Intelligent Scheduling System for Hospital Operating Rooms Using Predictive Analytics

Abstract

The orderly allocation of resources, the reduction of patients’ waiting times, and the general enhancement of service
quality to be provided in the healthcare system are dependent on optimizing the scheduling of the hospital's operating
rooms (OR). The classical techniques of dealing with scheduling are not able to cope with the complexity and
dynamics of the surgical procedure's hierarchy. In this paper, we present a scheduling enhancement algorithm for
operating rooms (ORs) that is based on intelligent predictive analytics. This method analyzes historical surgical data
to project operation duration, identifies probable delays, and makes schedule changes in advance. Furthermore, the
methodology facilitates real-time scheduling updates via the hospital information system through application of
machine learning algorithms. Testing the surgical scheduling system in a mid-sized hospital demonstrated
improvements in OR utilization, along with a reduction in surgical delays. We state that the application of predictive
analytics greatly enhances the future value of OR scheduling concerning resource management and patient
satisfaction.