Improving Hospital Preparedness for Future Pandemics with Machine Learning and Queue Management Solutions

The COVID-19 pandemic has underscored the critical need for resilient and efficient hospital workflows capable of managing sudden increases in patient volume. This study presents a comprehensive approach that integrates advanced machine learning algorithms with queue management techniques to enhance hospital preparedness and response during pandemics. By Employing predictive analytics and classification systems, our methodology improves patient triage, optimizes resource allocation, and reduces wait times, thereby enhancing overall patient care. The study includes a detailed analysis of machine learning models used for patient prioritization and an exploration of queue management strategies to streamline patient flow. Our findings indicate substantial improvements in hospital efficiency and the ability to handle peak demands during health crises. This research provides actionable insights and a robust framework for healthcare administrators aiming to fortify their facilities against future pandemic scenarios, ultimately contributing to better healthcare outcomes.