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Assuring safe artificial intelligence in critical ambulance service response: study protocol

INTRODUCTION: Early recognition of out-of-hospital cardiac arrest (OHCA) by ambulance service call centre operators is important so that cardiopulmonary resuscitation can be delivered immediately, but around 25% of OHCAs are not picked up by call centre operators. An artificial intelligence (AI) sys...

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Autores principales: Sujan, Mark, Thimbleby, Harold, Habli, Ibrahim, Cleve, Andreas, Maaløe, Lars, Rees, Nigel
Formato: Online Artículo Texto
Lenguaje:English
Publicado: The College of Paramedics 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9662146/
https://www.ncbi.nlm.nih.gov/pubmed/36452023
http://dx.doi.org/10.29045/14784726.2022.06.7.1.36
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author Sujan, Mark
Thimbleby, Harold
Habli, Ibrahim
Cleve, Andreas
Maaløe, Lars
Rees, Nigel
author_facet Sujan, Mark
Thimbleby, Harold
Habli, Ibrahim
Cleve, Andreas
Maaløe, Lars
Rees, Nigel
author_sort Sujan, Mark
collection PubMed
description INTRODUCTION: Early recognition of out-of-hospital cardiac arrest (OHCA) by ambulance service call centre operators is important so that cardiopulmonary resuscitation can be delivered immediately, but around 25% of OHCAs are not picked up by call centre operators. An artificial intelligence (AI) system has been developed to support call centre operators in the detection of OHCA. The study aims to (1) explore ambulance service stakeholder perceptions on the safety of OHCA AI decision support in call centres, and (2) develop a clinical safety case for the OHCA AI decision-support system. METHODS AND ANALYSIS: The study will be undertaken within the Welsh Ambulance Service. The study is part research and part service evaluation. The research utilises a qualitative study design based on thematic analysis of interview data. The service evaluation consists of the development of a clinical safety case based on document analysis, analysis of the AI model and its development process and informal interviews with the technology developer. CONCLUSIONS: AI presents many opportunities for ambulance services, but safety assurance requirements need to be understood. The ASSIST project will continue to explore and build the body of knowledge in this area.
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spelling pubmed-96621462023-06-01 Assuring safe artificial intelligence in critical ambulance service response: study protocol Sujan, Mark Thimbleby, Harold Habli, Ibrahim Cleve, Andreas Maaløe, Lars Rees, Nigel Br Paramed J Research Methodology INTRODUCTION: Early recognition of out-of-hospital cardiac arrest (OHCA) by ambulance service call centre operators is important so that cardiopulmonary resuscitation can be delivered immediately, but around 25% of OHCAs are not picked up by call centre operators. An artificial intelligence (AI) system has been developed to support call centre operators in the detection of OHCA. The study aims to (1) explore ambulance service stakeholder perceptions on the safety of OHCA AI decision support in call centres, and (2) develop a clinical safety case for the OHCA AI decision-support system. METHODS AND ANALYSIS: The study will be undertaken within the Welsh Ambulance Service. The study is part research and part service evaluation. The research utilises a qualitative study design based on thematic analysis of interview data. The service evaluation consists of the development of a clinical safety case based on document analysis, analysis of the AI model and its development process and informal interviews with the technology developer. CONCLUSIONS: AI presents many opportunities for ambulance services, but safety assurance requirements need to be understood. The ASSIST project will continue to explore and build the body of knowledge in this area. The College of Paramedics 2022-06-01 2022-06-01 /pmc/articles/PMC9662146/ /pubmed/36452023 http://dx.doi.org/10.29045/14784726.2022.06.7.1.36 Text en © 2022 The Author(s) https://creativecommons.org/licenses/by/2.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Methodology
Sujan, Mark
Thimbleby, Harold
Habli, Ibrahim
Cleve, Andreas
Maaløe, Lars
Rees, Nigel
Assuring safe artificial intelligence in critical ambulance service response: study protocol
title Assuring safe artificial intelligence in critical ambulance service response: study protocol
title_full Assuring safe artificial intelligence in critical ambulance service response: study protocol
title_fullStr Assuring safe artificial intelligence in critical ambulance service response: study protocol
title_full_unstemmed Assuring safe artificial intelligence in critical ambulance service response: study protocol
title_short Assuring safe artificial intelligence in critical ambulance service response: study protocol
title_sort assuring safe artificial intelligence in critical ambulance service response: study protocol
topic Research Methodology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9662146/
https://www.ncbi.nlm.nih.gov/pubmed/36452023
http://dx.doi.org/10.29045/14784726.2022.06.7.1.36
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