Data-Driven Surgical Duration Prediction Model for Surgery Scheduling: A Case-Study for a Practice-Feasible Model in a Public Hospital

Abstract

Hospitals have been trying to improve the utilization of operating rooms as it affects patient satisfaction, surgery throughput, revenues and costs. Surgical prediction model which uses post-surgery data often requires high-dimensional data and contains key predictors such as surgical team factors which may not be available during the surgical listing process. Our study considers a two-step data-mining model which provides a practical, feasible and parsimonious surgical duration prediction. Our model first leverages on domain knowledge to provide estimate of the first surgeon rank (a key predicting attribute) which is unavailable during the listing process, then uses this predicted attribute and other predictors such as surgical team, patient, temporal and operational factors in a tree-based model for predicting surgical durations. Experimental results show that the proposed two-step model is more parsimonious and outperforms existing moving averages method used by the hospital. Our model bridges the research-to-practice gap by combining data analytics with expert’s inputs to develop a deployable surgical duration prediction model for a real-world public hospital.