Machine Learning Applications in Sustainable Healthcare Resource Optimization: A Systematic Framework for Post-Pandemic Resilience

Abstract

The COVID-19 pandemic exposed critical vulnerabilities in healthcare resource management, necessitating innovative approaches to optimize resource allocation while maintaining quality of care. This study proposes a systematic machine learning framework for sustainable healthcare resource optimization that addresses post-pandemic challenges including patient flow management, staff scheduling, and supply chain resilience. This developed a multi-objective optimization framework integrating predictive analytics, simulation modeling, and reinforcement learning. The framework was validated using electronic health record data from three tertiary care hospitals (2019-2024), encompassing 245,000 patient admissions. Performance metrics included resource utilization efficiency, patient wait times, and cost-effectiveness ratios. The proposed framework achieved 23% improvement in resource utilization efficiency, 18% reduction in average patient wait times, and 15% decrease in operational costs compared to baseline configurations. Predictive models demonstrated 89% accuracy in admission forecasting and 92% precision in length-of-stay estimation. Machine learning-enabled resource optimization offers sustainable pathways for healthcare system resilience. The framework provides actionable insights for administrators while supporting equitable resource distribution in resource constrained settings.