A Dynamic Redeployment System for Critical Care Paramedic Units in Qatar Utilizing Deep Reinforcement Learning

Timely ambulance allocation is essential for Emergency Medical Services (EMS) to deliver life-saving care effectively. Conventional methods often struggle to adapt to the unpredictable nature and locations of emergencies. Within EMS, efficient resource management is crucial for ensuring rapid and effective responses. While much emphasis has been placed on optimizing the deployment of ambulances from fixed stations, managing specialized critical care response units-known as Charlie vehicles in Qatar EMS-presents a distinct challenge. These rapid response cars are vital for providing advanced care in challenging situations, and their dynamic deployment requires a more flexible management strategy. Effectively relocating Charlie vehicles to areas with high anticipated demand after they have responded to an emergency introduces unique challenges that differ from traditional ambulance redeployment approaches. This paper proposes a novel dynamic redeployment system specifically for optimizing the allocation of critical care response vehicles, including those involved in patient transfers. Utilizing a Deep Reinforcement Learning (DRL) framework, we create a deep scoring network that prioritizes and navigates various dynamic factors at each station. Experiments using real-world data from Qatar EMS demonstrate that our system significantly outperforms existing methods. For instance, our approach achieves faster average response times and improved critical response rates compared to the leading baseline method. Notably, we observe a substantial 21.55% reduction in average response time (AveRT) and an 18.34% increase in relative response time (RelaRT) in comparison to actual operational metrics. Our approach effectively shortens the time needed to reach patients, thereby increasing the likelihood of timely treatment and improving overall patient care outcomes.