Using a discrete-event simulation to balance ambulance availability and demand in static deployment systems.

OBJECTIVES To improve ambulance response time, matching ambulance availability with the emergency demand is crucial. To maintain the standard of 90% of response times within 9 minutes, the authors introduce a discrete-event simulation method to estimate the threshold for expanding the ambulance fleet when demand increases and to find the optimal dispatching strategies when provisional events create temporary decreases in ambulance availability. METHODS The simulation model was developed with information from the literature. Although the development was theoretical, the model was validated on the emergency medical services (EMS) system of Tainan City. The data are divided: one part is for model development, and the other for validation. For increasing demand, the effect was modeled on response time when call arrival rates increased. For temporary availability decreases, the authors simulated all possible alternatives of ambulance deployment in accordance with the number of out-of-routine-duty ambulances and the durations of three types of mass gatherings: marathon races (06:00-10:00 hr), rock concerts (18:00-22:00 hr), and New Year's Eve parties (20:00-01:00 hr). RESULTS Statistical analysis confirmed that the model reasonably represented the actual Tainan EMS system. The response-time standard could not be reached when the incremental ratio of call arrivals exceeded 56%, which is the threshold for the Tainan EMS system to expand its ambulance fleet. When provisional events created temporary availability decreases, the Tainan EMS system could spare at most two ambulances from the standard configuration, except between 20:00 and 01:00, when it could spare three. The model also demonstrated that the current Tainan EMS has two excess ambulances that could be dropped. The authors suggest dispatching strategies to minimize the response times in routine daily emergencies. CONCLUSIONS Strategies of capacity management based on this model improved response times. The more ambulances that are out of routine duty, the better the performance of the optimal strategies that are based on this model.

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