Evidence map›Paper›PMID 35232784›Full record

ArticleBMJ open2022

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

Ciarán McInerney, Carolyn McCrorie, Jonathan Benn, Ibrahim Habli, Tom Lawton, Teumzghi F Mebrahtu, Rebecca Randell, Naeem Sheikh, Owen Johnson

Abstract read
In one paragraph

Article in BMJ open, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

6 citing papers in PubMed.

  1. Article
  2. Article
  3. Review
  4. Review
  5. The impact of hospital command centre on patient flow and data quality: findings from the UK National Health Service.International journal for quality in health care : journal of the International Society for Quality in Health Care · 2023
    Article
  6. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors.

Ciarán McInerneySchool of Computing, University of Leeds Faculty of Engineering and Physical Sciences, Leeds, UK C.McInerney@leeds.ac.uk.ORCID 0000-0001-7620-7110
Carolyn McCrorieBradford Royal Infirmary, Wolfson Centre for Applied Health Research, Bradford, UK.
Jonathan BennBradford Royal Infirmary, Wolfson Centre for Applied Health Research, Bradford, UK.
Ibrahim HabliDepartment of Computer Science, University of York, York, UK.
Tom LawtonBradford Royal Infirmary, Bradford Teaching Hospitals NHS Foundation Trust, Bradford, UK.
Teumzghi F MebrahtuSchool of Computing, University of Leeds Faculty of Engineering and Physical Sciences, Leeds, UK.ORCID 0000-0003-4821-2304
Rebecca RandellBradford Royal Infirmary, Wolfson Centre for Applied Health Research, Bradford, UK.ORCID 0000-0002-5856-4912
Naeem SheikhBradford Royal Infirmary, Wolfson Centre for Applied Health Research, Bradford, UK.
Owen JohnsonSchool of Computing, University of Leeds Faculty of Engineering and Physical Sciences, Leeds, UK.

Funding

Department of Health NIHR129483
6 · The paper itself

Abstract

introductionThis 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.

Indexed as

Artificial IntelligenceState MedicineHospitalsHumansPatient ParticipationReproducibility of Resultshealth informaticsinformation managementinformation technologytelemedicine

Identifiers

PMID35232784
PMCPMC8889317

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.