Evidence map›Paper›PMID 39487462›Full record

ArticleBMC medical informatics and decision making2024

A scoping review, novel taxonomy and catalogue of implementation frameworks for clinical decision support systems.

Jared M Wohlgemut, Erhan Pisirir, Rebecca S Stoner, Zane B Perkins, William Marsh, Nigel R M Tai, Evangelia Kyrimi

Registry-linked trialAbstract readScoping Review
PubMed Full text at PMC Publisher
In one paragraph

Article in BMC medical informatics and decision making, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT07634185 (Clinical Evaluation of an AI Risk Prediction and Decision Support System for Early Management of Injured Patients), which is not on this 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.

NCT07634185 early_phase1not yet recruitingnot on this mapstarted 2026, after this paper: background citation

Clinical Evaluation of an AI Risk Prediction and Decision Support System for Early Management of Injured Patients: a Stepped-wedge Cluster Randomised Trial

TypeinterventionalSponsorQueen Mary University of LondonRan2026 to 2027Enrolled1,200ConditionsTrauma, Injury, Decision Support Systems, ClinicalArmsAI-TRiPS Device
3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

  1. Article
  2. Review
  3. 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

7 authors.

Jared M WohlgemutCentre for Trauma Sciences, Blizard Institute, Queen Mary University of London, London, UK.
Erhan PisirirSchool of Electronic Engineering and Computer Science, Queen Mary University of London, Mile End Road, London, E1 4NS, UK.
Rebecca S StonerCentre for Trauma Sciences, Blizard Institute, Queen Mary University of London, London, UK.
Zane B PerkinsCentre for Trauma Sciences, Blizard Institute, Queen Mary University of London, London, UK.
William MarshSchool of Electronic Engineering and Computer Science, Queen Mary University of London, Mile End Road, London, E1 4NS, UK.
Nigel R M TaiCentre for Trauma Sciences, Blizard Institute, Queen Mary University of London, London, UK.
Evangelia KyrimiSchool of Electronic Engineering and Computer Science, Queen Mary University of London, Mile End Road, London, E1 4NS, UK. e.kyrimi@qmul.ac.uk.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe primary aim of this scoping review was to synthesise key domains and sub-domains described in existing clinical decision support systems (CDSS) implementation frameworks into a novel taxonomy and demonstrate most-studied and least-studied areas. Secondary objectives were to evaluate the frequency and manner of use of each framework, and catalogue frameworks by implementation stage.

methodsA scoping review of Pubmed, Scopus, Web of Science, PsychInfo and Embase was conducted on 12/01/2022, limited to English language, including 2000-2021. Each framework was categorised as addressing one or multiple stages of implementation: design and development, evaluation, acceptance and integration, and adoption and maintenance. Key parts of each framework were grouped into domains and sub-domains.

resultsOf 3550 titles identified, 58 papers were included. The most-studied implementation stage was acceptance and integration, while the least-studied was design and development. The three main framework uses were: for evaluating adoption, for understanding attitudes toward implementation, and for framework validation. The most frequently used framework was the Consolidated Framework for Implementation Research.

conclusionsMany frameworks have been published to overcome barriers to CDSS implementation and offer guidance towards successful adoption. However, for co-developers, choosing relevant frameworks may be a challenge. A taxonomy of domains addressed by CDSS implementation frameworks is provided, as well as a description of their use, and a catalogue of frameworks listed by the implementation stages they address. Future work should ensure best practices for CDSS design are adequately described, and existing frameworks are well-validated. An emphasis on collaboration between clinician and non-clinician affected parties may help advance the field.

Indexed as

Decision Support Systems, ClinicalHumansAdoptionClinical decision support systemDesignDevelopmentEvaluationFrameworksImplementationScoping review

Identifiers

PMID39487462
PMCPMC11531160

What OpenQuestion holds

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Registered trials

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.