Predicting Procedure Duration to Improve Scheduling of Elective Surgery

Abstract

The accuracy of surgery schedules depends on precise estimation of surgery duration. Current approaches employed by hospitals include historical averages and surgical team estimates which are not accurate enough. The inher- ent complexity of surgery duration estimation contributes significantly to in- creased procedure cancellations and reduced utilisation of already encumbered resources. In this study we employ administrative and perioperative data from a large metropolitan hospital to investigate the performance of different machine learning approaches for improving procedure duration estimation. The predic- tive modelling approaches applied include linear regression (LR), multivariate adaptive regression splines (MARS), and random forests (RF). Cross validation results reveal that the random forest model outperforms other methods, reduc- ing mean absolute percentage error by 28% when compared to current hospital estimation approaches.