Machine Learning and Simulation: pathways to efficient emergency care in Brazil.

Abstract

Modeling and Simulation (M&S) allows for reproducing medical procedures and services, understanding disease progression, and predicting treatment responses without risks to real patients. This study aims to simulate the ambulance service system of the Mobile Emergency Care Service (SAMU) in a Brazilian region, using the Arena software and Machine Learning (ML). The quantitative methodology combines mathematical modeling and a case study to analyze variables such as the number of ambulances, patient arrivals, waiting times, and workload. Using the Manchester Protocol as a reference, the Arena results feed a regression model to relate waiting times and the number of ambulances. Integrating these techniques allowed for predictions regarding the impact of different resource configurations. Based on real data, the numerical results indicated reduced waiting times with increased ambulances and streamlined resource allocation. Thus, by contributing to the operational efficiency of mobile emergency services, the findings also strengthen the resilient performance of the Unified Health System (SUS) in the face of adversities.