Evidence map›Paper›PMID 39985249›Full record

ArticleAustralasian journal on ageing2025

Pilot implementation of an electronic diagnostic support tool (AiD-DST) designed to identify the cause(s) of delirium.

Elizabeth Leong, Stephane Chang, Kristi Yearwood, Eamonn Eeles, Stephanie Yerkovich, Carolina Ling, Andrew Teodorczuk, Nadeeka Dissanayaka

Abstract read
In one paragraph

Article in Australasian journal on ageing, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Article
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

8 authors.

Elizabeth LeongInternal Medicine Services, The Prince Charles Hospital, Brisbane, Queensland, Australia.
Stephane ChangInternal Medicine Services, The Prince Charles Hospital, Brisbane, Queensland, Australia.
Kristi YearwoodInternal Medicine Services, The Prince Charles Hospital, Brisbane, Queensland, Australia.
Eamonn EelesInternal Medicine Services, The Prince Charles Hospital, Brisbane, Queensland, Australia.ORCID https://orcid.org/0000-0002-5647-2512
Stephanie YerkovichMenzies School of Health Research, Brisbane, Queensland, Australia.
Carolina LingInternal Medicine Services, The Prince Charles Hospital, Brisbane, Queensland, Australia.
Andrew TeodorczukUniversity of Queensland, Academy for Medical Education, Brisbane, Queensland, Australia.
Nadeeka DissanayakaUQ Centre for Clinical Research, The University of Queensland, Herston, Brisbane, Queensland, Australia.ORCID https://orcid.org/0000-0002-0164-3337

Funding

Department of Health, Queensland
6 · The paper itself

Abstract

OBJECTIVE(S): The identification of cause(s) of delirium remains a clinical challenge within medicine. Our group have previously successfully developed and tested the Aetiology in Delirium-Decision Support Tool (AiD-DST). The AiD-DST is designed to help medical professionals close the gap on the detection of cause(s) of delirium. Here, we report on use of AiD-DST in the real-world setting.

methodsA real-world implementation study of the AiD-DST within a general medical ward of a metropolitan hospital was conducted over a 10-week period. A mixed method evaluation was performed based upon the RE-AIM Framework that incorporates reach, effectiveness, adoption, implementation and maintenance of an intervention.

resultsReach: fifty-three out of 87 (61%) eligible doctors consented to participation in the study. EFFECTIVENESS: A mean of 4.3 diagnoses were generated per patient with no difference in frequency when compared with historical control (z = 1.36; p = .17). Average usability score was 5.86 (SD = 1.15) on a 7-point scale, with 93% of respondents being satisfied with the AiD-DST. Free text feedback comprised themes of accessibility, ergonomics, diagnostic accuracy and applicability of AiD-DST to related conditions. IMPLEMENTATION: Instrument completion rate was 98% (n = 49/50), with a median completion time of 90 s. Maintenance: Sixty-seven % of uses of AiD-DST occurred in the second half of the study (p = .3). Following the initiation period there was an increase in use (r = .79; p = 02).

conclusionProof of principle was demonstrated for local implementation of a diagnostic support tool (AiD-DST).

Indexed as

Decision Support Systems, ClinicalDecision Support TechniquesDeliriumAgedFemaleHumansMaleMiddle AgedPilot ProjectsPredictive Value of TestsReproducibility of Resultsdeliriumdigital technologypoint‐of‐care diagnostics

Identifiers

PMID39985249
PMCPMC11845965

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LicenceCC BY-NC-ND
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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.