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A predictive ambulance dispatch algorithm to the scene of a motor vehicle crash: the search for optimal over and under triage rates

BACKGROUND: Calls for emergency medical assistance at the scene of a motor vehicle crash (MVC) substantially contribute to the demand on ambulance services. Triage by emergency medical dispatch systems is therefore important, to ensure the right care is provided to the right patient, in the right am...

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Autores principales: Ceklic, Ellen, Tohira, Hideo, Ball, Stephen, Brown, Elizabeth, Brink, Deon, Bailey, Paul, Brits, Rudolph, Finn, Judith
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9074212/
https://www.ncbi.nlm.nih.gov/pubmed/35524169
http://dx.doi.org/10.1186/s12873-022-00609-5
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author Ceklic, Ellen
Tohira, Hideo
Ball, Stephen
Brown, Elizabeth
Brink, Deon
Bailey, Paul
Brits, Rudolph
Finn, Judith
author_facet Ceklic, Ellen
Tohira, Hideo
Ball, Stephen
Brown, Elizabeth
Brink, Deon
Bailey, Paul
Brits, Rudolph
Finn, Judith
author_sort Ceklic, Ellen
collection PubMed
description BACKGROUND: Calls for emergency medical assistance at the scene of a motor vehicle crash (MVC) substantially contribute to the demand on ambulance services. Triage by emergency medical dispatch systems is therefore important, to ensure the right care is provided to the right patient, in the right amount of time. A lights and sirens (L&S) response is the highest priority ambulance response, also known as a priority one or hot response. In this context, over triage is defined as dispatching an ambulance with lights and sirens (L&S) to a low acuity MVC and under triage is not dispatching an ambulance with L&S to those who require urgent medical care. We explored the potential for crash characteristics to be used during emergency ambulance calls to identify those MVCs that required a L&S response. METHODS: We conducted a retrospective cohort study using ambulance and police data from 2014 to 2016. The predictor variables were crash characteristics (e.g. road surface), and Medical Priority Dispatch System (MPDS) dispatch codes. The outcome variable was the need for a L&S ambulance response. A Chi-square Automatic Interaction Detector technique was used to develop decision trees, with over/under triage rates determined for each tree. The model with an under/over triage rate closest to that prescribed by the American College of Surgeons Committee on Trauma (ACS COT) will be deemed to be the best model (under triage rate of ≤ 5% and over triage rate of between 25–35%. RESULTS: The decision tree with a 2.7% under triage rate was closest to that specified by the ACS COT, had as predictors—MPDS codes, trapped, vulnerable road user, anyone aged 75 + , day of the week, single versus multiple vehicles, airbag deployment, atmosphere, surface, lighting and accident type. This model had an over triage rate of 84.8%. CONCLUSIONS: We were able to derive a model with a reasonable under triage rate, however this model also had a high over triage rate. Individual EMS may apply the findings here to their own jurisdictions when dispatching to the scene of a MVC. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12873-022-00609-5.
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spelling pubmed-90742122022-05-07 A predictive ambulance dispatch algorithm to the scene of a motor vehicle crash: the search for optimal over and under triage rates Ceklic, Ellen Tohira, Hideo Ball, Stephen Brown, Elizabeth Brink, Deon Bailey, Paul Brits, Rudolph Finn, Judith BMC Emerg Med Research Article BACKGROUND: Calls for emergency medical assistance at the scene of a motor vehicle crash (MVC) substantially contribute to the demand on ambulance services. Triage by emergency medical dispatch systems is therefore important, to ensure the right care is provided to the right patient, in the right amount of time. A lights and sirens (L&S) response is the highest priority ambulance response, also known as a priority one or hot response. In this context, over triage is defined as dispatching an ambulance with lights and sirens (L&S) to a low acuity MVC and under triage is not dispatching an ambulance with L&S to those who require urgent medical care. We explored the potential for crash characteristics to be used during emergency ambulance calls to identify those MVCs that required a L&S response. METHODS: We conducted a retrospective cohort study using ambulance and police data from 2014 to 2016. The predictor variables were crash characteristics (e.g. road surface), and Medical Priority Dispatch System (MPDS) dispatch codes. The outcome variable was the need for a L&S ambulance response. A Chi-square Automatic Interaction Detector technique was used to develop decision trees, with over/under triage rates determined for each tree. The model with an under/over triage rate closest to that prescribed by the American College of Surgeons Committee on Trauma (ACS COT) will be deemed to be the best model (under triage rate of ≤ 5% and over triage rate of between 25–35%. RESULTS: The decision tree with a 2.7% under triage rate was closest to that specified by the ACS COT, had as predictors—MPDS codes, trapped, vulnerable road user, anyone aged 75 + , day of the week, single versus multiple vehicles, airbag deployment, atmosphere, surface, lighting and accident type. This model had an over triage rate of 84.8%. CONCLUSIONS: We were able to derive a model with a reasonable under triage rate, however this model also had a high over triage rate. Individual EMS may apply the findings here to their own jurisdictions when dispatching to the scene of a MVC. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12873-022-00609-5. BioMed Central 2022-05-06 /pmc/articles/PMC9074212/ /pubmed/35524169 http://dx.doi.org/10.1186/s12873-022-00609-5 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Research Article
Ceklic, Ellen
Tohira, Hideo
Ball, Stephen
Brown, Elizabeth
Brink, Deon
Bailey, Paul
Brits, Rudolph
Finn, Judith
A predictive ambulance dispatch algorithm to the scene of a motor vehicle crash: the search for optimal over and under triage rates
title A predictive ambulance dispatch algorithm to the scene of a motor vehicle crash: the search for optimal over and under triage rates
title_full A predictive ambulance dispatch algorithm to the scene of a motor vehicle crash: the search for optimal over and under triage rates
title_fullStr A predictive ambulance dispatch algorithm to the scene of a motor vehicle crash: the search for optimal over and under triage rates
title_full_unstemmed A predictive ambulance dispatch algorithm to the scene of a motor vehicle crash: the search for optimal over and under triage rates
title_short A predictive ambulance dispatch algorithm to the scene of a motor vehicle crash: the search for optimal over and under triage rates
title_sort predictive ambulance dispatch algorithm to the scene of a motor vehicle crash: the search for optimal over and under triage rates
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9074212/
https://www.ncbi.nlm.nih.gov/pubmed/35524169
http://dx.doi.org/10.1186/s12873-022-00609-5
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