A machine learning approach for optimizing waiting times in a hand surgery operation center

Abstract

For patients scheduled for surgery, long waiting times are unpleasant. However, scheduling that is too patient-oriented can lead to friction losses in the operating room and waiting times for the medical personnel. We have conducted an analysis of historical hand surgery data to improve forecasting of hand surgery durations, optimize operation room scheduling for physicians and patients and reduce overall waiting times. Several models have been evaluated to forecast surgery durations. A quantile-based approach based on the distribution of surgery durations has been tested in a scheduling simulation. This approach has indicated possibilities to gradually balance waiting times between patients and medical personnel. Within a field trial, a trained regression model has been successfully deployed in a hand surgery operation center.