Evidence map›Paper›PMID 35212459›Full record

ArticleInternational wound journal2022

Reshaping wound care: Evaluation of an artificial intelligence app to improve wound assessment and management amid the COVID-19 pandemic.

Michelle Barakat-Johnson, Aaron Jones, Mitch Burger, Thomas Leong, Astrid Frotjold, Sue Randall, Bora Kim, Judith Fethney, Fiona Coyer

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in International wound journal, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers.

0numbers the graph read from it
0cells of the map it votes in
30citing 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

30 citing papers in PubMed.

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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

9 authors.

Michelle Barakat-JohnsonDepartment of Nursing and Midwifery Executive Services, Sydney Local Health District (SLHD), Sydney, New South Wales, Australia.ORCID https://orcid.org/0000-0001-7764-4515
Aaron JonesSusan Wakil School of Nursing and Midwifery, Faculty of Medicine and Health, University of Sydney, Sydney, New South Wales, Australia.
Mitch BurgerHealth Informatics Unit, Sydney Local Health District (SLHD), Sydney, New South Wales, Australia.
Thomas LeongNursing and Midwifery Services, Royal Prince Alfred Hospital, Sydney Local Health District, Sydney, New South Wales, Australia.
Astrid FrotjoldSusan Wakil School of Nursing and Midwifery, Faculty of Medicine and Health, University of Sydney, Sydney, New South Wales, Australia.
Sue RandallSusan Wakil School of Nursing and Midwifery, Faculty of Medicine and Health, University of Sydney, Sydney, New South Wales, Australia.
Bora KimSusan Wakil School of Nursing and Midwifery, Faculty of Medicine and Health, University of Sydney, Sydney, New South Wales, Australia.
Judith FethneySusan Wakil School of Nursing and Midwifery, Faculty of Medicine and Health, University of Sydney, Sydney, New South Wales, Australia.
Fiona CoyerIntensive Care Services, Royal Brisbane and Women's Hospital, Brisbane, Queensland, Australia.

Funding

eHealth NSW Grant
6 · The paper itself

Abstract

Wound documentation is integral to effective wound care, health data coding and facilitating continuity of care. This study evaluated the usability and effectiveness of an artificial intelligence application for wound assessment and management from a clinician-and-patient user perspective. A quasi-experimental design was conducted in four settings in an Australian health service. Data were collected from patients in the standard group (n = 166, 243 wounds) and intervention group (n = 124, 184 wounds), at baseline and post-intervention. Clinicians participated in a survey (n = 10) and focus group interviews (n = 13) and patients were interviewed (n = 4). Wound documentation data were analysed descriptively, and bivariate statistics were used to determine between-group differences. Thematic analysis of interviews was conducted. Compared with the standard group, wound documentation in the intervention group improved significantly (more than two items documented 24% vs 70%, P < .001). During the intervention, 101 out of 132 wounds improved (mean wound size reduction = 53.99%). Positive evaluations identified improvements such as instantaneous objective wound assessment, shared wound plans, increased patient adherence and enhanced efficiency in providing virtual care. The use of the application facilitated remote patient monitoring and reduced patient travel time while maintaining optimal wound care.

Indexed as

COVID-19Mobile ApplicationsArtificial IntelligenceAustraliaHealth ServicesHumansPandemicsartificial intelligencedigital applicationdocumentationwoundwound care

Identifiers

PMID35212459
PMCPMC9111327

What OpenQuestion 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 OpenQuestion graph.