Ambulance Dispatch Prioritisation of Road Crash Patients: A Retrospective Study Using Population-Based Linked Data

Abstract

This thesis aimed to improve the accuracy of dispatching ambulances to road crashes by identifying the need for a lights and sirens (L&S) response. The current system of dispatching ambulances had low accuracy in predicting the need for L&S response. To address this, predictive models utilising a novel machine-learning approach and incorporating emergency medical dispatcher text were developed, achieving high accuracy. This research suggests that improving ambulance dispatching can enhance system efficiency and provide timely care to the appropriate patients.