Evidence map›Paper›PMID 30126421›Full record

SynthesisImplementation science : IS2018

A systematic review of trials evaluating success factors of interventions with computerised clinical decision support.

Stijn Van de Velde, Annemie Heselmans, Nicolas Delvaux, Linn Brandt, Luis Marco-Ruiz, David Spitaels, Hanne Cloetens, Tiina Kortteisto, Pavel Roshanov, Ilkka Kunnamo and 3 more

Abstract readSystematic Review
In one paragraph

Synthesis in Implementation science : IS, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 81 papers, 12 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
81citing papers in PubMed, 12 pooled it
–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

81 citing papers in PubMed, 12 syntheses or guidelines pooled it.

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  8. A systematic review of theoretical constructs in CDS literature.BMC medical informatics and decision making · 2021
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  10. Quality and effectiveness of osteoporosis treatment decision aids: a systematic review and environmental scan.Osteoporosis international : a journal established as result of cooperation between the European Foundation for Osteoporosis and the National Osteoporosis Foundation of the USA · 2020
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21 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

13 authors.

Stijn Van de VeldeCentre for Informed Health Choices, Division for Health Services, Norwegian Institute of Public Health, Oslo, Norway. Stijn.vandevelde@fhi.no.ORCID 0000-0001-8908-3823
Annemie HeselmansDepartment of Public Health and Primary Care, KU Leuven, Leuven, Belgium.
Nicolas DelvauxDepartment of Public Health and Primary Care, KU Leuven, Leuven, Belgium.
Linn BrandtMAGIC non-profit research and innovation programme, Oslo, Norway.
Luis Marco-RuizNorwegian Centre for E-health Research, Tromsø, Norway.
David SpitaelsDepartment of Public Health and Primary Care, KU Leuven, Leuven, Belgium.
Hanne CloetensFlemish College of General Practitioners, Antwerp, Belgium.
Tiina KortteistoDepartment of Internal Medicine, Tampere University Hospital, Tampere, Finland.
Pavel RoshanovDepartment of Medicine, McMaster University, Hamilton, Canada.
Ilkka KunnamoDuodecim, Scientific Society of Finnish Physicians, Helsinki, Finland.
Bert AertgeertsDepartment of Public Health and Primary Care, KU Leuven, Leuven, Belgium.
Per Olav VandvikCentre for Informed Health Choices, Division for Health Services, Norwegian Institute of Public Health, Oslo, Norway.
Signe FlottorpCentre for Informed Health Choices, Division for Health Services, Norwegian Institute of Public Health, Oslo, Norway.

Funding

Horizon 2020 654981
6 · The paper itself

Abstract

backgroundComputerised clinical decision support (CDS) can potentially better inform decisions, and it can help with the management of information overload. It is perceived to be a key component of a learning health care system. Despite its increasing implementation worldwide, it remains uncertain why the effect of CDS varies and which factors make CDS more effective.

objectiveTo examine which factors make CDS strategies more effective on a number of outcomes, including adherence to recommended practice, patient outcome measures, economic measures, provider or patient satisfaction, and medical decision quality.

methodsWe identified randomised controlled trials, non-randomised trials, and controlled before-and-after studies that directly compared CDS implementation with a given factor to CDS without that factor by searching CENTRAL, MEDLINE, EMBASE, and CINAHL and checking reference lists of relevant studies. We considered CDS with any objective for any condition in any healthcare setting. We included CDS interventions that were either displayed on screen or provided on paper and that were directed at healthcare professionals or targeted at both professionals and patients. The reviewers screened the potentially relevant studies in duplicate. They extracted data and assessed risk of bias in independent pairs or individually followed by a double check by another reviewer. We summarised results using medians and interquartile ranges and rated our certainty in the evidence using the GRADE system.

resultsWe identified 66 head-to-head trials that we synthesised across 14 comparisons of CDS intervention factors. Providing CDS automatically versus on demand led to large improvements in adherence. Displaying CDS on-screen versus on paper led to moderate improvements and making CDS more versus less patient-specific improved adherence modestly. When CDS interventions were combined with professional-oriented strategies, combined with patient-oriented strategies, or combined with staff-oriented strategies, then adherence improved slightly. Providing CDS to patients slightly increased adherence versus CDS aimed at the healthcare provider only. Making CDS advice more explicit and requiring users to respond to the advice made little or no difference. The CDS intervention factors made little or no difference to patient outcomes. The results for economic outcomes and satisfaction outcomes were sparse.

conclusionMultiple factors may affect the success of CDS interventions. CDS may be more effective when the advice is provided automatically and displayed on-screen and when the suggestions are more patient-specific. CDS interventions combined with other strategies probably also improves adherence. Providing CDS directly to patients may also positively affect adherence. The certainty of the evidence was low to moderate for all factors.

trial registrationPROSPERO, CRD42016033738.

Indexed as

Decision Support Systems, ClinicalDecision Support TechniquesHumansQuality of LifeRandomized Controlled Trials as TopicClinical computerised decision support systemsEvidence-based medicineGuideline adherenceImplementationPractice guidelinesSystematic review

Identifiers

PMID30126421
PMCPMC6102833

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.