Background and objective: The growth of the urban population exerts considerable pressure on municipalities' public managers to focus their attention on providing emergency medical care that meets the growing demand for emergency pre-hospital medical care. Currently, there are a significant number of traffic accidents and other serious occurrences, such as heart attacks, drownings, epidemics, fires and disasters (floods, landslides, earthquakes) that demand a prompt and seamless response from pre-hospital medical care. As a result of such scenario, the present article endeavours to apply a dual-coverage mathematical model (DSM-Double Standard Model) to define the optimal location of the Emergency Medical Service (SAMU) decentralized dispatch bases in Natal/RN and conduct a simulation study to evaluate the displacement of ambulances between such bases.
Methods: The methodological course that was followed by this research constitutes of 12 steps. The location of decentralized bases for sending emergency ambulances was established using the DSM model and the simulation model was performed using the FlexSim© software version 2018 evaluating base coverage in relation to the total number of calls by demand points for different scenarios.
Results: The results obtained throughout the research demonstrated the feasibility of redefining the decentralized bases of SAMU/Natal ambulances as a strategy to reduce response time and guarantee compliance with performance parameters established by international organizations (the World Health Organization, for instance, establishes the time of 8 minutes for emergency medical service calls response). The simulation study showed a significant reduction in response time, by up to 60% in some cases.
Conclusion: The proposition of new locations for the decentralized dispatch bases of the SAMU/Natal can provide an overall significant reduction on the ambulance response time, so as to contribute to expedite the initiation of treatment of patients, if necessary, sent to hospitals.
Keywords: Medical emergency service; ambulances; optimization; response time; simulation.