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

Area of Science:

• Operations Research in Healthcare
• Health Systems Management
• Emergency Medical Services Simulation

Background:

• Modeling and Simulation (M&S) offers a risk-free method for analyzing healthcare systems, disease progression, and treatment outcomes.
• The Mobile Emergency Care Service (SAMU) in Brazil requires efficient operational strategies to manage patient flow and resource allocation.
• Optimizing ambulance services is crucial for enhancing the resilience of the Unified Health System (SUS).

Purpose of the Study:

• To simulate the ambulance service system of SAMU in a Brazilian region using Arena software and Machine Learning (ML).
• To analyze the impact of varying resource configurations on key performance indicators like waiting times and workload.
• To predict the effects of different numbers of ambulances on patient waiting times.

Main Methods:

• Quantitative methodology combining mathematical modeling and a case study approach.
• Utilized Arena software for discrete-event simulation of the SAMU system.
• Integrated ML, specifically a regression model, with simulation outputs to correlate waiting times and ambulance numbers, referencing the Manchester Protocol.

Main Results:

• Simulation results based on real data indicated that increasing the number of ambulances significantly reduces patient waiting times.
• Streamlined resource allocation was observed with optimized ambulance deployment.
• The integrated M&S and ML approach provided predictive insights into system performance under various scenarios.

Conclusions:

• The study demonstrates that optimizing ambulance numbers and resource allocation can enhance the operational efficiency of mobile emergency services.
• Improved efficiency in emergency medical services contributes to the overall resilience and performance of Brazil's Unified Health System (SUS).
• The combined use of simulation and machine learning provides a powerful tool for healthcare system planning and improvement.