Predictive Analytics for Hospital Resource Optimization

Abstract

Efficient utilization of hospital resources is critical for delivering quality healthcare services, especially under conditions of high patient inflow and limited infrastructure. This paper presents a Predictive Analytics framework for Hospital Resource Optimization using machine learning techniques to forecast demand and allocate resources effectively. The proposed system analyzes historical healthcare data, including patient admissions, discharge rates, bed occupancy, and staffing patterns, to identify trends and predict future requirements. Various predictive models such as regression, time-series forecasting, and ensemble learning methods are employed to improve accuracy and reliability. The framework enables proactive decision-making by assisting hospital administrators in optimizing resource allocation, reducing patient waiting times, and minimizing operational costs. Additionally, integration with prior research in disease prediction and hybrid learning enhances the robustness and adaptability of the system. Experimental results demonstrate that the proposed approach significantly improves resource utilization and operational efficiency. The study highlights the potential of predictive analytics in transforming hospital management systems and supporting data-driven healthcare planning, particularly in resource-constrained environments.