Related Experiment Video
Updated: May 6, 2026
Setting Up a Stroke Team Algorithm and Conducting Simulation-based Training in the Emergency Department - A Practical Guide
Published on: January 15, 2017
Reducing ambulance response times using discrete event simulation
Sean Shao Wei Lam1, Zhong Cheng Zhang, Hong Choon Oh
1from the Health Services Research and Biostatistics Unit, Division of Research, Singapore General Hospital , Singapore (SSWL) , Department of Emergency Medicine, Singapore General Hospital , Singapore (ZCZ, MEHO) , Centre for Health Services Research, Singapore Health Services Pte Ltd , Singapore (HCO) , Medical Department, Singapore Civil Defence Force , Singapore (YYN) , Centre for Infectious Disease Epidemiology and Research, Saw Swee Hock School of Public Health, National University of Singapore , Singapore (WW) .
Objectives:
The objectives of this study are to develop a discrete-event simulation (DES) model for the Singapore Emergency Medical Services (EMS), and to demonstrate the utility of this DES model for the evaluation of different policy alternatives to improve ambulance response times.
Methods:
A DES model was developed based on retrospective emergency call data over a continuous 6-month period in Singapore. The main outcome measure is the distribution of response times. The secondary outcome measure is ambulance utilization levels based on unit hour utilization (UHU) ratios. The DES model was used to evaluate different policy options in order to improve the response times, while maintaining reasonable fleet utilization.
Results:
Three policy alternatives looking at the reallocation of ambulances, the addition of new ambulances, and alternative dispatch policies were evaluated. Modifications of dispatch policy combined with the reallocation of existing ambulances were able to achieve response time performance equivalent to that of adding 10 ambulances. The median (90th percentile) response time was 7.08 minutes (12.69 minutes). Overall, this combined strategy managed to narrow the gap between the ideal and existing response time distribution by 11-13%. Furthermore, the median UHU under this combined strategy was 0.324 with an interquartile range (IQR) of 0.047 versus a median utilization of 0.285 (IQR of 0.051) resulting from the introduction of additional ambulances.
Conclusions:
Response times were shown to be improved via a more effective reallocation of ambulances and dispatch policy. More importantly, the response time improvements were achieved without a reduction in the utilization levels and additional costs associated with the addition of ambulances. We demonstrated the effective use of DES as a versatile platform to model the dynamic system complexities of Singapore's national EMS systems for the evaluation of operational strategies to improve ambulance response times.
