AUTOMATED AMBULANCE DISPATCH MANAGEMENT SYSTEM
Abstract
Emergency medical services (EMS) play a critical role in providing life-saving assistance during emergencies. However, traditional ambulance dispatch systems often face inefficiencies such as delayed response times, lack of real-time tracking, manual allocation of ambulances, and poor communication between emergency responders. These issues can significantly impact patient survival rates, making it crucial to adopt a more efficient, automated approach. This project presents the development of an Automated Ambulance Dispatch System, designed to optimize ambulance allocation, minimize response times, and enhance communication between dispatch centers, ambulance drivers, and healthcare facilities. The system integrates real-time GPS tracking, automated dispatching algorithms, and a dynamic resource management framework to ensure that ambulances are assigned based on proximity, availability, and emergency severity. The system was developed using PHP, MySQL, JavaScript, and Flutter for mobile compatibility, enabling seamless access for administrators, dispatch operators, and ambulance drivers. Key features of the system include ,Automated Ambulance Allocation which Uses an intelligent algorithm to assign ambulances based on the patient’s location and ambulance availability. Real-Time GPS Tracking which Allows dispatch operators to track ambulance locations and provide optimized routes to drivers. The system was tested using simulated emergency scenarios in Kano State, demonstrating significant improvements in response time, ambulance utilization, and overall system efficiency. Compared to manual dispatching, the automated system reduced response times by 30-40%, ensuring faster medical intervention and improved patient outcomes. In conclusion, the Automated Ambulance Dispatch System provides a scalable, efficient, and intelligent solution for emergency medical services. By integrating automation, real-time tracking, and decision-making, the system enhances emergency response capabilities, reduces delays, and optimizes healthcare resource management. Future enhancements may include AI-based predictive dispatching, integration with smart city traffic systems, and the use of drones for pre-hospital care.
Full-Text Access Notice
In accordance with the NERD Policy on promoting peer-reviewed publication, public access to the full text of a project, thesis or dissertation is restricted for three years, allowing the author and supervisors sufficient time to pursue peer-reviewed publication.
During this period, researchers with legitimate academic or research purposes may request authorisation directly from the author to enable NERD to release the indexed full texts of the work using the form below.