compared with the scheduling system. In phase 3, applying the model from phase 2 improved the accuracy of the surgeons' estimates by 18.2%. Conclusions: Surgeons provide more accurate time estimates than does the scheduling software as it is used in our institution. Regression modeling effects modest improvements in accuracy. Further improvements would be likely if the hospital information system could provide timely historical data and feedback to the surgeons.