STATISTICAL MODELING TO PREDICT ELECTIVE SURGERY TIME - COMPARISON WITH A COMPUTER SCHEDULING SYSTEM AND SURGEON-PROVIDED ESTIMATES

Background: Accurate estimation of operating times is a prerequisite f or the efficient scheduling of the operating suite, The authors, in th is study, sought to compare surgeons' time estimates for elective case s with those of commercial scheduling software, and to ascertain wheth er improvements could be made by regression modeling. Methods: The stu dy was conducted at the University of Washington Medical Center in thr ee phases. Phase 1 retrospectively reviewed surgeons' time estimates a nd 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 mere collected from five selected surgeons by personal interview. Results: In Phase 1, 26 of 43 surgeons provide d significantly better estimates than did the scheduling system (P < 0 .01), and no surgeon was significantly worse, although the absolute er rors were large (34% of 157 min average case length). In phase 2, mode ling improved the accuracy of the surgeons' estimates by 11.5%, compar ed with the scheduling system. In phase 3, applying the model from pha se 2 improved the accuracy of the surgeons' estimates by 18.2%. Conclu sions: Surgeons provide more accurate time estimates than does the sch eduling software as it is used in our institution. Regression modeling effects modest improvements in accuracy. Further improvements would b e likely if the hospital information system could provide timely histo rical data and feedback to the surgeons.