On the issue of emergency medical service transportation support: relevance and research directions

Background. The article examines current challenges in optimizing emergency medical services (EMS) transportation logistics in the Russian Federation. It analyzes the system's current state, identifying key issues such as uneven call distribution, suboptimal station placement, outdated vehicle fleets, and inefficient dispatch operations. The study proposes research directions and development approaches to enhance EMS efficiency, including differentiated optimization strategies based on service type (emergency vs. urgent care), queuing theory applications, station location optimization, advanced routing and dispatching methods, and artificial intelligence/neural network implementations. The work emphasizes the need for region-specific solution adaptation and suggests concrete topics for further research. This article aims to draw researchers' and practitioners' attention to critical EMS logistics improvement opportunities to enhance healthcare accessibility and quality for the population.

Purpose – development of scientifically grounded approaches to optimize emergency medical services transportation logistics in the Russian Federation, aimed at enhancing service efficiency, reducing response times, and improving the quality of medical care for the population.

Materials and methods. The primary research method is theoretical-statistical analysis of the current system state and identification of key optimization challenges in EMS transportation logistics in the Russian Federation. The study is based on a comprehensive set of sources including regulatory legal acts, official records, statistical and reference materials, and periodical publications.

Results. This paper presents an analysis of the current state of ambulance service transportation support, identifying several key challenges, including uneven call distribution, suboptimal station location, an aging vehicle fleet, inefficient dispatching, and limited funding. The proposed research and development directions, based on a differentiated approach to optimization depending on the type of care provided (emergency and urgent), application of queuing theory, optimized station placement, implementation of advanced routing and dispatching methods, and the utilization of artificial intelligence and neural networks, constitute a comprehensive action plan for improving the efficiency of ambulance service transportation support operations.