Using a Discrete‐event Simulation to Balance Ambulance Availability and Demand in Static Deployment Systems

Abstract

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.

A generic method to develop simulation models for ambulance systems

“…Su and Shih [24] developed a simulation model of the ambulance service system of Taipei, Taiwan and used it to explore alternative scenarios that might improve the operation of the system. Wu and Hwang [25] built a wide-ranging simulation model to analyse the ambulance system of Tainan, Taiwan. This model was used to estimate the threshold for expanding the ambulance fleet and to evaluate ambulance-dispatching strategies during large public events such as concerts and marathon races.…”

Integrated decision making in healthcare : an operations research and management science perspective

“…Emergency care services have the goal to reduce morbidity and mortality resulting from acute illness and trauma [412,526]. To attain this goal, rapid response of an ambulance and transportation to an emergency care center (e.g., an emergency department in a hospital, or an emergency location near a disaster) are required [40,412].…”

“…Other factors to take into account in planning the locations of ambulances are the likelihood of timing and location of an emergency, staff availability, location constraints (e.g., a place where staff can rest), and the emergency care center where patients are potentially transported to [36,62,79,158,180,205,267,276,399,412,526]. Methods: computer simulation [62,165,180,186,204,237,267,412,429,459,526], heuristics [22,36,38,164,197,266], Markov processes [21,257], mathematical programming [18,36,38,39,40,45,79,118,158,165,186,205,207,237,257,276,412,…”

“…Ambulances exist in different transport modalities (helicopters, vans, cars) carrying different types of equipment and staff [38,39,40,165,186,267,412,429,446,468,526]. Ambulances collect emergency patients, but also perform less urgent transfers of patients between care facilities [468].…”

“…The number of ambulances should be chosen to include buffer capacity, to cope with fluctuations in demand and ambulance availability. Fluctuations in demand may be caused by expected demand peaks, such as large events, or unexpected demand peaks, such as large-scale accidents [526].…”