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dc.contributor.authorCiarán,McInerney,
dc.contributor.authorCarolyn,McCrorie,
dc.contributor.authorJonathan,Benn,
dc.contributor.authorIbrahim,Habli,
dc.contributor.authorTom,Lawton,
dc.contributor.authorTeumzghi F,Mebrahtu,
dc.contributor.authorRebecca,Randell,
dc.contributor.authorNaeem,Sheikh,
dc.contributor.authorOwen,Johnson,
dc.date.accessioned2023-06-19T12:04:06Z
dc.date.accessioned2023-06-28T12:27:05Z
dc.date.available2023-06-19T12:04:06Z
dc.date.available2023-06-28T12:27:05Z
dc.date.issued2022-03-01
dc.identifier.citationMcInerney C, McCrorie C, Benn J et al (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.en_US
dc.identifier.urihttp://hdl.handle.net/10454/19491
dc.descriptionYesen_US
dc.description.abstractThis 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. 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. 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.en_US
dc.description.sponsorshipNational Institute for Health Research Health Service and Delivery Research Programme (NIHR129483)en_US
dc.language.isoenen_US
dc.rights(c) 2022 The Authors. This is an Open Access article distributed under the Creative Commons CC-BY license (https://creativecommons.org/licenses/by/4.0/)en_US
dc.subjectArtificial intelligenceen_US
dc.subjectAIen_US
dc.subjectCentralised command centreen_US
dc.subjectDecision makingen_US
dc.subjectPatient safetyen_US
dc.subjectInformation technologyen_US
dc.subjectOperational efficiencyen_US
dc.titleEvaluating the safety and patient impacts of an artificial intelligence command centre in acute hospital care: a mixed-methods protocol.en_US
dc.status.refereedYesen_US
dc.date.Accepted2022-01-11
dc.date.application2022-03-01
dc.typeArticleen_US
dc.type.versionPublished versionen_US
dc.identifier.doihttps://doi.org/10.1136/bmjopen-2021-054090
dc.rights.licenseCC-BYen_US
dc.date.updated2023-06-19T12:04:08Z
refterms.dateFOA2023-06-28T12:27:31Z
dc.openaccess.statusopenAccessen_US


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