Evidence map›Paper›PMID 38805252›Full record

ReviewJMIR nursing2024

Using AI-Based Technologies to Help Nurses Detect Behavioral Disorders: Narrative Literature Review.

Sofia Fernandes, Armin von Gunten, Henk Verloo

Abstract readReview
In one paragraph

Review in JMIR nursing, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Article
  2. Article
  3. Review
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

3 authors.

Sofia FernandesSchool of Health Sciences, University of Applied Sciences and Arts Western Switzerland (HES-SO), Sion, Switzerland.ORCID 0000-0003-4541-1041
Armin von GuntenService of Old Age Psychiatry, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland.ORCID 0000-0001-7852-3803
Henk VerlooSchool of Health Sciences, University of Applied Sciences and Arts Western Switzerland (HES-SO), Sion, Switzerland.ORCID 0000-0002-5375-3255

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe behavioral and psychological symptoms of dementia (BPSD) are common among people with dementia and have multiple negative consequences. Artificial intelligence-based technologies (AITs) have the potential to help nurses in the early prodromal detection of BPSD. Despite significant recent interest in the topic and the increasing number of available appropriate devices, little information is available on using AITs to help nurses striving to detect BPSD early.

objectiveThe aim of this study is to identify the number and characteristics of existing publications on introducing AITs to support nursing interventions to detect and manage BPSD early.

methodsA literature review of publications in the PubMed database referring to AITs and dementia was conducted in September 2023. A detailed analysis sought to identify the characteristics of these publications. The results were reported using a narrative approach.

resultsA total of 25 publications from 14 countries were identified, with most describing prospective observational studies. We identified three categories of publications on using AITs and they are (1) predicting behaviors and the stages and progression of dementia, (2) screening and assessing clinical symptoms, and (3) managing dementia and BPSD. Most of the publications referred to managing dementia and BPSD.

conclusionsDespite growing interest, most AITs currently in use are designed to support psychosocial approaches to treating and caring for existing clinical signs of BPSD. AITs thus remain undertested and underused for the early and real-time detection of BPSD. They could, nevertheless, provide nurses with accurate, reliable systems for assessing, monitoring, planning, and supporting safe therapeutic interventions.

Indexed as

Artificial IntelligenceDementiaHumansartificial intelligencebehavioral and psychological symptoms of dementiaearly detectionmanagementnarrative literature reviewneuropsychiatric symptoms

Identifiers

PMID38805252
PMCPMC11167323

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Registered trials

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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.