Medipath: An Intelligent Emergency Medical Service System with NLP and Real-Time Coordination

This paper discusses the creation and assessment
of MediPath, a smart web-based emergency medical assistance
system. It connects patients, hospitals, and ambulance services
in one digital platform. The system uses technologies like
Natural Language Processing (NLP) to match hospital
specialties with patient symptoms. It also uses real-time location
services and smart route planning to address gaps in emergency
medical response. MediPath has a three-part structure that
supports logins for patients or attendants, hospital management
portals, and navigation systems for ambulance drivers. The
platform uses machine learning to link patient symptoms with
hospital specialties, Firebase Realtime Database for easy data
syncing, and mapping APIs for better traffic-aware routes.
Performance tests show average response times of under 30
seconds for hospital matches and 95% accuracy in matching
symptoms to specialties. There is also a significant drop in
ambulance dispatch delays. User studies at different healthcare
facilities indicate improved coordination, shorter emergency
response times, and better use of resources. Although there are
challenges with highly specialized medical conditions and
connectivity in rural areas, MediPath greatly enhances
emergency medical service delivery by creating a unified
ecosystem that connects all parties in real time. This research
presents a scalable, smart solution that addresses disconnected
emergency medical services. It uses modern web technologies
alongside healthcare knowledge to help save lives through
quicker and more coordinated emergency responses.