Ambulance Dispatch Prioritisation of Road Crash Patients: A Retrospective Study Using Population-Based Linked Data
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 utilis...
| Main Author: | |
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| Format: | Thesis |
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Curtin University
2023
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| Online Access: | http://hdl.handle.net/20.500.11937/93621 |
| _version_ | 1848765756407283712 |
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| author | Ceklic, Ellen |
| author_facet | Ceklic, Ellen |
| author_sort | Ceklic, Ellen |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | 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. |
| first_indexed | 2025-11-14T11:40:18Z |
| format | Thesis |
| id | curtin-20.500.11937-93621 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T11:40:18Z |
| publishDate | 2023 |
| publisher | Curtin University |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-936212023-10-26T00:30:10Z Ambulance Dispatch Prioritisation of Road Crash Patients: A Retrospective Study Using Population-Based Linked Data Ceklic, Ellen 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. 2023 Thesis http://hdl.handle.net/20.500.11937/93621 Curtin University fulltext |
| spellingShingle | Ceklic, Ellen Ambulance Dispatch Prioritisation of Road Crash Patients: A Retrospective Study Using Population-Based Linked Data |
| title | Ambulance Dispatch Prioritisation of Road Crash Patients:
A Retrospective Study Using Population-Based Linked Data |
| title_full | Ambulance Dispatch Prioritisation of Road Crash Patients:
A Retrospective Study Using Population-Based Linked Data |
| title_fullStr | Ambulance Dispatch Prioritisation of Road Crash Patients:
A Retrospective Study Using Population-Based Linked Data |
| title_full_unstemmed | Ambulance Dispatch Prioritisation of Road Crash Patients:
A Retrospective Study Using Population-Based Linked Data |
| title_short | Ambulance Dispatch Prioritisation of Road Crash Patients:
A Retrospective Study Using Population-Based Linked Data |
| title_sort | ambulance dispatch prioritisation of road crash patients:
a retrospective study using population-based linked data |
| url | http://hdl.handle.net/20.500.11937/93621 |