SynthesisBMC medical informatics and decision making2021
A systematic review of theoretical constructs in CDS literature.
Synthesis in BMC medical informatics and decision making, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 4 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
18 citing papers in PubMed, 4 syntheses or guidelines pooled it.
- Task-Technology Fit of Artificial Intelligence-based clinical decision support systems: a review of qualitative studies.BMC medical informatics and decision making · 2025Pooled 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
- 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
- A theory-based meta-regression of factors influencing clinical decision support adoption and implementation.Journal of the American Medical Informatics Association : JAMIA · 2021Pooled it
- Exposure to clinical decision support and training increases primary care clinician confidence in managing cognitive impairment care but not confidence to diagnose.BMC primary care · 2025Trial
- Exploring primary care providers' perspectives on clinical decision support for venous thromboembolism diagnosis: a qualitative study.BMJ open · 2026Article
- Qualitative study on physicians' acceptance of a clinical decision support system for anemia management in patients receiving hemodialysis.BMC health services research · 2025Article
- Care at home for remdesivir treatment of COVID-19: a survey study of patient and physician experiences.BMC infectious diseases · 2025Article
- A standard-based taxonomy of features that affect user response to clinical decision support alerts.BMC medical informatics and decision making · 2025Article
- User-Centered Framework for Implementation of Technology (UFIT): Development of an Integrated Framework for Designing Clinical Decision Support Tools Packaged With Tailored Implementation Strategies.Journal of medical Internet research · 2024Article
- Leveraging explainable artificial intelligence to optimize clinical decision support.Journal of the American Medical Informatics Association : JAMIA · 2024Article
- Introduction of an Electronic Clinical Decision Support Tool to Inform Prescribing for Pediatric Diarrhea in Bangladesh and Mali: Do Provider Expectations Predict Experiences?The American journal of tropical medicine and hygiene · 2022Article
- The potential for leveraging machine learning to filter medication alerts.Journal of the American Medical Informatics Association : JAMIA · 2022Article
- Clinician Acceptance of Order Sets for Pain Management: A Survey in Two Urban Hospitals.Applied clinical informatics · 2022Article
- Public attitudes toward COVID-19 vaccines on English-language Twitter: A sentiment analysis.Vaccine · 2021Article
- Leveraging Transfer Learning to Analyze Opinions, Attitudes, and Behavioral Intentions Toward COVID-19 Vaccines: Social Media Content and Temporal Analysis.Journal of medical Internet research · 2021Article
- Determinants of Catalan public primary care professionals' intention to use digital clinical consultations (eConsulta) in the post-COVID-19 context: optical illusion or permanent transformation?Journal of medical Internet research · 2021Article
- Willingness to Adopt Health Information Among Social Question-and-Answer Community Users in China: Cross-sectional Survey Study.Journal of medical Internet research · 2021Article
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.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundStudies that examine the adoption of clinical decision support (CDS) by healthcare providers have generally lacked a theoretical underpinning. The Unified Theory of Acceptance and Use of Technology (UTAUT) model may provide such a theory-based explanation; however, it is unknown if the model can be applied to the CDS literature.
objectiveOur overall goal was to develop a taxonomy based on UTAUT constructs that could reliably characterize CDS interventions.
methodsWe used a two-step process: (1) identified randomized controlled trials meeting comparative effectiveness criteria, e.g., evaluating the impact of CDS interventions with and without specific features or implementation strategies; (2) iteratively developed and validated a taxonomy for characterizing differential CDS features or implementation strategies using three raters.
resultsTwenty-five studies with 48 comparison arms were identified. We applied three constructs from the UTAUT model and added motivational control to characterize CDS interventions. Inter-rater reliability was as follows for model constructs: performance expectancy (κ = 0.79), effort expectancy (κ = 0.85), social influence (κ = 0.71), and motivational control (κ = 0.87).
conclusionWe found that constructs from the UTAUT model and motivational control can reliably characterize features and associated implementation strategies. Our next step is to examine the quantitative relationships between constructs and CDS adoption.
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.