Real-Time Hospital Recommendation System with Machine Learning for Traffic-Aware Routing, Patient Ratings, and Dynamic Resource Management

A critical selection criterion of hospitals that a patient faces in urban healthcare settings is the selection of a well-suited hospital during emergencies. These problems delay access to timely appropriate medical care, primarily due to traffic congestion, lack of real-time information at hospitals for availability, and patient preference. Therefore, this project will present a Hospital Recommendation System offering real-time recommendations based on factors like patient ratings, proximity, traffic conditions, and availability of resources within the hospital. Using real-time traffic data for better routing, the system uses machine learning algorithms to make very accurate predictions and cluster hospitals based on specialization. The system also fits best in terms of ensuring privacy and scalability by adopting a decentralized model of data wherein every hospital has its node to ensure both hospital and patient data without having a centralized server. It has a mobile interface on the patient side for accessing hospital recommendations and on the hospital side, a dashboard to manage the real-time updates of resources. The results obtained from pilot studies show significantly reduced waiting times for patients, better use of resources in hospitals, and an overwhelming majority of satisfied patients with the potential of the system as a means for enhancing accessibility and efficiency in healthcare services.