Evidence map›Paper›PMID 32943437›Full record

SynthesisBMJ (Clinical research ed.)2020

Computerised clinical decision support systems and absolute improvements in care: meta-analysis of controlled clinical trials.

Janice L Kwan, Lisha Lo, Jacob Ferguson, Hanna Goldberg, Juan Pablo Diaz-Martinez, George Tomlinson, Jeremy M Grimshaw, Kaveh G Shojania

Abstract readMeta-AnalysisSystematic Review
PubMed Full text at PMC Publisher
In one paragraph

Synthesis in BMJ (Clinical research ed.), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 228 papers, 22 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
228citing papers in PubMed, 22 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

228 citing papers in PubMed, 22 syntheses or guidelines pooled it.

  1. Pooled it
  2. Pooled it
  3. Pooled it
  4. Pooled it
  5. Pooled it
  6. Effect of electronic drug-drug interaction alerts on patient and clinician outcomes: a systematic review.Journal of the American Medical Informatics Association : JAMIA · 2025
    Pooled it
  7. Pooled it
  8. Pooled it
  9. Pooled it
  10. Pooled it
  11. Pooled it
  12. Pooled it
  13. Pooled it
  14. Pooled it
  15. Pooled it
  16. Pooled it
  17. Pooled it
  18. Pooled it
  19. Pooled it
  20. Pooled it

168 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

8 authors.

Janice L KwanSinai Health System, Department of Medicine, 600 University Avenue, Toronto, ON M5G 1X5, Canada janice.kwan@utoronto.ca.ORCID 0000-0001-9252-0609
Lisha LoCentre for Quality Improvement and Patient Safety, University of Toronto, Toronto, ON, Canada.
Jacob FergusonFaculty of Medicine, University of Toronto, Toronto, ON, Canada.
Hanna GoldbergFaculty of Medicine, University of Toronto, Toronto, ON, Canada.
Juan Pablo Diaz-MartinezBiostatistics Research Unit, University Health Network and Sinai Health System, Toronto, ON, Canada.
George TomlinsonBiostatistics Research Unit, University Health Network and Sinai Health System, Toronto, ON, Canada.
Jeremy M GrimshawClinical Epidemiology Program, Ottawa Hospital Research Institute and Department of Medicine, University of Ottawa, Ottawa, ON, Canada.
Kaveh G ShojaniaDepartment of Medicine, University of Toronto, Toronto, ON, Canada.ORCID 0000-0002-9942-0130

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveTo report the improvements achieved with clinical decision support systems and examine the heterogeneity from pooling effects across diverse clinical settings and intervention targets.

designSystematic review and meta-analysis. DATA SOURCES: Medline up to August 2019. ELIGIBILITY CRITERIA FOR SELECTING STUDIES AND

methodsRandomised or quasi-randomised controlled trials reporting absolute improvements in the percentage of patients receiving care recommended by clinical decision support systems. Multilevel meta-analysis accounted for within study clustering. Meta-regression was used to assess the degree to which the features of clinical decision support systems and study characteristics reduced heterogeneity in effect sizes. Where reported, clinical endpoints were also captured.

resultsIn 108 studies (94 randomised, 14 quasi-randomised), reporting 122 trials that provided analysable data from 1 203 053 patients and 10 790 providers, clinical decision support systems increased the proportion of patients receiving desired care by 5.8% (95% confidence interval 4.0% to 7.6%). This pooled effect exhibited substantial heterogeneity (I

conclusionsMost interventions with clinical decision support systems appear to achieve small to moderate improvements in targeted processes of care, a finding confirmed by the small changes in clinical endpoints found in studies that reported them. A minority of studies achieved substantial increases in the delivery of recommended care, but predictors of these more meaningful improvements remain undefined.

Indexed as

Decision Support Systems, ClinicalQuality ImprovementHumans

Identifiers

PMID32943437
PMCPMC7495041

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
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.