ArticleImplementation science : IS2023
Identifying barriers and facilitators to successful implementation of computerized clinical decision support systems in hospitals: a NASSS framework-informed scoping review.
Article in Implementation science : IS, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 81 papers, 8 of them syntheses that pooled it.
What it found
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Who cites it
81 citing papers in PubMed, 8 syntheses or guidelines pooled it.
- Implementation determinants of rapid diagnostic tests for fever management in sub-Saharan Africa: a mixed-studies systematic review.Nature communications · 2026Pooled it
- Barriers and facilitators to the implementation and adoption of computerised clinical decision support systems: an overview of reviews.Systematic reviews · 2026Pooled it
- A NASSS framework-guided systematic review and exploratory modelling of digital health interventions for polypharmacy management in older adults.BMC geriatrics · 2025Pooled it
- Improving Explainability and Integrability of Medical AI to Promote Health Care Professional Acceptance and Use: Mixed Systematic Review.Journal of medical Internet research · 2025Pooled it
- Factors Influencing the Implementation and Adoption of Digital Nursing Technologies: Systematic Umbrella Review.Journal of medical Internet research · 2025Pooled it
- Facilitators and Barriers to Implementing AI in Routine Medical Imaging: Systematic Review and Qualitative Analysis.Journal of medical Internet research · 2025Pooled it
- Characterizing Behaviors That Influence the Implementation of Digital-Based Interventions in Health Care: Systematic Review.Journal of medical Internet research · 2025Pooled it
- Exploring the role of professional identity in the implementation of clinical decision support systems-a narrative review.Implementation science : IS · 2024Pooled it
- Patterns of CDSS adoption in primary care: a cluster analysis and predictive modelling study from a stepped wedge trial.Implementation science : IS · 2026Trial
- Clinical Decision Support System, Antihypertensive Treatment Intensification, and Blood Pressure Control: A Post Hoc Secondary Analysis of a Cluster Randomized Trial.JAMA network open · 2026Trial
- Large language models enhance diagnostic reasoning of medical students in rheumatology: a randomized controlled trial.BMC medical education · 2026Trial
- Decision Support Intervention and Anticoagulation for Emergency Department Atrial Fibrillation: The O'CAFÉ Stepped-Wedge Cluster Randomized Clinical Trial.JAMA network open · 2024Trial
- Factors facilitating the implementation of a clinical decision support system in primary care practices: a fuzzy set qualitative comparative analysis.BMC health services research · 2023Trial
- Integrating Artificial Intelligence in Nursing Practice With Decubitus Risk Prediction Alerts: A Pilot Process Evaluation.Journal of clinical nursing · 2026Article
- The GUIDE-CDSS framework: a comprehensive framework to GUIDE and evaluate the implementation of Clinical Decision Support Systems in healthcare, based on an overview of reviews.Journal of the American Medical Informatics Association : JAMIA · 2026Review
- Comment on: Integrating Artificial Intelligence in Nursing Practice With Decubitus Risk Prediction Alerts: A Pilot Process Evaluation.Journal of clinical nursing · 2026Article
- Why integration, not innovation, is the real-world challenge facing digital health.BMJ health & care informatics · 2026Article
- The Utility of Artificial Intelligence in Dermatology Training and Practice: A National, Cross-Sectional Study.The Australasian journal of dermatology · 2026Article
- Large language model driven multicenter prediction and explainable risk attribution of acute kidney injury.Nature communications · 2026Article
- Daily Monitoring of Behavioral and Psychological Symptoms of Dementia in Residential Care: Mixed Methods Pilot Study.JMIR aging · 2026Article
21 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundSuccessful implementation and utilization of Computerized Clinical Decision Support Systems (CDSS) in hospitals is complex and challenging. Implementation science, and in particular the Nonadoption, Abandonment, Scale-up, Spread and Sustainability (NASSS) framework, may offer a systematic approach for identifying and addressing these challenges. This review aimed to identify, categorize, and describe barriers and facilitators to CDSS implementation in hospital settings and map them to the NASSS framework. Exploring the applicability of the NASSS framework to CDSS implementation was a secondary aim.
methodsElectronic database searches were conducted (21 July 2020; updated 5 April 2022) in Ovid MEDLINE, Embase, Scopus, PyscInfo, and CINAHL. Original research studies reporting on measured or perceived barriers and/or facilitators to implementation and adoption of CDSS in hospital settings, or attitudes of healthcare professionals towards CDSS were included. Articles with a primary focus on CDSS development were excluded. No language or date restrictions were applied. We used qualitative content analysis to identify determinants and organize them into higher-order themes, which were then reflexively mapped to the NASSS framework.
resultsForty-four publications were included. These comprised a range of study designs, geographic locations, participants, technology types, CDSS functions, and clinical contexts of implementation. A total of 227 individual barriers and 130 individual facilitators were identified across the included studies. The most commonly reported influences on implementation were fit of CDSS with workflows (19 studies), the usefulness of the CDSS output in practice (17 studies), CDSS technical dependencies and design (16 studies), trust of users in the CDSS input data and evidence base (15 studies), and the contextual fit of the CDSS with the user's role or clinical setting (14 studies). Most determinants could be appropriately categorized into domains of the NASSS framework with barriers and facilitators in the "Technology," "Organization," and "Adopters" domains most frequently reported. No determinants were assigned to the "Embedding and Adaptation Over Time" domain.
conclusionsThis review identified the most common determinants which could be targeted for modification to either remove barriers or facilitate the adoption and use of CDSS within hospitals. Greater adoption of implementation theory should be encouraged to support CDSS implementation.
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