SynthesisJournal of the American Medical Informatics Association : JAMIA2021
A theory-based meta-regression of factors influencing clinical decision support adoption and implementation.
Synthesis in Journal of the American Medical Informatics Association : JAMIA, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 2 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
16 citing papers in PubMed, 2 syntheses or guidelines pooled it, 13 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
- Conceptualizing clinical decision support as complex interventions: a meta-analysis of comparative effectiveness trials.Journal of the American Medical Informatics Association : JAMIA · 2022Pooled it
- Artificial intelligence predictive analytics in heart failure: results of the pilot phase of a pragmatic randomized clinical trial.Journal of the American Medical Informatics Association : JAMIA · 2024Trial
- Avoiding "shiny objects" and heeding "red flags": a sensemaking framework for emerging technology procurement.JAMIA open · 2026Article
- Factors associated with chronic pain clinical decision support use in primary care.PLOS digital health · 2026Article
- User-Directed vs Interruptive Real-Time Benefit Tools for Medication Changes in Primary Care.JAMA network open · 2026Article
- Learning From the Adoption of a Readmission Clinical Decision Support Tool: Group Model Building Approach.JMIR human factors · 2026Article
- Integrating rule-based NLP and large language models for statin information extraction from clinical notes.International journal of medical informatics · 2026Article
- A standard-based taxonomy of features that affect user response to clinical decision support alerts.BMC medical informatics and decision making · 2025Article
- Optimizing Order Sets With a Large Language Model-Powered Multiagent System.JAMA network open · 2025Article
- A scoping review, novel taxonomy and catalogue of implementation frameworks for clinical decision support systems.BMC medical informatics and decision making · 2024Article
- Barriers and Facilitators to Using a Clinical Decision Support Tool for Opioid Use Disorder in Primary Care.Journal of the American Board of Family Medicine : JABFM · 2024Article
- Leveraging explainable artificial intelligence to optimize clinical decision support.Journal of the American Medical Informatics Association : JAMIA · 2024Article
- Examining primary care provider experiences with using a clinical decision support tool for pain management.JAMIA open · 2023Article
- Shared Decision-Making for Drug-Drug Interactions: Formative Evaluation of an Anticoagulant Drug Interaction.JMIR formative research · 2022Article
- The potential for leveraging machine learning to filter medication alerts.Journal of the American Medical Informatics Association : JAMIA · 2022Article
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 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveThe purpose of the study was to explore the theoretical underpinnings of effective clinical decision support (CDS) factors using the comparative effectiveness results. MATERIALS AND
methodsWe leveraged search results from a previous systematic literature review and updated the search to screen articles published from January 2017 to January 2020. We included randomized controlled trials and cluster randomized controlled trials that compared a CDS intervention with and without specific factors. We used random effects meta-regression procedures to analyze clinician behavior for the aggregate effects. The theoretical model was the Unified Theory of Acceptance and Use of Technology (UTAUT) model with motivational control.
resultsThirty-four studies were included. The meta-regression models identified the importance of effort expectancy (estimated coefficient = -0.162; P = .0003); facilitating conditions (estimated coefficient = 0.094; P = .013); and performance expectancy with motivational control (estimated coefficient = 1.029; P = .022). Each of these factors created a significant impact on clinician behavior. The meta-regression model with the multivariate analysis explained a large amount of the heterogeneity across studies (R2 = 88.32%). DISCUSSION: Three positive factors were identified: low effort to use, low controllability, and providing more infrastructure and implementation strategies to support the CDS. The multivariate analysis suggests that passive CDS could be effective if users believe the CDS is useful and/or social expectations to use the CDS intervention exist.
conclusionsOverall, a modified UTAUT model that includes motivational control is an appropriate model to understand psychological factors associated with CDS effectiveness and to guide CDS design, implementation, and optimization.
Indexed as
Identifiers
What OpenQuestion holds
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.