Cargando…

Artificial intelligence in Emergency Medical Services dispatching: assessing the potential impact of an automatic speech recognition software on stroke detection taking the Capital Region of Denmark as case in point

BACKGROUND AND PURPOSE: Stroke recognition at the Emergency Medical Services (EMS) impacts the stroke treatment and thus the related health outcome. At the EMS Copenhagen 66.2% of strokes are detected by the Emergency Medical Dispatcher (EMD) and in Denmark approximately 50% of stroke patients arriv...

Descripción completa

Detalles Bibliográficos
Autores principales: Scholz, Mirjam Lisa, Collatz-Christensen, Helle, Blomberg, Stig Nikolaj Fasmer, Boebel, Simone, Verhoeven, Jeske, Krafft, Thomas
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9097123/
https://www.ncbi.nlm.nih.gov/pubmed/35549978
http://dx.doi.org/10.1186/s13049-022-01020-6
_version_ 1784706113666547712
author Scholz, Mirjam Lisa
Collatz-Christensen, Helle
Blomberg, Stig Nikolaj Fasmer
Boebel, Simone
Verhoeven, Jeske
Krafft, Thomas
author_facet Scholz, Mirjam Lisa
Collatz-Christensen, Helle
Blomberg, Stig Nikolaj Fasmer
Boebel, Simone
Verhoeven, Jeske
Krafft, Thomas
author_sort Scholz, Mirjam Lisa
collection PubMed
description BACKGROUND AND PURPOSE: Stroke recognition at the Emergency Medical Services (EMS) impacts the stroke treatment and thus the related health outcome. At the EMS Copenhagen 66.2% of strokes are detected by the Emergency Medical Dispatcher (EMD) and in Denmark approximately 50% of stroke patients arrive at the hospital within the time-to-treatment. An automatic speech recognition software (ASR) can increase the recognition of Out-of-Hospital cardiac arrest (OHCA) at the EMS by 16%. This research aims to analyse the potential impact an ASR could have on stroke recognition at the EMS Copenhagen and the related treatment. METHODS: Stroke patient data (n = 9049) from the years 2016–2018 were analysed retrospectively, regarding correlations between stroke detection at the EMS and stroke specific, as well as personal characteristics such as stroke type, sex, age, weekday, time of day, year, EMS number contacted, and treatment. The possible increase in stroke detection through an ASR and the effect on stroke treatment was calculated based on the impact of an existing ASR to detect OHCA from CORTI AI. RESULTS: The Chi-Square test with the respective post-hoc test identified a negative correlation between stroke detection and females, the 1813-Medical Helpline, as well as weekends, and a positive correlation between stroke detection and treatment and thrombolysis. While the association analysis showed a moderate correlation between stroke detection and treatment the correlation to the other treatment options was weak or very weak. A potential increase in stroke detection to 61.19% with an ASR and hence an increase of thrombolysis by 5% in stroke patients calling within time-to-treatment was predicted. CONCLUSIONS: An ASR can potentially improve stroke recognition by EMDs and subsequent stroke treatment at the EMS Copenhagen. Based on the analysis results improvement of stroke recognition is particularly relevant for females, younger stroke patients, calls received through the 1813-Medical Helpline, and on weekends. TRIAL REGISTRATION: This study was registered at the Danish Data Protection Agency (PVH-2014-002) and the Danish Patient Safety Authority (R-21013122).
format Online
Article
Text
id pubmed-9097123
institution National Center for Biotechnology Information
language English
publishDate 2022
publisher BioMed Central
record_format MEDLINE/PubMed
spelling pubmed-90971232022-05-13 Artificial intelligence in Emergency Medical Services dispatching: assessing the potential impact of an automatic speech recognition software on stroke detection taking the Capital Region of Denmark as case in point Scholz, Mirjam Lisa Collatz-Christensen, Helle Blomberg, Stig Nikolaj Fasmer Boebel, Simone Verhoeven, Jeske Krafft, Thomas Scand J Trauma Resusc Emerg Med Original Research BACKGROUND AND PURPOSE: Stroke recognition at the Emergency Medical Services (EMS) impacts the stroke treatment and thus the related health outcome. At the EMS Copenhagen 66.2% of strokes are detected by the Emergency Medical Dispatcher (EMD) and in Denmark approximately 50% of stroke patients arrive at the hospital within the time-to-treatment. An automatic speech recognition software (ASR) can increase the recognition of Out-of-Hospital cardiac arrest (OHCA) at the EMS by 16%. This research aims to analyse the potential impact an ASR could have on stroke recognition at the EMS Copenhagen and the related treatment. METHODS: Stroke patient data (n = 9049) from the years 2016–2018 were analysed retrospectively, regarding