Development, Deployment, and Implementation of a Machine Learning Surgical Case Length Prediction Model and Prospective Evaluation.

Abstract

ObjectiveImplement a machine learning model using only the restricted data available at case creation time to predict surgical case length for multiple services at different locations.BackgroundThe Operating Room (OR) is one of the most expensive resources in a health system, estimated to cost $22-133 per minute and generate about 40% of the hospital revenue. Accurate prediction of surgical case length is necessary for efficient scheduling and cost-effective utilization of the OR and other resources.MethodsWe introduced a similarity cascade to capture the complexity of cases and surgeon influence on the case length and incorporated that into a gradient boosting machine learning model. The model loss function was customized to improve the balance between over- and under-prediction of the case length. A production pipeline was created to seamlessly deploy and implement the model across our institution.ResultsThe prospective analysis showed that the model output was gradually adopted by the schedulers and outperformed the scheduler-predicted case length in Aug-Dec 2022. In 33,815 surgical cases across outpatient and inpatient platforms, the operational implementation predicted 11.2% fewer under-predicted cases and 5.9% more cases within 20% of the actual case length compared with the schedulers and only over-predicted 5.3% more. The model assisted schedulers to predict 3.4% more cases within 20% of the actual case length and 4.3% fewer under-predicted cases.ConclusionsWe created a unique framework that is being leveraged every day to predict surgical case length more accurately at case posting time and could be potentially utilized to deploy future machine learning models.