ReviewJMIR medical informatics2018
Reasons For Physicians Not Adopting Clinical Decision Support Systems: Critical Analysis.
Review in JMIR medical informatics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 172 papers, 6 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
172 citing papers in PubMed, 6 syntheses or guidelines pooled it.
- From interface to outcome: a 4I framework for AI-linked functionality in electronic health records.BMC medical informatics and decision making · 2026Pooled it
- Barriers and facilitators to the implementation and adoption of computerised clinical decision support systems: an overview of reviews.Systematic reviews · 2026Pooled it
- Evaluating the role of clinical decision support systems in medication safety for older people: a systematic review.Age and ageing · 2025Pooled it
- Optimizing Clinical Decision Support System Functionality by Leveraging Specific Human-Computer Interaction Elements: Insights From a Systematic Review.JMIR human factors · 2025Pooled it
- Understanding "Alert Fatigue" in Primary Care: Qualitative Systematic Review of General Practitioners Attitudes and Experiences of Clinical Alerts, Prompts, and Reminders.Journal of medical Internet research · 2025Pooled it
- Inclusion of Clinicians in the Development and Evaluation of Clinical Artificial Intelligence Tools: A Systematic Literature Review.Frontiers in psychology · 2022Pooled it
- Evaluation of the Impact of a Mobile App (LoAD Calc) on the Calculation of Maximum Safe Doses of Local Anesthetics: Randomized Controlled Trial.JMIR mHealth and uHealth · 2026Trial
- Patterns of CDSS adoption in primary care: a cluster analysis and predictive modelling study from a stepped wedge trial.Implementation science : IS · 2026Trial
- Effect of Clinical Decision Support on Diagnostic Imaging for Pediatric Appendicitis: A Cluster Randomized Trial.JAMA network open · 2021Trial
- Clinical Decision Support Intervention for Rib Fracture Treatment.Journal of the American College of Surgeons · 2020Trial
- Computerized clinical decision support system for diabetes in primary care does not improve quality of care: a cluster-randomized controlled trial.Implementation science : IS · 2020 · on this mapTrial
- Bridging the trust-adoption gap for AI scribes in rural communities: A machine learning approach using the 2024 Canadian digital health survey.International journal of medical informatics · 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
- When is AI "just another innovation"? A comparative conceptual analysis of artificial intelligence and evidence-based practice implementation.Implementation science communications · 2026Article
- Factors Influencing Physician Adherence to Venous Thromboembolism Risk Assessment Model Recommendations: A Best-Worst Scaling.American journal of cardiovascular drugs : drugs, devices, and other interventions · 2026Article
- Development and Evaluation of a Custom GPT-Based Artificial Intelligence Clinical Decision Support System for Emergency Department Interdepartmental Consultation Automation.Healthcare informatics research · 2026Article
- Development of a Machine Learning-Based Triage Score for Medication-Related Osteonecrosis of the Jaw in Osteoporosis Patients Undergoing Tooth Extraction.Diagnostics (Basel, Switzerland) · 2026Article
- From CXR to ultrasound: a multi-framework approach to advancing De-implementation in acute care.Implementation science communications · 2026Article
- Development and prospective evaluation of a real-time deep learning model for inpatient hypoglycemia prediction.NPJ digital medicine · 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
112 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
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundClinical decision support systems (CDSSs) are an integral component of today's health information technologies. They assist with interpretation, diagnosis, and treatment. A CDSS can be embedded throughout the patient safety continuum providing reminders, recommendations, and alerts to health care providers. Although CDSSs have been shown to reduce medical errors and improve patient outcomes, they have fallen short of their full potential. User acceptance has been identified as one of the potential reasons for this shortfall.
objectiveThe purpose of this paper was to conduct a critical review and task analysis of CDSS research and to develop a new framework for CDSS design in order to achieve user acceptance.
methodsA critical review of CDSS papers was conducted with a focus on user acceptance. To gain a greater understanding of the problems associated with CDSS acceptance, we conducted a task analysis to identify and describe the goals, user input, system output, knowledge requirements, and constraints from two different perspectives: the machine (ie, the CDSS engine) and the user (ie, the physician).
resultsFavorability of CDSSs was based on user acceptance of clinical guidelines, reminders, alerts, and diagnostic suggestions. We propose two models: (1) the user acceptance and system adaptation design model, which includes optimizing CDSS design based on user needs/expectations, and (2) the input-process-output-engagemodel, which reveals to users the processes that govern CDSS outputs.
conclusionsThis research demonstrates that the incorporation of the proposed models will improve user acceptance to support the beneficial effects of CDSSs adoption. Ultimately, if a user does not accept technology, this not only poses a threat to the use of the technology but can also pose a threat to the health and well-being of patients.
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.