Evidence map›Paper›PMID 34937723›Full record

SynthesisBMJ open2021

Effect of computerised, knowledge-based, clinical decision support systems on patient-reported and clinical outcomes of patients with chronic disease managed in primary care settings: a systematic review.

Marie Line El Asmar, Kanika I Dharmayat, Antonio J Vallejo-Vaz, Ryan Irwin, Nikolaos Mastellos

Abstract readSystematic Review
In one paragraph

Synthesis in BMJ open, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
16citing papers in PubMed, 1 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

16 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Testing an HPV Vaccine Decision Aid for 27- to 45-Year-Old Adults in the United States: A Randomized Trial.Medical decision making : an international journal of the Society for Medical Decision Making · 2025
    Trial
  3. Review
  4. Article
  5. Article
  6. Review
  7. Review
  8. Review
  9. [Supporting medical and nursing activities with AI: recommendations for responsible design and use].Bundesgesundheitsblatt, Gesundheitsforschung, Gesundheitsschutz · 2024
    Article
  10. Article
  11. Article
  12. Article
  13. Article
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  16. 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

5 authors.

Marie Line El AsmarSelf Care Academic Research Unit, Imperial College London, London, UK me719@ic.ac.uk.ORCID 0000-0002-8481-6431
Kanika I DharmayatDepartment of Primary Care and Public Health, Imperial Centre for Cardiovascular Disease Prevention, Imperial College London, London, UK.
Antonio J Vallejo-VazImperial Centre for Cardiovascular Disease Prevention (ICCP), Department of Primary Care and Public Health, School of Public Health, Imperial College London. London, United Kingdom, London, UK.
Ryan Irwin *Department of Primary Care Clinical Sciences, Institute of Applied Health Research, University of Birmingham, Birmingham, UK.
Nikolaos Mastellos *Department of Primary Care and Public Health, School of Public Health, Imperial College London, London, UK.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectivesChronic diseases are the leading cause of disability globally. Most chronic disease management occurs in primary care with outcomes varying across primary care providers. Computerised clinical decision support systems (CDSS) have been shown to positively affect clinician behaviour by improving adherence to clinical guidelines. This study provides a summary of the available evidence on the effect of CDSS embedded in electronic health records on patient-reported and clinical outcomes of adult patients with chronic disease managed in primary care. DESIGN AND ELIGIBILITY CRITERIA: Systematic review, including randomised controlled trials (RCTs), cluster RCTs, quasi-RCTs, interrupted time series and controlled before-and-after studies, assessing the effect of CDSS (vs usual care) on patient-reported or clinical outcomes of adult patients with selected common chronic diseases (asthma, chronic obstructive pulmonary disease, heart failure, myocardial ischaemia, hypertension, diabetes mellitus, hyperlipidaemia, arthritis and osteoporosis) managed in primary care. DATA SOURCES: Medline, Embase, CENTRAL, Scopus, Health Management Information Consortium and trial register clinicaltrials.gov were searched from inception to 24 June 2020. DATA EXTRACTION AND SYNTHESIS: Screening, data extraction and quality assessment were performed by two reviewers independently. The Cochrane risk of bias tool was used for quality appraisal.

resultsFrom 5430 articles, 8 studies met the inclusion criteria. Studies were heterogeneous in population characteristics, intervention components and outcome measurements and focused on diabetes, asthma, hyperlipidaemia and hypertension. Most outcomes were clinical with one study reporting on patient-reported outcomes. Quality of the evidence was impacted by methodological biases of studies.

conclusionsThere is inconclusive evidence in support of CDSS. A firm inference on the intervention effect was not possible due to methodological biases and study heterogeneity. Further research is needed to provide evidence on the intervention effect and the interplay between healthcare setting features, CDSS characteristics and implementation processes. PROSPERO REGISTRATION NUMBER: CRD42020218184.

Indexed as

Decision Support Systems, ClinicalDiabetes MellitusAdultChronic DiseaseHumansPatient Reported Outcome MeasuresPrimary Health Caregeneral medicine (see internal medicine)health informaticspreventive medicineprimary care

Identifiers

PMID34937723
PMCPMC8705223

What OpenQuestion holds

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LicenceCC BY-NC
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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.