Optimizing Hospital Operations with AI-driven Resource Allocation and Capacity Planning

Abstract

This study presents an AI-driven approach to optimize hospital operations by dynamically allocating resources and planning capacity to meet patient demand effectively. Using machine learning algorithms, our model analyzes historical patient flow data, bed occupancy rates, and staffing levels to forecast future demand and optimize resource allocation in real-time. Implementation of this approach can improve operational efficiency, reduce wait times, and enhance the overall quality of care delivery in hospital settings.