Evidence map›Paper›PMID 40225833›Full record

ArticleNursing research and practice2025

Feasibility of Licensed Vocational Nurses Using a CDS App to Communicate Signs and Symptoms of a UTI.

Alyce S Ashcraft, Donna C Owen, Kyle Johnson, Huaxin Song

Abstract read
In one paragraph

Article in Nursing research and practice, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

4 authors.

Alyce S Ashcraft, School of Nursing, Texas Tech University Health Sciences Center, 3601 4 Street, Stop 6264, Lubbock, Texas 79430, USA.ORCID https://orcid.org/0000-0002-3124-8987
Donna C Owen, School of Nursing, Texas Tech University Health Sciences Center, 3601 4 Street, Stop 6264, Lubbock, Texas 79430, USA.ORCID https://orcid.org/0000-0001-9644-0972
Kyle Johnson, School of Nursing, Texas Tech University Health Sciences Center, 3601 4 Street, Stop 6264, Lubbock, Texas 79430, USA.
Huaxin Song, School of Nursing, Texas Tech University Health Sciences Center, 3601 4 Street, Stop 6264, Lubbock, Texas 79430, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Urinary tract infections (UTIs) occurring in nursing home (NH) residents are poorly assessed and challenging to treat. The emergence of clinical decision support (CDS) technology provides an opportunity for improved diagnosis and treatment of UTIs in the NH. The purpose of this study was to report findings examining the feasibility of licensed vocational nurses (LVNs) using a CDS algorithm designed to directly guide assessment of a standardized NH resident experiencing symptoms of a potential UTI in a simulation setting at a university in the Southern United States. A structured observational design was used. A sample of ten practicing nurses used an algorithm developed by the authors from published UTI assessment and practice-driven criteria. Data collection was framed using (a) UTI-guided assessment tool, (b) analysis of LVN behavior and verbal interaction with the algorithm, and (c) postsimulation interviews about the algorithm and nurse-resident interactions. Results showed LVNs found the algorithm easy or very easy to use, their behaviors demonstrated high levels of engagement with the simulation while using the algorithm, and interviews supported the positive value LVNs placed on using an algorithmic approach for UTI assessment. The algorithm we developed fills a gap in the current approaches to diagnosing a UTI in the NH by focusing on the role of the LVN in data collection in relation to assessment of the resident.

Identifiers

PMID40225833
PMCPMC11986932

What OpenQuestion holds

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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.