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

    Ceklic E 2023 Public.pdf (40.42Mb)
    Access Status
    Open access
    Authors
    Ceklic, Ellen
    Date
    2023
    Supervisor
    Judith Finn
    Stephen Ball
    Hideo Tohira
    Type
    Thesis
    Award
    PhD
    
    Metadata
    Show full item record
    Faculty
    Health Sciences
    School
    Curtin School of Nursing
    URI
    http://hdl.handle.net/20.500.11937/93621
    Collection
    • Curtin Theses
    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.

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