Optimal location for a helicopter in a rural trauma system: prediction using discrete-event computer simulation

Area of Science:

• Emergency Medical Services
• Operations Research
• Public Health

Background:

• Rural areas present unique challenges for emergency medical services (EMS) due to dispersed populations and potentially long transport times.
• Optimizing the placement of critical resources like air ambulances is crucial for improving patient outcomes.
• Previous studies have not fully utilized advanced simulation techniques for EMS resource allocation in rural settings.

Purpose of the Study:

• To determine the optimal geographic base location for a trauma system helicopter in Maine using discrete-event simulation.
• To assess the impact of various factors, including population distribution, injury severity, weather, and operational constraints, on helicopter utilization.
• To evaluate the effectiveness of discrete-event simulation as a planning tool for rural EMS systems.

Main Methods:

• Developed a discrete-event simulation model in C programming language.
• Utilized one year of ambulance run reports for trauma event data (time, location).
• Incorporated statewide trauma registry data for trauma center care percentages and locations.
• Modeled trauma event incidence using a nonstationary Poisson process and empirical distribution for location.
• Included climatic data to account for weather-related flight limitations.
• Simulated 35 years for each of 4 proposed helicopter base locations.

Main Results:

• A geographically intermediate location was identified as yielding the highest helicopter utilization frequency.
• The simulation successfully integrated multiple real-world variables affecting air medical transport.
• The model demonstrated the potential for significant helicopter use under optimal placement.

Conclusions:

• Discrete-event simulation is a valuable tool for planning and optimizing emergency medical services systems, particularly in rural environments.
• Strategic helicopter base placement can significantly enhance resource utilization and potentially improve response times.
• Further validation and refinement of simulation models can lead to broader adoption in EMS planning.