Ambulance Routing Optimization for CT-Ready Hospitals

This paper aims to enhance emergency medical services by optimizing ambulance routes towards hospitals equipped for spiral CT scans with minimal wait times. It integrates real-time data on hospital availability and traffic conditions, utilizing machine learning and smart routing algorithms to predict traffic jams and determine the fastest routes. Additionally, a machine learning model is used to detect the risk level of patients based on reported symptoms, helping prioritize critical cases. It aims to reduce emergency response times, ensuring quicker patient treatment. Preliminary results show that the system has significantly reduced the time it takes for ambulances to reach patients for care receiving at hospitals. This means that emergency cases are treated faster, potentially saving more lives. By optimizing ambulance routes and prioritizing cases based on risk levels, the system enhances emergency response and healthcare efficiency.