Using kernel density estimation to model surgical procedure duration

Abstract

Estimating the length of surgical cases is an important research topic due to its significant effect on the accuracy of the surgical schedule and operating room (OR) efficiency. Several factors can be considered in the estimation, for example, surgeon, surgeon experience, case type, case start time, etc. Some of these factors are correlated, and this correlation needs to be considered in the prediction model in order to have an accurate estimation. Extensive research exists that identifies the preferred estimation methods for cases that occur frequently. However, in practice, there are many procedure types with limited historical data, which makes it hard to use common statistical methods (such as regression) that rely on a large number of data points. Moreover, only point estimates are typically provided. In this research, kernel density estimation (KDE) is implemented as an estimator for the probability distribution of surgery duration, and a comparison against lognormal and Gaussian mixture models is reported, showing the efficiency of the KDE. In addition, an improvement procedure for the KDE that further enables the algorithm to outperform other methods is proposed. Based on the analysis, KDE can be recommended as an alternative estimator of surgical duration for cases with low volume (or limited historical data).