Optimising Ambulance Placement and Routing Using ML-driven predictive Analytics for Enhanced Emergency Response

This paper proposes a new healthcare platform, combining an innovative module in the emergency response sector aimed at improving the availability, timeliness, and effectiveness of patient care within the city. The platform allows monitoring of health metrics for patients and communication with providers of care among other recommendations tailored to the patients. By utilizing cloud technology and mobile access, the platform guarantees data safety while encouraging legitimate user access and monitoring in real-time, which contributes to more involved patients and up to 20% fewer redundant clinic visits. Emergency services are provided with a module that utilizes the tracking of real-time data, integration with Google Maps, and advanced predictive analytics to ensure strategic deployment of ambulances in high-demand areas, thereby optimizing the distribution of patients. This system has dynamic route calculation, generative algorithms, and analysis of road conditions through Google Maps reviews, making ambulances reach emergencies in the shortest time possible regardless of the complexity of traffic or sudden changes in the roads. Adaptive, multi-objective optimization helps in the improvement of resource allocation and facilitates balanced patient load distribution across hospitals to avoid strain on individual facilities. Research suggests that the EMS system can predict an optimal route and risk area with an 83.33% accuracy level, thereby bolstering the emergency response ability and offering a scalable model for urban planners and healthcare providers to improve their EMS systems. This synergy points toward the potential of real-time data and predictive analytics for transforming healthcare delivery and emergency response, making it more resilient, accessible, and responsive in diverse urban settings.