Evidence map›Paper›PMID 36197748›Full record

ArticleJournal of nursing management2022

Artificial intelligence for falls management in older adult care: A scoping review of nurses' role.

Siobhan O'Connor, Norina Gasteiger, Emma Stanmore, David C Wong, Jung Jae Lee

Open access · hybridAbstract readScoping Review
PubMed Full text at PMC Publisher
In one paragraph

Article in Journal of nursing management, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.

0numbers the graph read from it
0cells of the map it votes in
10citing papers in PubMed
3.2field-weighted citation impact, top 8% of its field
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

10 citing papers in PubMed, 31 citations in OpenAlex.

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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

5 authors at 2 institutions in 2 countries.

Siobhan O'ConnorDivision of Nursing, Midwifery and Social Work, School of Health Sciences, The University of Manchester, Manchester, UK.ORCID https://orcid.org/0000-0001-8579-1718
Norina GasteigerDivision of Nursing, Midwifery and Social Work, School of Health Sciences, The University of Manchester, Manchester, UK.ORCID https://orcid.org/0000-0001-7801-7417
Emma StanmoreDivision of Nursing, Midwifery and Social Work, School of Health Sciences, The University of Manchester, Manchester, UK.ORCID https://orcid.org/0000-0002-4522-5292
David C WongDivision of Informatics, Imaging and Data Sciences, The University of Manchester, Manchester, UK.ORCID https://orcid.org/0000-0001-8117-9193
Jung Jae LeeSchool of Nursing, The University of Hong Kong, Pokfulam, Hong Kong.ORCID https://orcid.org/0000-0001-9704-2116
University of Manchester · GBUniversity of Hong Kong · HK

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

aimThis study aims to synthesize evidence on nurses' involvement in artificial intelligence research for managing falls in older adults.

backgroundArtificial intelligence techniques are used to analyse health datasets to aid clinical decision making, patient care and service delivery but nurses' involvement in this area of research for managing falls in older adults remains unknown. EVALUATION: A scoping review was conducted. CINAHL, the Cochrane Library, Embase, MEDLI and PubMed were searched. Results were screened against inclusion criteria. Relevant data were extracted, and studies summarized using a descriptive approach. KEY ISSUES: The evidence shows many artificial intelligence techniques, particularly machine learning, are used to identify falls risk factors and build predictive models that could help prevent falls in older adults, with nurses leading and participating in this research.

conclusionFurther rigorous experimental research is needed to determine the effectiveness of algorithms in predicting aspects of falls in older adults and how to implement artificial intelligence tools in gerontological nursing practice. IMPLICATIONS FOR NURSING MANAGEMENT: Nurses should pursue interdisciplinary collaborations and educational opportunities in artificial intelligence, so they can actively contribute to research on falls management. Nurses should facilitate the collection of digital falls datasets to support this emerging research agenda and the care of older adults.

Indexed as

Geriatric NursingNurse's RoleAgedArtificial IntelligenceHumansartificial intelligencefallsmachine learningnatural language processingnursing

Identifiers

PMID36197748
PMCPMC10092211
OpenAlexW4302028726

What Socratic holds

Textmetadata
LicenceCC BY-NC
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 Socratic graph.