Optimization of Hospital Queue Management Using Priority Queue Algorithm and Reinforcement Learning for Emergency Service Prioritization

This study aims to develop and implement an efficient hospital queue management system by integrating the Priority Queue algorithm with Reinforcement Learning (RL). The primary objective is to enhance the prioritization of emergency patients, ensuring that those with the most critical conditions receive timely care. The Priority Queue algorithm facilitates the sorting of patients based on the severity of their medical conditions, while RL enables the system to continuously learn and optimize the queue management process using historical data and real-time feedback. The research methodology includes data collection from hospital queues, algorithm model development, and simulated and real-world data validation. The results demonstrate that the combination of these algorithms significantly reduces waiting times for emergency patients and improves overall hospital operational efficiency. Additionally, implementing this algorithm has increased patient satisfaction due to shorter wait times and more timely services. The study concludes that the Priority Queue algorithm enhanced by RL is an effective solution for hospital queue management and recommends further research on larger scales and with more complex algorithms.