Published Paper Details:

Efficient allocation and utilization of healthcare resources--beds, staff, operating rooms, and medical equipment -- is critical to hospital performance, patient outcomes, and operational cost. Traditional (non-AI) resource management relies on rule-based scheduling, human planning, and queuing heuristics; these methods are robust and interpretable but can struggle with uncertainty, dynamic demand, and scale. Artificial intelligence (AI) approaches--especially predictive analytics and optimization augmented by machine learning--promise improved forecasting, dynamic allocation, and prescriptive recommendations. This paper presents a comparative analysis of healthcare resource management systems with and without AI. We define evaluation metrics (accuracy of demand forecasting, utilization efficiency, waiting-time reduction, robustness to uncertainty, and implementation cost/complexity), synthesize findings from recent literature, and summarize benefits, limitations, and adoption barriers. Our analysis shows AI-enabled systems consistently improve forecasting accuracy and operational efficiency but introduce challenges in integration, data quality, transparency, and clinician trust. We conclude with recommendations for hybrid adoption strategies and directions for future research. (PMC).