McInerney, CD orcid.org/0000-0001-7620-7110, McCrorie, C, Benn, J orcid.org/0000-0001-5919-9905 et al. (6 more authors) (2022) Evaluating the safety and patient impacts of an Artificial Intelligence Command Centre in acute hospital care: A mixed-methods protocol. BMJ Open, 12 (3). e054090. ISSN 2044-6055
Abstract
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.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © Author(s) (or their employer(s)) 2022. Re-use permitted under CC BY. Published by BMJ. 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. |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Computing (Leeds) The University of Leeds > Faculty of Medicine and Health (Leeds) > School of Psychology (Leeds) |
Funding Information: | Funder Grant number NIHR National Inst Health Research NIHR129483 |
Depositing User: | Symplectic Publications |
Date Deposited: | 31 Jan 2022 15:53 |
Last Modified: | 25 Jun 2023 22:53 |
Status: | Published |
Publisher: | BMJ Publishing Group |
Identification Number: | 10.1136/bmjopen-2021-054090 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:183029 |