AI-Driven Optimization of Healthcare Resources: An Intelligent Management Model for Hospital Operational Efficiency in the Post-Pandemic Era

Abstract

The COVID-19 pandemic has underscored the critical need for flexible and adaptive hospital management systems to handle fluctuating patient volumes and constrained medical resources. Traditional resource allocation methods, relying on fixed schedules and static capacity planning, are increasingly inadequate in addressing the dynamic challenges faced by hospitals. This study addresses this gap by proposing an AI-driven hospital resource optimization model, integrating demand forecasting, dynamic scheduling, and system simulation. The model was evaluated using real-world hospital data, demonstrating significant improvements in operational efficiency, including a 20% reduction in patient waiting times and a 33% increase in bed turnover rate. The AI model also enabled proactive staff scheduling and bed management during peak demand periods, such as flu seasons. This research contributes to the understanding of hospitals as complex adaptive systems and provides a replicable framework for optimizing resource allocation. The findings have significant implications for improving hospital resilience, operational efficiency, and patient care quality, particularly in post-pandemic healthcare settings. Future work will focus on refining the model for multi-hospital applications and addressing the challenges of data integration and ethical concerns in AI decision-making.