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.
Jakub Novák (Fri,) studied this question.