SynthesisImplementation science : IS2022
Do providers use computerized clinical decision support systems? A systematic review and meta-regression of clinical decision support uptake.
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.
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.
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.
Who cites it
76 citing papers in PubMed, 5 syntheses or guidelines pooled it, 121 citations in OpenAlex.
- Barriers and facilitators to the implementation and adoption of computerised clinical decision support systems: an overview of reviews.Systematic reviews · 2026Pooled it
- Low-value interventions to deimplement: A secondary analysis of a systematic review of low back pain clinical practice guidelines.PM & R : the journal of injury, function, and rehabilitation · 2025Pooled it
- 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 · 2025Pooled it
- How intervention studies measure the effectiveness of medication safety-related clinical decision support systems in primary and long-term care: a systematic review.BMC medical informatics and decision making · 2024Pooled it
- Design, effectiveness, and economic outcomes of contemporary chronic disease clinical decision support systems: a systematic review and meta-analysis.Journal of the American Medical Informatics Association : JAMIA · 2022Pooled it
- Improving Musculoskeletal Care Quality With a Web-Based Clinical Decision Support System in UK Primary Care: Mixed Methods Cluster-Randomized Controlled Trial.Journal of medical Internet research · 2026Trial
- Patterns of CDSS adoption in primary care: a cluster analysis and predictive modelling study from a stepped wedge trial.Implementation science : IS · 2026Trial
- Understanding Clinicians' Usage Patterns of the CONCERN Early Warning System: Insights from a Multi-Site Pragmatic Cluster Randomized Controlled Trial.Studies in health technology and informatics · 2025Trial
- Does Clinical Decision Support Increase Appropriate Medication Prescribing for Cardiovascular Risk Reduction?Journal of the American Board of Family Medicine : JABFM · 2023Trial
- Trial
- Optimising prescribing in older adults with multimorbidity and polypharmacy in primary care (OPTICA): cluster randomised clinical trial.BMJ (Clinical research ed.) · 2023Trial
- De-Implementing Opioids for Dental Extractions (DIODE): a multi-clinic, cluster-randomized trial of clinical decision support strategies in dentistry.Implementation science : IS · 2023Trial
- Evaluation of an Antimicrobial Stewardship Decision Support for Pediatric Infections.Applied clinical informatics · 2023Trial
- Impact of a clinical decision support system on paediatric drug dose prescribing: a randomised within-subject simulation trial.BMJ paediatrics open · 2023Trial
- Association between radiological CDSS availability and quality of imaging referrals graded using RI-RADS.iScience · 2026Article
- Usability and Quality of UCISAB, a Clinical Decision Support System for Optimization of Analgesia and Sedation Infusions in the ICU: A Multicenter, Cross-Sectional, Mixed-Methods Usability Evaluation in Colombia.Journal of clinical medicine · 2026Article
- Evaluation of a Decision Mining Tool for Nursing Quality Improvement by Hospital Nurses: Theory-Guided Qualitative Study.JMIR formative research · 2026Article
- Implementation of Electronic Clinical Decision Support for Radiology Referrals: The Role of Governance, Clinician Engagement and Education.Emergency medicine Australasia : EMA · 2026Article
- Designing a computerized decision support system for asthma chronic disease management in community pharmacies.International journal of clinical pharmacy · 2026Article
- Clinical decision support system for child and adolescent mental health services: a formative usability study.BMC medical informatics and decision making · 2026Article
16 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 3 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
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.
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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.