Cargando…

Training emergency services’ dispatchers to recognise stroke: an interrupted time-series analysis

BACKGROUND: Stroke is a time-dependent medical emergency in which early presentation to specialist care reduces death and dependency. Up to 70% of all stroke patients obtain first medical contact from the Emergency Medical Services (EMS). Identifying ‘true stroke’ from an EMS call is challenging, wi...

Descripción completa

Detalles Bibliográficos
Autores principales: Watkins, Caroline L, Leathley, Michael J, Jones, Stephanie P, Ford, Gary A, Quinn, Tom, Sutton, Chris J
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2013
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3751943/
https://www.ncbi.nlm.nih.gov/pubmed/23947656
http://dx.doi.org/10.1186/1472-6963-13-318
_version_ 1782281707968266240
author Watkins, Caroline L
Leathley, Michael J
Jones, Stephanie P
Ford, Gary A
Quinn, Tom
Sutton, Chris J
author_facet Watkins, Caroline L
Leathley, Michael J
Jones, Stephanie P
Ford, Gary A
Quinn, Tom
Sutton, Chris J
author_sort Watkins, Caroline L
collection PubMed
description BACKGROUND: Stroke is a time-dependent medical emergency in which early presentation to specialist care reduces death and dependency. Up to 70% of all stroke patients obtain first medical contact from the Emergency Medical Services (EMS). Identifying ‘true stroke’ from an EMS call is challenging, with over 50% of strokes being misclassified. The aim of this study was to evaluate the impact of the training package on the recognition of stroke by Emergency Medical Dispatchers (EMDs). METHODS: This study took place in an ambulance service and a hospital in England using an interrupted time-series design. Suspected stroke patients were identified in one week blocks, every three weeks over an 18 month period, during which time the training was implemented. Patients were included if they had a diagnosis of stroke (EMS or hospital). The effect of the intervention on the accuracy of dispatch diagnosis was investigated using binomial (grouped) logistic regression. RESULTS: In the Pre-implementation period EMDs correctly identified 63% of stroke patients; this increased to 80% Post-implementation. This change was significant (p=0.003), reflecting an improvement in identifying stroke patients relative to the Pre-implementation period both the During-implementation (OR=4.10 [95% CI 1.58 to 10.66]) and Post-implementation (OR=2.30 [95% CI 1.07 to 4.92]) periods. For patients with a final diagnosis of stroke who had been dispatched as stroke there was a marginally non-significant 2.8 minutes (95% CI −0.2 to 5.9 minutes, p=0.068) reduction between Pre- and Post-implementation periods from call to arrival of the ambulance at scene. CONCLUSIONS: This is the first study to develop, implement and evaluate the impact of a training package for EMDs with the aim of improving the recognition of stroke. Training led to a significant increase in the proportion of stroke patients dispatched as such by EMDs; a small reduction in time from call to arrival at scene by the ambulance also appeared likely. The training package has been endorsed by the UK Stroke Forum Education and Training, and is free to access on-line.
format Online
Article
Text
id pubmed-3751943
institution National Center for Biotechnology Information
language English
publishDate 2013
publisher BioMed Central
record_format MEDLINE/PubMed
spelling pubmed-37519432013-08-24 Training emergency services’ dispatchers to recognise stroke: an interrupted time-series analysis Watkins, Caroline L Leathley, Michael J Jones, Stephanie P Ford, Gary A Quinn, Tom Sutton, Chris J BMC Health Serv Res Research Article BACKGROUND: Stroke is a time-dependent medical emergency in which early presentation to specialist care reduces death and dependency. Up to 70% of all stroke patients obtain first medical contact from the Emergency Medical Services (EMS). Identifying ‘true stroke’ from an EMS call is challenging, with over 50% of strokes being misclassified. The aim of this study was to evaluate the impact of the training package on the recognition of stroke by Emergency Medical Dispatchers (EMDs). METHODS: This study took place in an ambulance service and a hospital in England using an interrupted time-series design. Suspected stroke patients were identified in one week blocks, every three weeks over an 18 month period, during which time the training was implemented. Patients were included if they had a diagnosis of stroke (EMS or hospital). The effect of the intervention on the accuracy of dispatch diagnosis was investigated using binomial (grouped) logistic regression. RESULTS: In the Pre-implementation period EMDs correctly identified 63% of stroke patients; this increased to 80% Post-implementation. This change was significant (p=0.003), reflecting an improvement in identifying stroke patients relative to the Pre-implementation period both the During-implementation (OR=4.10 [95% CI 1.58 to 10.66]) and Post-implementation (OR=2.30 [95% CI 1.07 to 4.92]) periods. For patients with a final diagnosis of stroke who had been dispatched as stroke there was a marginally non-significant 2.8 minutes (95% CI −0.2 to 5.9 minutes, p=0.068) reduction between Pre- and Post-implementation periods from call to arrival of the ambulance at scene. CONCLUSIONS: This is the first study to develop, implement and evaluate the impact of a training package for EMDs with the aim of improving the recognition of stroke. Training led to a significant increase in the proportion of stroke patients dispatched as such by EMDs; a small reduction in time from call to arrival at scene by the ambulance also appeared likely. The training package has been endorsed by the UK Stroke Forum Education and Training, and is free to access on-line. BioMed Central 2013-08-15 /pmc/articles/PMC3751943/ /pubmed/23947656 http://dx.doi.org/10.1186/1472-6963-13-318 Text en Copyright © 2013 Watkins et al.; licensee BioMed Central Ltd. http://creativecommons.org/licenses/by/2.0 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
Watkins, Caroline L
Leathley, Michael J
Jones, Stephanie P
Ford, Gary A
Quinn, Tom
Sutton, Chris J
Training emergency services’ dispatchers to recognise stroke: an interrupted time-series analysis
title Training emergency services’ dispatchers to recognise stroke: an interrupted time-series analysis
title_full Training emergency services’ dispatchers to recognise stroke: an interrupted time-series analysis
title_fullStr Training emergency services’ dispatchers to recognise stroke: an interrupted time-series analysis
title_full_unstemmed Training emergency services’ dispatchers to recognise stroke: an interrupted time-series analysis
title_short Training emergency services’ dispatchers to recognise stroke: an interrupted time-series analysis
title_sort training emergency services’ dispatchers to recognise stroke: an interrupted time-series analysis
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3751943/
https://www.ncbi.nlm.nih.gov/pubmed/23947656
http://dx.doi.org/10.1186/1472-6963-13-318
work_keys_str_mv AT watkinscarolinel trainingemergencyservicesdispatcherstorecognisestrokeaninterruptedtimeseriesanalysis
AT leathleymichaelj trainingemergencyservicesdispatcherstorecognisestrokeaninterruptedtimeseriesanalysis
AT jonesstephaniep trainingemergencyservicesdispatcherstorecognisestrokeaninterruptedtimeseriesanalysis
AT fordgarya trainingemergencyservicesdispatcherstorecognisestrokeaninterruptedtimeseriesanalysis
AT quinntom trainingemergencyservicesdispatcherstorecognisestrokeaninterruptedtimeseriesanalysis
AT suttonchrisj trainingemergencyservicesdispatcherstorecognisestrokeaninterruptedtimeseriesanalysis