Introduction
Accurately predicting ambulance time of arrival to the emergency department (ED) is important for effective resource management, especially for critical patients. Overestimates of arrival time potentially waste resources in the ED as personnel await the ambulance. Underestimates result in unpreparedness when the patient arrives, with potential adverse outcomes in high-acuity patients. Currently, there are no aids available for the EMS crew or ED staff to reliably estimate the ambulance time of arrival.
There are numerous disadvantages to relying on the prehospital provider's estimated time of arrival (ETA). First, providers' perception of elapsed time is often inaccurate, with a prospective study of transport times showing that the prehospital provider's estimate of the duration of the transport at its conclusion was off by an average of 33%.1 Predictions made at the beginning of the transport were also inaccurate. A study of 6139 trauma patients in Portland, Oregon, showed that prehospital providers were more than 5 minutes outside their ETA 55% of the time and greater than 10 minutes inaccurate in 28% of transports.2 Secondly, as the ETA is often reported to the receiving hospital along with the medical report, it may be delayed until the patient is nearly at the hospital, giving little time to prepare. Finally, calling in an ETA by radio or telephone distracts providers from patient care and driving.
Our objective in this study was to derive and validate a model to more accurately predict ambulance time of arrival to the ED based on global positioning system (GPS) location data and other relevant factors. This model was then used to create a Google Maps web application that could provide these predictions in real time to ED providers for all EMS transports. A secondary objective was to assess the influence of lights and sirens on transport time.