Evidence map›Paper›PMID 32046991›Full record

ArticleEvidence-based mental health2020

Introducing artificial intelligence in acute psychiatric inpatient care: qualitative study of its use to conduct nursing observations.

Alvaro Barrera, Carol Gee, Andrew Wood, Oliver Gibson, Daniel Bayley, John Geddes

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Article in Evidence-based mental health, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 25 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
25citing papers in PubMed, 2 pooled it
–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

25 citing papers in PubMed, 2 syntheses or guidelines pooled it.

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  15. Adiaphorisation and the digital nursing gaze: Liquid surveillance in long-term care.Nursing philosophy : an international journal for healthcare professionals · 2023
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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

6 authors.

Alvaro BarreraUniversity of Oxford, Oxford, UK alvaro.barrera@psych.ox.ac.uk.ORCID http://orcid.org/0000-0003-4716-8487
Carol GeeOxford Health NHS Foundation Trust, Oxford, Oxfordshire, UK.
Andrew WoodOxford Health NHS Foundation Trust, Oxford, Oxfordshire, UK.
Oliver GibsonOxehealth, Oxford, UK.
Daniel BayleyOxehealth, Oxford, UK.
John GeddesUniversity of Oxford, Oxford, UK.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundAll patients admitted to an acute inpatient mental health unit must have nursing observations carried out at night either hourly or every 15 minutes, to ascertain that they are safe and breathing. However, while this practice ensures patient safety, it can also disturb patients' sleep, which in turn can impact negatively on their recovery.

objectiveThis article describes the process of introducing artificial intelligence ('digitally assisted nursing observations') in an acute mental health inpatient ward, to enable staff to carry out the hourly and the 15 minutes observations, minimising disruption of patients' sleep while maintaining their safety.

findingsThe preliminary data obtained indicate that the digitally assisted nursing observations agreed with the observations without sensors when both were carried out in parallel and that over an estimated 755 patient nights, the new system has not been associated with any untoward incidents. Preliminary qualitative data suggest that the new technology improves patients' and staff's experience at night. DISCUSSION: This project suggests that the digitally assisted nursing observations could maintain patients' safety while potentially improving patients' and staff's experience in an acute psychiatric ward. The limitations of this study, namely, its narrative character and the fact that patients were not randomised to the new technology, suggest taking the reported findings as qualitative and preliminary. CLINICAL IMPLICATIONS: These results suggest that the care provided at night in acute inpatient psychiatric units could be substantially improved with this technology. This warrants a more thorough and stringent evaluation.

Indexed as

Artificial IntelligenceMonitoring, PhysiologicObservationPsychiatric Department, HospitalPsychiatric NursingAcute DiseaseAdultHumansPatient SafetyQualitative Researchadult psychiatrydepression & mood disordersSchizophrenia & psychotic disorderssuicide & self-harm

Identifiers

PMID32046991
PMCPMC7034347

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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.