Statistical Method for Predicting When Patients Should Be Ready on the Day of Surgery

Publisher Website
Google Scholar
Abstract
Background: Previously, mathematical theory was developed for determining when a patient should be ready for surgery on the day of surgery. To apply this theory, a method is needed to predict the earliest start time of the case. Methods: The authors calculated a time estimate such that the probability is 0.05 that the preceding case in the patient's operating room (OR) will be finished before the patient is ready for surgery. This implies there will be a 5% risk of OR personnel being idle and waiting for the patient. This 0. 05 value was chosen by considering the relative cost valuation of an average patient's time to that of an average surgical team based on national salary data. Case duration data from a surgical services information system were used to test different statistical methods to estimate earliest start times. Results: Simulations found that 0.05 prediction bounds, calculated assuming case durations followed log-normal distributions, achieved actual risks for the OR staff to wait for patients of 0.050 to 0.053 (SEM = 0.001). Nonparametric prediction bounds performed no better than the parametric method. Having patients ready a fixed number of hours before the scheduled starts of their operations is not reliable. If the preceding case in an OR had been underway for 0.5 to 1.5 h, the parametric 0.05 prediction bounds for the time remaining achieved actual risks for OR staff waiting of 0.055 to 0.058 (SEM = 0.001). Conclusion: The earliest start time of a case can be estimated using the 0.05 prediction bound for the duration of the preceding case. The authors show 0.05 prediction bounds can be estimated accurately assuming that case durations follow log-normal distributions.