Evidence map›Paper›PMID 41353275›Full record

ArticleNPJ digital medicine2025

Mixed methods evaluation of a clinical decision support system to reduce variation in healthcare.

Anton Pak, Samantha T Robertson, Bao Hoang Nguyen, Tobias Hodgson, Clair Sullivan, Andrew Burton-Jones

Abstract read
In one paragraph

Article in NPJ digital medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Review
  2. 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

6 authors.

Anton PakCentre for the Business and Economics of Health, The University of Queensland, Brisbane, QLD, Australia. a.pak@uq.edu.au.
Samantha T RobertsonQueensland Digital Health Centre, The University of Queensland, Brisbane, QLD, Australia.
Bao Hoang NguyenCentre for the Business and Economics of Health, The University of Queensland, Brisbane, QLD, Australia.
Tobias HodgsonUQ Business School, The University of Queensland, Brisbane, QLD, Australia.
Clair SullivanQueensland Digital Health Centre, The University of Queensland, Brisbane, QLD, Australia.
Andrew Burton-JonesUQ Business School, The University of Queensland, Brisbane, QLD, Australia. abj@business.uq.edu.au.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Evidence that clinical decision support systems (CDSS) reduce unwarranted variation in care and improve patient outcomes remains limited, particularly for acute, high-risk conditions where deviations from clinical guidelines are costly. We evaluated utilisation of a CDSS-embedded care plan within an electronic medical record designed to support timely, protocol-driven management of a complex metabolic emergency. Among 345 episodes, the CDSS was used in just over half of cases; yet a 1% increase in utilisation was associated with a reduction in hospital length of stay by approximately 1.3%, and any use was linked to a 3.3% reduction in in-hospital mortality. Fourteen clinician interviews supported these findings but highlighted barriers such as limited awareness and lack of training that constrain meaningful use. Quantitative and qualitative evidence together suggest that well-designed CDSS care plans can reduce variation and improve outcomes, while emphasising the need for awareness, education, and workflow alignment to unlock greater value.

Identifiers

PMID41353275
PMCPMC12739111

What OpenQuestion holds

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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.