ArticlePloS one2018
Acceptance and barriers pertaining to a general practice decision support system for multiple clinical conditions: A mixed methods evaluation.
Article in PloS one, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 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
21 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Exploring the role of professional identity in the implementation of clinical decision support systems-a narrative review.Implementation science : IS · 2024Pooled it
- Barriers and Facilitators to the Use of Clinical Decision Support Systems in Primary Care: A Mixed-Methods Systematic Review.Annals of family medicinePooled it
- Influence of a clinical decision support system on the anticoagulation treatment knowledge, attitude and practice of general practitioners and their using experience: a mixed method study.BMC primary care · 2026Trial
- Physicians' preferences for the use of clinical decision support systems in the context of acutely ill children presenting to ambulatory care: a focus group study.BMC medical informatics and decision making · 2026Article
- Usability and Usefulness of Machine Learning-Based Clinical Decision Support Software in Primary Care: Survey of Users in a Prospective Observational Study.JMIR medical informatics · 2026Observational
- Diagnostic performance of Prof. Valmed, ChatGPT-5 Thinking, and OpenEvidence in rheumatology: A comparative evaluation.Rheumatology international · 2026Article
- Adherence to oral thromboprophylaxis in atrial fibrillation: an overview for clinicians.Europace : European pacing, arrhythmias, and cardiac electrophysiology : journal of the working groups on cardiac pacing, arrhythmias, and cardiac cellular electrophysiology of the European Society of Cardiology · 2025Review
- A Mixed-Method Case Study to Evaluate Adoption of Clinical Decision Support for Cancer Symptom Management.Applied clinical informatics · 2025Article
- Optimizing Decision Support Alerts to Reduce Telemetry Duration: A Multicenter Evaluation.Applied clinical informatics · 2024Article
- Challenges and Facilitation Approaches for the Participatory Design of AI-Based Clinical Decision Support Systems: Protocol for a Scoping Review.JMIR research protocols · 2024Article
- Clinical decision support systems to optimize adherence to anticoagulant guidelines in patients with atrial fibrillation: a systematic review and meta-analysis of randomized controlled trials.Thrombosis journal · 2024Review
- Acceptance and use of a clinical decision support system in musculoskeletal pain disorders - the SupportPrim project.BMC medical informatics and decision making · 2023Article
- Facilitators and Barriers to Interacting With Clinical Decision Support in the ICU: A Mixed-Methods Approach.Critical care explorations · 2023Article
- Workload and workflow implications associated with the use of electronic clinical decision support tools used by health professionals in general practice: a scoping review.BMC primary care · 2023Article
- Electronic nudge tool technology used in the critical care and peri-anaesthetic setting: a scoping review protocol.BMJ open · 2022Article
- Acceptance, Barriers, and Facilitators to Implementing Artificial Intelligence-Based Decision Support Systems in Emergency Departments: Quantitative and Qualitative Evaluation.JMIR formative research · 2022Article
- Evaluation Framework for Successful Artificial Intelligence-Enabled Clinical Decision Support Systems: Mixed Methods Study.Journal of medical Internet research · 2021Article
- Electronic Health Record Reminders for Chlamydia Screening in an American Indian Population.Public health reports (Washington, D.C. : 1974) · 2021Article
- Clinician acceptance of complex clinical decision support systems for treatment allocation of patients with chronic low back pain.BMC medical informatics and decision making · 2021Article
- Clinical Decision Support and Implications for the Clinician Burnout Crisis.Yearbook of medical informatics · 2020Review
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
backgroundMany studies have investigated the use of clinical decision support systems as a means to improve care, but have thus far failed to show significant effects on patient-related outcomes. We developed a clinical decision support system that attempted to address issues that were identified in these studies. The system was implemented in Dutch general practice and was designed to be both unobtrusive and to respond in real time. Despite our efforts, usage of the system was low. In the current study we perform a mixed methods evaluation to identify remediable barriers which led to disappointing usage rates for our system.
methodsA mixed methods evaluation employing an online questionnaire and focus group. The focus group was organized to clarify free text comments and receive more detailed feedback from general practitioners. Topics consisted of items based on results from the survey and additional open questions.
resultsThe response rate for the questionnaire was 94%. Results from the questionnaire and focus group can be summarized as follows: The system was perceived as interruptive, despite its design. Participants felt that there were too many recommendations and that the relevance of the recommendations varied. Demographic based recommendations (e.g. age) were often irrelevant, while specific risk-based recommendations (e.g. diagnosis) were more relevant. The other main barrier to use was lack of time during the patient visit.
conclusionThese results are likely to be useful to other researchers who are attempting to address the problems of interruption and alert fatigue in decision support.
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.