Implementing an artificial intelligence command centre in the NHS: a mixed-methods study
Rattachement africain : gb, nl. Niveau de preuve : code pays fourni par la source.
Le résumé fourni par la source
Background: Hospital 'command centres' use digital technologies to collect, analyse and present real-time information that may improve patient flow and patient safety. Bradford Royal Infirmary has trialled this approach and presents an opportunity to evaluate effectiveness to inform future adoption in the United Kingdom. Objective: To evaluate the impact of the Bradford Command Centre on patient care and organisational processes. Design: A comparative mixed-methods study. Operational data from a study and control site were collected and analysed. The intervention was observed, and staff at both sites were interviewed. Analysis was grounded in a literature review and the results were synthesised to form conclusions about the intervention. Setting: The study site was Bradford Royal Infirmary, a large teaching hospital in the city of Bradford, United Kingdom. The control site was Huddersfield Royal Infirmary in the nearby city of Huddersfield. Participants: Thirty-six staff members were interviewed and/or observed. Intervention: The implementation of a digitally enabled hospital command centre. Main outcome measures: Qualitative perspectives on hospital management. Quantitative metrics on patient flow, patient safety, data quality. Data sources: Anonymised electronic health record data. Ethnographic observations including interviews with hospital staff. Cross-industry review including relevant literature and expert panel interviews. Results: The Command Centre was implemented successfully and has improved staff confidence of better operational control. Unintended consequences included tensions between localised and centralised decision-making and variable confidence in the quality of data available. The Command Centre supported the hospital through the COVID-19 pandemic, but the direct impact of the Command Centre was difficult to measure as the pandemic forced all hospitals, including the study and control sites, to innovate rapidly. Late in the study we learnt that the control site had visited the study site and replicated some aspects of the command centre themselves; we were unable to explore this in detail. There was no significant difference between pre- and post-intervention periods for the quantitative outcome measures and no conclusive impact on patient flow and data quality. Staff and patients supported the command-centre approaches but patients expressed concern that individual needs might get lost to 'the system'. Conclusions: Qualitative evidence suggests the Command Centre implementation was successful, but it proved challenging to link quantitative evidence to specific technology interventions. Staff were positive about the benefits and emphasised that these came from the way they adapted to and used the new technology rather than the technology per se. Limitations: The COVID-19 pandemic disrupted care patterns and forced rapid innovation which reduced our ability to compare study and control sites and data before, during and after the intervention. Future work: We plan to follow developments at Bradford and in command centres in the National Health Service in order to share learning. Our mixed-methods approach should be of interest to future studies attempting similar evaluation of complex digitally enabled whole-system changes. Study registration: The study is registered as IRAS No.: 285933. Funding: ; Vol. 12, No. 41. See the NIHR Funding and Awards website for further award information.
Ce résumé expose les affirmations des auteurs. BNTIC ne l’interprète pas comme une validation indépendante des résultats.
Le contrôle bibliographique ouvert
DOI retrouvé dans Crossref DOI retrouvé ; titre concordant.
- Titre Crossref
- Implementing an artificial intelligence command centre in the NHS: a mixed-methods study
- Date Crossref
- 01/10/2024
- Éditeur
- National Institute for Health and Care Research
- Type
- journal-article
Ce recoupement confirme des métadonnées liées au DOI. Il ne confirme ni la méthode ni les conclusions de l’étude, et il ne compte pas comme une seconde source scientifique indépendante.
Où se fait cette recherche
-
University of Leeds pays non établi dans la noticeUniversité ou école supérieure
-
University of Huddersfield pays non établi dans la noticeUniversité ou école supérieure
-
University of Sheffield pays non établi dans la noticeUniversité ou école supérieure
-
Bradford Royal Infirmary pays non établi dans la noticeÉtablissement de santé
-
Bradford Institute for Health Research pays non établi dans la noticeOrganisation à but non lucratif
-
House of Representatives pays non établi dans la noticeOrganisme public
-
University of York Department of Computer Science pays non établi dans la noticeUniversité ou école supérieure
-
University of Bradford pays non établi dans la noticeUniversité ou école supérieure
-
School of Computing pays non établi dans la noticeUniversité ou école supérieure
-
School of Human and Health Sciences pays non établi dans la noticeUniversité ou école supérieure
-
School of Medicine and Population Health Academic Unit of Primary Medical Care pays non établi dans la noticeUniversité ou école supérieure
-
School of Psychology pays non établi dans la noticeUniversité ou école supérieure
University of Leeds, University of Huddersfield et University of Sheffield, avec 9 autres affiliations.
Une affiliation ne permet pas de déduire la nationalité d’un auteur.