Does case-mix classification affect predictions? A machine learning algorithm for surgical duration estimation

Abstract

Surgical management has a direct impact on the quality treatment of patients and the economic efficiency of hospitals. Surgeries are managed in hospitals according to the duration of surgery reported by the surgeons. Surgical management efficiency would improve if the duration could be predicted accurately. This study focuses on predicting the individual duration in surgical management. The data on 9567 surgical cases from the National Cancer Center Hospital Japan East illustrates hospital characteristics and forecasts the duration requirements using a machine learning algorithm. We obtain an adjusted coefficient of determination exceeding 0.7 and show the relationship between the hospital characteristics and duration in a case-mix classification framework. We also discuss the relationship between data characteristics and the prediction accuracy of machine learning. This study supports operating room managers when they predict surgical duration by machine learning in ascertaining data characteristics, understanding the prediction accuracy of machine learning, and predicting surgical duration more accurately.

Keywords

• Machine learning
• Case-mix classification
• Random forest
• Surgical duration estimation
• Predictive analytics