Evidence map›Paper›PMID 35272667›Full record

SynthesisImplementation science : IS2022

Do providers use computerized clinical decision support systems? A systematic review and meta-regression of clinical decision support uptake.

Andrew Kouri, Janet Yamada, Jeffrey Lam Shin Cheung, Stijn Van de Velde, Samir Gupta

Open access · goldAbstract readMeta-AnalysisSystematic Review
In one paragraph

Synthesis in Implementation science : IS, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 76 papers, 5 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
76citing papers in PubMed, 5 pooled it
31.4field-weighted citation impact, top 1% of its field
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

76 citing papers in PubMed, 5 syntheses or guidelines pooled it, 121 citations in OpenAlex.

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  3. Facilitators and Barriers of the Use of Prognostic Models for Clinical Decision Making in Acute Neurologic Care: A Systematic Review.Medical decision making : an international journal of the Society for Medical Decision Making · 2025
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16 more citing papers are in PubMed but not listed here.

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

5 authors at 3 institutions in 3 countries.

Andrew KouriDivision of Respirology, Department of Medicine, St. Michael's Hospital, Unity Health Toronto, 6 PGT, 30 Bond St, Toronto, ON, Canada.ORCID 0000-0002-2566-9187
Janet YamadaDaphne Cockwell School of Nursing, Faculty of Community Services, Ryerson University, Toronto, ON, Canada.
Jeffrey Lam Shin CheungKeenan Research Center, Li Ka Shing Knowledge Institute, St. Michael's Hospital, University of Toronto, Toronto, Ontario, Canada.
Stijn Van de VeldeDivision for Health Services, Norwegian Institute of Public Health, Oslo, Norway.
Samir GuptaDivision of Respirology, Department of Medicine, St. Michael's Hospital, Unity Health Toronto, 6 PGT, 30 Bond St, Toronto, ON, Canada. samir.gupta@unityhealth.to.
St. Michael's Hospital · CANorwegian Institute of Public Health · NOToronto Metropolitan University · CA

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundComputerized clinical decision support systems (CDSSs) are a promising knowledge translation tool, but often fail to meaningfully influence the outcomes they target. Low CDSS provider uptake is a potential contributor to this problem but has not been systematically studied. The objective of this systematic review and meta-regression was to determine reported CDSS uptake and identify which CDSS features may influence uptake.

methodsMedline, Embase, CINAHL, and the Cochrane Database of Controlled Trials were searched from January 2000 to August 2020. Randomized, non-randomized, and quasi-experimental trials reporting CDSS uptake in any patient population or setting were included. The main outcome extracted was CDSS uptake, reported as a raw proportion, and representing the number of times the CDSS was used or accessed over the total number of times it could have been interacted with. We also extracted context, content, system, and implementation features that might influence uptake, for each CDSS. Overall weighted uptake was calculated using random-effects meta-analysis and determinants of uptake were investigated using multivariable meta-regression.

resultsAmong 7995 citations screened, 55 studies involving 373,608 patients and 3607 providers met full inclusion criteria. Meta-analysis revealed that overall CDSS uptake was 34.2% (95% CI 23.2 to 47.1%). Uptake was only reported in 12.4% of studies that otherwise met inclusion criteria. Multivariable meta-regression revealed the following factors significantly associated with uptake: (1) formally evaluating the availability and quality of the patient data needed to inform CDSS advice; and (2) identifying and addressing other barriers to the behaviour change targeted by the CDSS. CONCLUSIONS AND RELEVANCE: System uptake was seldom reported in CDSS trials. When reported, uptake was low. This represents a major and potentially modifiable barrier to overall CDSS effectiveness. We found that features relating to CDSS context and implementation strategy best predicted uptake. Future studies should measure the impact of addressing these features as part of the CDSS implementation strategy. Uptake reporting must also become standard in future studies reporting CDSS intervention effects. REGISTRATION: Pre-registered on PROSPERO, CRD42018092337.

Indexed as

Decision Support Systems, ClinicalHumans

Identifiers

PMID35272667
PMCPMC8908582
OpenAlexW4221057293

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.