Predicting ambulance time of arrival to the emergency department using global positioning system and Google maps

Abstract

Objective: To derive and validate a model that accurately predicts ambulance arrival time that could be implemented as a Google Maps web application.

Methods: This was a retrospective study of all scene transports in Multnomah County, Oregon, from January 1 through December 31, 2008. Scene and destination hospital addresses were converted to coordinates. ArcGIS Network Analyst was used to estimate transport times based on street network speed limits. We then created a linear regression model to improve the accuracy of these street network estimates using weather, patient characteristics, use of lights and sirens, daylight, and rush-hour intervals. The model was derived from a 50% sample and validated on the remainder. Significance of the covariates was determined by p < 0.05 for a t-test of the model coefficients. Accuracy was quantified by the proportion of estimates that were within 5 minutes of the actual transport times recorded by computer-aided dispatch. We then built a Google Maps-based web application to demonstrate application in real-world EMS operations.

Results: There were 48,308 included transports. Street network estimates of transport time were accurate within 5 minutes of actual transport time less than 16% of the time. Actual transport times were longer during daylight and rush-hour intervals and shorter with use of lights and sirens. Age under 18 years, gender, wet weather, and trauma system entry were not significant predictors of transport time. Our model predicted arrival time within 5 minutes 73% of the time. For lights and sirens transports, accuracy was within 5 minutes 77% of the time. Accuracy was identical in the validation dataset. Lights and sirens saved an average of 3.1 minutes for transports under 8.8 minutes, and 5.3 minutes for longer transports.

Conclusions: An estimate of transport time based only on a street network significantly underestimated transport times. A simple model incorporating few variables can predict ambulance time of arrival to the emergency department with good accuracy. This model could be linked to global positioning system data and an automated Google Maps web application to optimize emergency department resource use. Use of lights and sirens had a significant effect on transport times.

Figure 1

Distribution of actual transport times from scene to emergency department (n = 24,133). From derivation dataset.

Figure 2

Scatterplot of actual transport times in minutes versus street network estimates in the derivation dataset (n = 24,133). Lines show model predicted times. Lines for nighttime, rush-hour transports represent less than 2 hours a day in the middle of winter and were omitted for clarity.

Figure 3

Distribution of error in predicted transport time for three models. Error is shown as predicted minus actual, so negative numbers signify the model underestimating transport time (patient arriving after the predicted time).

Figure 4

Google Maps web application to display ambulance time of arrival.