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Open Access Highly Accessed Research

A validation of ground ambulance pre-hospital times modeled using geographic information systems

Alka B Patel1, Nigel M Waters25, Ian E Blanchard13, Christopher J Doig145 and William A Ghali145*

Author Affiliations

1 Department of Community Health Sciences, University of Calgary, Calgary, Alberta, Canada

2 Department of Geography and Director, GIS Center of Excellence, George Mason University, Fairfax, Virginia, USA

3 Alberta Health Services, Emergency Medical Services, Calgary, Alberta, Canada

4 Department of Medicine, University of Calgary, Calgary, Alberta, Canada

5 Institute of Public Health, University of Calgary, Calgary, Alberta, Canada

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International Journal of Health Geographics 2012, 11:42  doi:10.1186/1476-072X-11-42

Published: 3 October 2012

Abstract

Background

Evaluating geographic access to health services often requires determining the patient travel time to a specified service. For urgent care, many research studies have modeled patient pre-hospital time by ground emergency medical services (EMS) using geographic information systems (GIS). The purpose of this study was to determine if the modeling assumptions proposed through prior United States (US) studies are valid in a non-US context, and to use the resulting information to provide revised recommendations for modeling travel time using GIS in the absence of actual EMS trip data.

Methods

The study sample contained all emergency adult patient trips within the Calgary area for 2006. Each record included four components of pre-hospital time (activation, response, on-scene and transport interval). The actual activation and on-scene intervals were compared with those used in published models. The transport interval was calculated within GIS using the Network Analyst extension of Esri ArcGIS 10.0 and the response interval was derived using previously established methods. These GIS derived transport and response intervals were compared with the actual times using descriptive methods. We used the information acquired through the analysis of the EMS trip data to create an updated model that could be used to estimate travel time in the absence of actual EMS trip records.

Results

There were 29,765 complete EMS records for scene locations inside the city and 529 outside. The actual median on-scene intervals were longer than the average previously reported by 7–8 minutes. Actual EMS pre-hospital times across our study area were significantly higher than the estimated times modeled using GIS and the original travel time assumptions. Our updated model, although still underestimating the total pre-hospital time, more accurately represents the true pre-hospital time in our study area.

Conclusions

The widespread use of generalized EMS pre-hospital time assumptions based on US data may not be appropriate in a non-US context. The preference for researchers should be to use actual EMS trip records from the proposed research study area. In the absence of EMS trip data researchers should determine which modeling assumptions more accurately reflect the EMS protocols across their study area.

Keywords:
Pre-hospital time; Geographic Information Systems; Validation; Emergency medical services