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Evaluating the safety and patient impacts of an artificial intelligence command centre in acute hospital care: a mixed-methods protocol

INTRODUCTION: This paper presents a mixed-methods study protocol that will be used to evaluate a recent implementation of a real-time, centralised hospital command centre in the UK. The command centre represents a complex intervention within a complex adaptive system. It could support better operati...

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Autores principales: McInerney, Ciarán, McCrorie, Carolyn, Benn, Jonathan, Habli, Ibrahim, Lawton, Tom, Mebrahtu, Teumzghi F, Randell, Rebecca, Sheikh, Naeem, Johnson, Owen
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BMJ Publishing Group 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8889317/
https://www.ncbi.nlm.nih.gov/pubmed/35232784
http://dx.doi.org/10.1136/bmjopen-2021-054090
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author McInerney, Ciarán
McCrorie, Carolyn
Benn, Jonathan
Habli, Ibrahim
Lawton, Tom
Mebrahtu, Teumzghi F
Randell, Rebecca
Sheikh, Naeem
Johnson, Owen
author_facet McInerney, Ciarán
McCrorie, Carolyn
Benn, Jonathan
Habli, Ibrahim
Lawton, Tom
Mebrahtu, Teumzghi F
Randell, Rebecca
Sheikh, Naeem
Johnson, Owen
author_sort McInerney, Ciarán
collection PubMed
description INTRODUCTION: This paper presents a mixed-methods study protocol that will be used to evaluate a recent implementation of a real-time, centralised hospital command centre in the UK. The command centre represents a complex intervention within a complex adaptive system. It could support better operational decision-making and facilitate identification and mitigation of threats to patient safety. There is, however, limited research on the impact of such complex health information technology on patient safety, reliability and operational efficiency of healthcare delivery and this study aims to help address that gap. METHODS AND ANALYSIS: We will conduct a longitudinal mixed-method evaluation that will be informed by public-and-patient involvement and engagement. Interviews and ethnographic observations will inform iterations with quantitative analysis that will sensitise further qualitative work. Quantitative work will take an iterative approach to identify relevant outcome measures from both the literature and pragmatically from datasets of routinely collected electronic health records. ETHICS AND DISSEMINATION: This protocol has been approved by the University of Leeds Engineering and Physical Sciences Research Ethics Committee (#MEEC 20-016) and the National Health Service Health Research Authority (IRAS No.: 285933). Our results will be communicated through peer-reviewed publications in international journals and conferences. We will provide ongoing feedback as part of our engagement work with local trust stakeholders.
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spelling pubmed-88893172022-03-17 Evaluating the safety and patient impacts of an artificial intelligence command centre in acute hospital care: a mixed-methods protocol McInerney, Ciarán McCrorie, Carolyn Benn, Jonathan Habli, Ibrahim Lawton, Tom Mebrahtu, Teumzghi F Randell, Rebecca Sheikh, Naeem Johnson, Owen BMJ Open Health Informatics INTRODUCTION: This paper presents a mixed-methods study protocol that will be used to evaluate a recent implementation of a real-time, centralised hospital command centre in the UK. The command centre represents a complex intervention within a complex adaptive system. It could support better operational decision-making and facilitate identification and mitigation of threats to patient safety. There is, however, limited research on the impact of such complex health information technology on patient safety, reliability and operational efficiency of healthcare delivery and this study aims to help address that gap. METHODS AND ANALYSIS: We will conduct a longitudinal mixed-method evaluation that will be informed by public-and-patient involvement and engagement. Interviews and ethnographic observations will inform iterations with quantitative analysis that will sensitise further qualitative work. Quantitative work will take an iterative approach to identify relevant outcome measures from both the literature and pragmatically from datasets of routinely collected electronic health records. ETHICS AND DISSEMINATION: This protocol has been approved by the University of Leeds Engineering and Physical Sciences Research Ethics Committee (#MEEC 20-016) and the National Health Service Health Research Authority (IRAS No.: 285933). Our results will be communicated through peer-reviewed publications in international journals and conferences. We will provide ongoing feedback as part of our engagement work with local trust stakeholders. BMJ Publishing Group 2022-03-01 /pmc/articles/PMC8889317/ /pubmed/35232784 http://dx.doi.org/10.1136/bmjopen-2021-054090 Text en © Author(s) (or their employer(s)) 2022. Re-use permitted under CC BY. Published by BMJ. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed in accordance with the Creative Commons Attribution 4.0 Unported (CC BY 4.0) license, which permits others to copy, redistribute, remix, transform and build upon this work for any purpose, provided the original work is properly cited, a link to the licence is given, and indication of whether changes were made. See: https://creativecommons.org/licenses/by/4.0/.
spellingShingle Health Informatics
McInerney, Ciarán
McCrorie, Carolyn
Benn, Jonathan
Habli, Ibrahim
Lawton, Tom
Mebrahtu, Teumzghi F
Randell, Rebecca
Sheikh, Naeem
Johnson, Owen
Evaluating the safety and patient impacts of an artificial intelligence command centre in acute hospital care: a mixed-methods protocol
title Evaluating the safety and patient impacts of an artificial intelligence command centre in acute hospital care: a mixed-methods protocol
title_full Evaluating the safety and patient impacts of an artificial intelligence command centre in acute hospital care: a mixed-methods protocol
title_fullStr Evaluating the safety and patient impacts of an artificial intelligence command centre in acute hospital care: a mixed-methods protocol
title_full_unstemmed Evaluating the safety and patient impacts of an artificial intelligence command centre in acute hospital care: a mixed-methods protocol
title_short Evaluating the safety and patient impacts of an artificial intelligence command centre in acute hospital care: a mixed-methods protocol
title_sort evaluating the safety and patient impacts of an artificial intelligence command centre in acute hospital care: a mixed-methods protocol
topic Health Informatics
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8889317/
https://www.ncbi.nlm.nih.gov/pubmed/35232784
http://dx.doi.org/10.1136/bmjopen-2021-054090
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