ReviewJournal of medical Internet research2020
A Conceptual Framework to Study the Implementation of Clinical Decision Support Systems (BEAR): Literature Review and Concept Mapping.
Review in Journal of medical Internet research, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers, 1 of them a synthesis 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
31 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Barriers and Facilitators to Patient Acceptance of Artificial Intelligence in Health Care: Systematic Review.Journal of medical Internet research · 2026Pooled it
- Patterns of CDSS adoption in primary care: a cluster analysis and predictive modelling study from a stepped wedge trial.Implementation science : IS · 2026Trial
- Barriers and facilitators for intensive care nurses in using clinical decision support systems for intravenous Y-site compatibility in intensive care: A survey study.International journal of nursing studies advances · 2026Article
- Therapist perspectives on the clinical utility of hand performance information from at-home egocentric video in outpatient neurorehabilitation: a multi-methods evaluation study.BMC medical informatics and decision making · 2026Article
- Radiology artificial intelligence for prioritized imaging and diagnosis of lung cancer: qualitative interview analysis of stakeholder perspectives in Northern Ireland.Frontiers in medicine · 2026Article
- Evaluating the Effects of Clinician Prescribing and Implementation Materials on Adoption of Virtual Reality Therapeutics: Randomized Feasibility Pilot Study.JMIR XR and spatial computing · 2026Article
- Qualitative study on physicians' acceptance of a clinical decision support system for anemia management in patients receiving hemodialysis.BMC health services research · 2025Article
- Presenting a conceptual model for decision support systems in infertility: A developmental study.International journal of reproductive biomedicine · 2025Article
- A Survey on Digital Solutions for Health Services Management: Features and Use Cases from Brazilian National Literature.Healthcare (Basel, Switzerland) · 2025Review
- Factors Influencing Health Workers' Acceptance of Guideline-Based Clinical Decision Support Systems for Preventive Services in Thailand: Questionnaire-Based Study.JMIR human factors · 2025Article
- A systematic review of clinicians' acceptance and use of clinical decision support systems over time.NPJ digital medicine · 2025Article
- New Performance Measurement Framework for Realizing Patient-Centered Clinical Decision Support: Qualitative Development Study.Journal of medical Internet research · 2025Article
- A scoping review, novel taxonomy and catalogue of implementation frameworks for clinical decision support systems.BMC medical informatics and decision making · 2024Article
- Developing the teledentistry acceptance survey for dentists - TAS-D: a Delphi study.BMC oral health · 2024Article
- Successful Electronic Consultation Service Initiative in Quebec, Canada With Primary Care Physicians' and Specialists' Experiences on Acceptance and Use of Technological Innovation: Cross-Sectional Exploratory Study.JMIR formative research · 2024Article
- Clinical Decision Support Requirements for Ventricular Tachycardia Diagnosis Within the Frameworks of Knowledge and Practice: Survey Study.JMIR human factors · 2024Article
- Transforming Healthcare with Nanomedicine: A SWOT Analysis of Drug Delivery Innovation.Drug design, development and therapy · 2024Review
- Digital health and acute kidney injury: consensus report of the 27th Acute Disease Quality Initiative workgroup.Nature reviews. Nephrology · 2023Review
- Using Theories, Models, and Frameworks to Inform Implementation Cycles of Computerized Clinical Decision Support Systems in Tertiary Health Care Settings: Scoping Review.Journal of medical Internet research · 2023Article
- Improving computerized decision support system interventions: a qualitative study combining the theoretical domains framework with the GUIDES Checklist.BMC medical informatics and decision making · 2023Article
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
Abstract
backgroundThe implementation of clinical decision support systems (CDSSs) as an intervention to foster clinical practice change is affected by many factors. Key factors include those associated with behavioral change and those associated with technology acceptance. However, the literature regarding these subjects is fragmented and originates from two traditionally separate disciplines: implementation science and technology acceptance.
objectiveOur objective is to propose an integrated framework that bridges the gap between the behavioral change and technology acceptance aspects of the implementation of CDSSs.
methodsWe employed an iterative process to map constructs from four contributing frameworks-the Theoretical Domains Framework (TDF); the Consolidated Framework for Implementation Research (CFIR); the Human, Organization, and Technology-fit framework (HOT-fit); and the Unified Theory of Acceptance and Use of Technology (UTAUT)-and the findings of 10 literature reviews, identified through a systematic review of reviews approach.
resultsThe resulting framework comprises 22 domains: agreement with the decision algorithm; attitudes; behavioral regulation; beliefs about capabilities; beliefs about consequences; contingencies; demographic characteristics; effort expectancy; emotions; environmental context and resources; goals; intentions; intervention characteristics; knowledge; memory, attention, and decision processes; patient-health professional relationship; patient's preferences; performance expectancy; role and identity; skills, ability, and competence; social influences; and system quality. We demonstrate the use of the framework providing examples from two research projects.
conclusionsWe proposed BEAR (BEhavior and Acceptance fRamework), an integrated framework that bridges the gap between behavioral change and technology acceptance, thereby widening the view established by current models.
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.