Original contribution
A geographic information system simulation model of EMS: reducing ambulance response time☆

https://doi.org/10.1016/j.ajem.2004.02.003Get rights and content

Abstract

Response time is a very important factor in determining the quality of prehospital EMS. Our objective was to model the response by Israeli ambulances and to offer model-derived strategies for improved deployment of ambulances to reduce response time. Using a geographic information system (GIS), a retrospective review of computerized ambulance call and dispatch logs was performed in two different regional districts, one large and urban and the other rural. All calls that were pinpointed geographically by the GIS were included, and their data were stratified by weekday and by daily shifts. Geographic areas (polygons) of, at most, 8 minutes response time were simulated for each of these subgroups to maximize the timely response of calls. Before using the GIS model, mean response times in the Carmel and Lachish districts were 12.3 and 9.2 minutes, respectively, with 34% and 62% of calls responded within 8 minutes. When ambulances were positioned within the modeled polygons, more than 94% of calls met the 8-minute criterion. The GIS simulation model presented in this study suggests that EMS could be more effective if a dynamic load-responsive ambulance deployment is adopted, potentially resulting in increased survival and cost-effectiveness.

Section snippets

Materials and methods

A retrospective review of local ambulance calls recorded for a 12-month period during the years 1996 through 1997 was performed on computer-logged data from two of the 12 MDA regional districts. These two regions were selected to represent the full range of needs for EMS and the whole gamut of extrinsic factors affecting ambulance response time, namely distances, traffic conditions, and population load, and to enable testing a model of optimal ambulance deployment.

One district was Carmel, in

Results

The response times (from receipt of call to arrival on scene) by MDA teams in the Carmel and Lachish districts are tabulated in Table 1. The mean response times in these districts were 12.3 and 9.2 minutes, respectively. The 95th percentiles of response in these districts were in excess of 26 and 23 minutes, respectively. Indeed, with 8 minutes set as the standard, only 34% of the calls were responded to in the Carmel district, implying that 66% of them were late (Fig 2A). In Lachish, 62% of

Discussion

The simulation model that was constructed and presented in this study suggests that EMS could become more effective if a dynamic, load-responsive ambulance deployment model is adopted. This model can be tailored for any district and EMS system in which call data are available for retrospective analysis.

The response time is critical for the patient’s survival and reduction of disability, and many have shown a direct relationship between prehospital medical treatment or resuscitation1, 2, 3, 4, 5

Acknowledgements

The authors thank Joseph Rosenblum, MD, for his invaluable assistance in the statistical and computing aspects of this work.

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Supported in part by grant 15/97 from the Israeli National Institute for Health Policy and Health Services Research.

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