Optimizing Resource Allocation in Oral Surgery Units Using Machine Learning-Based Hospital Management Systems

Abstract

Resource allocation in oral surgery departments is the key indicator that determines clinical outcomes, patient satisfaction, and the overall performance of the hospitals. Due to rising requirements and the complexity of needs, this paper will discuss how machine learning (ML) in Hospital Management Systems (HMS) can be used to support the related decision-making in surgical schedule generation, human resource allocation, and equipment management. On the basis of real-time hospital data regarding three urban tertiary dental centers, we used supervised ML algorithms to forecast surgery length, bottlenecks in personnel allocation, and improve inventory management. The ML-enhanced HMS showed 27 percent increase in surgery room turnover rates, 19 percent decrease in idle time of staff, 22 percent enhancement in inventory accuracy in the analyzed centers. All these findings demonstrate the great possibility of data-driven automation in the organization of the work of oral surgery and minimization of operational wastes. In addition, the study illustrates the importance of the AI-powered tools in enhancing cross-functional coordination in the hospital settings and suggests a scalable decision-support system that might be replicated in various clinical networks