correlations between stroke detection at the EMS and stroke specific, as well as personal characteristics such as stroke type, sex, age, weekday, time of day, year, EMS number contacted, and treatment. The possible increase in stroke detection through an ASR and the effect on stroke treatment was calculated based on the impact of an existing ASR to detect OHCA from CORTI AI. RESULTS: The Chi-Square test with the respective post-hoc test identified a negative correlation between stroke detection and females, the 1813-Medical Helpline, as well as weekends, and a positive correlation between stroke detection and treatment and thrombolysis. While the association analysis showed a moderate correlation between stroke detection and treatment the correlation to the other treatment options was weak or very weak. A potential increase in stroke detection to 61.19% with an ASR and hence an increase of thrombolysis by 5% in stroke patients calling within time-to-treatment was predicted. CONCLUSIONS: An ASR can potentially improve stroke recognition by EMDs and subsequent stroke treatment at the EMS Copenhagen. Based on the analysis results improvement of stroke recognition is particularly relevant for females, younger stroke patients, calls received through the 1813-Medical Helpline, and on weekends. TRIAL REGISTRATION: This study was registered at the Danish Data Protection Agency (PVH-2014-002) and the Danish Patient Safety Authority (R-21013122). BioMed Central 2022-05-12 /pmc/articles/PMC9097123/ /pubmed/35549978 http://dx.doi.org/10.1186/s13049-022-01020-6 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 Original Research
Scholz, Mirjam Lisa
Collatz-Christensen, Helle
Blomberg, Stig Nikolaj Fasmer
Boebel, Simone
Verhoeven, Jeske
Krafft, Thomas
Artificial intelligence in Emergency Medical Services dispatching: assessing the potential impact of an automatic speech recognition software on stroke detection taking the Capital Region of Denmark as case in point
title Artificial intelligence in Emergency Medical Services dispatching: assessing the potential impact of an automatic speech recognition software on stroke detection taking the Capital Region of Denmark as case in point
title_full Artificial intelligence in Emergency Medical Services dispatching: assessing the potential impact of an automatic speech recognition software on stroke detection taking the Capital Region of Denmark as case in point
title_fullStr Artificial intelligence in Emergency Medical Services dispatching: assessing the potential impact of an automatic speech recognition software on stroke detection taking the Capital Region of Denmark as case in point
title_full_unstemmed Artificial intelligence in Emergency Medical Services dispatching: assessing the potential impact of an automatic speech recognition software on stroke detection taking the Capital Region of Denmark as case in point
title_short Artificial intelligence in Emergency Medical Services dispatching: assessing the potential impact of an automatic speech recognition software on stroke detection taking the Capital Region of Denmark as case in point
title_sort artificial intelligence in emergency medical services dispatching: assessing the potential impact of an automatic speech recognition software on stroke detection taking the capital region of denmark as case in point
topic Original Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9097123/
https://www.ncbi.nlm.nih.gov/pubmed/35549978
http://dx.doi.org/10.1186/s13049-022-01020-6
work_keys_str_mv AT scholzmirjamlisa artificialintelligenceinemergencymedicalservicesdispatchingassessingthepotentialimpactofanautomaticspeechrecognitionsoftwareonstrokedetectiontakingthecapitalregionofdenmarkascaseinpoint
AT collatzchristensenhelle artificialintelligenceinemergencymedicalservicesdispatchingassessingthepotentialimpactofanautomaticspeechrecognitionsoftwareonstrokedetectiontakingthecapitalregionofdenmarkascaseinpoint
AT blombergstignikolajfasmer artificialintelligenceinemergencymedicalservicesdispatchingassessingthepotentialimpactofanautomaticspeechrecognitionsoftwareonstrokedetectiontakingthecapitalregionofdenmarkascaseinpoint
AT boebelsimone artificialintelligenceinemergencymedicalservicesdispatchingassessingthepotentialimpactofanautomaticspeechrecognitionsoftwareonstrokedetectiontakingthecapitalregionofdenmarkascaseinpoint
AT verhoevenjeske artificialintelligenceinemergencymedicalservicesdispatchingassessingthepotentialimpactofanautomaticspeechrecognitionsoftwareonstrokedetectiontakingthecapitalregionofdenmarkascaseinpoint
AT krafftthomas artificialintelligenceinemergencymedicalservicesdispatchingassessingthepotentialimpactofanautomaticspeechrecognitionsoftwareonstrokedetectiontakingthecapitalregionofdenmarkascaseinpoint