Predicting Robotic Hysterectomy Incision Time: Optimizing Surgical Scheduling with Machine Learning.
Journal:
JSLS : Journal of the Society of Laparoendoscopic Surgeons
PMID:
39831273
Abstract
BACKGROUND AND OBJECTIVES: Operating rooms (ORs) are critical for hospital revenue and cost management, with utilization efficiency directly affecting financial outcomes. Traditional surgical scheduling often results in suboptimal OR use. We aim to build a machine learning (ML) model to predict incision times for robotic-assisted hysterectomies, enhancing scheduling accuracy and hospital finances.