Emergency medical response systems often face significant delays due to traffic congestion, inefficient ambulance allocation, and poor coordination between emergency services and hospitals. This project proposes an AI-powered intelligent ambulance dispatch coordination system designed to improve emergency response efficiency and reduce ambulance arrival time. The system integrates a citizen reporting application, ambulance driver interface, hospital dashboard, and a centralized control room for effective communication and decision-making. Emergencies can be reported by citizens or bystanders through location input, symptom description, image uploads, and a speech-to-text module that converts voice input into text for faster reporting. An AI-based triage system utilizing the XG Boost algorithm analyzes emergency data to determine the severity of the patient’s condition and prioritize dispatch decisions. The platform also evaluates ambulance availability, hospital capacity, and real-time traffic conditions to select the most suitable hospital and ambulance. Route optimization using the A* algorithm identifies the fastest path primarily through main roads to ensure safe and rapid ambulance movement. Additionally, nearby traffic control rooms are automatically notified to clear traffic routes. The proposed system aims to enhance emergency coordination, reduce response time, and improve patient survival outcomes in critical situations.
P et al. (Fri,) studied this question.