Real-time estimation of surgical procedure duration

Abstract

Efficiency in the Operating Room (OR) is a topic of growing interest. Planning of care is a crucial element to ensure optimal use of the ORs. Currently, OR scheduling is considered as a complex task based on predictions of surgery duration. The latter are often based on average times, but turn out to be inaccurate in practice because of various factors (such as complexity, patient's characteristics, unexpected events, etc). The aim of this study is to develop a prediction system that estimates in real-time the remaining duration of a surgical procedure. The prediction system was based on monitoring the progress of a procedure by recording the activation of a single piece of equipment in the OR, the electrosurgical device. Support Vector Machines was then used as a classifier to predict the remaining surgical procedure duration and thereby the optimal timing to start preparing the next patient for surgery. The classifier was trained with data on the activation of the electrosurgical device during 55 laparoscopic cholecystectomies. The performance tests showed a mean error rate about 0.2, which means that about 80% of the procedures were classified correctly. The real-time prediction system is a promising tool to improve OR planning and decrease unnecessary patients' waiting times.