Published Paper Details:

Emergency medical services are critical for saving lives, but instances of ambulance misuse, driven by factors such as commissions, can hinder timely responses. This project introduces a Smart Ambulance Routing System designed to mitigate such issues and optimize emergency response efforts. Leveraging real-time hospital information, GPS technology, and intelligent algorithms, the system guides ambulance drivers to the nearest hospitals, ensuring efficient and patient-centered emergency care. Two-way communication facilitates information exchange, and a user-friendly interface supports manual intervention. Continuous learning algorithms and privacy measures ensure system adaptability and data security. The system aims to revolutionize emergency response through AI-driven optimization and dynamic resource allocation. Ongoing collaboration and monitoring are essential for its effectiveness. The patient's biomedical parameters, such as heart rate, respiration, and body temperature, will be collected by the LSAI48266X Artificial Intelligence board. It will then use an algorithm to process the data and determine which category the patient falls into, i.e., oxygen bed support, ventilator support, or ICU support. The board will do this by utilizing Internet of Things to check the hospital's networked medical system availability. The hospital is informed to the ambulance driver via the Telegram app. The Smart Ambulance Routing System makes use of effective communication protocols, a cloud-based platform for hospital data storage, and NodeMCU for real-time GPS tracking. Hospital distances are dynamically calculated by cloud functions, and the best routes are determined by an onboard algorithm. An alert message is triggered when there is a major delay in the ambulance arrival.