Trial reportComputers, informatics, nursing : CIN2015
Use of Simulation to Study Nurses' Acceptance and Nonacceptance of Clinical Decision Support Suggestions.
Trial report in Computers, informatics, nursing : CIN, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 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
12 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Towards Usable E-Health. A Systematic Review of Usability Questionnaires.Applied clinical informatics · 2017Pooled it
- 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
- Capabilities of computerized decision support systems supporting the nursing process in hospital settings: a scoping review.BMC nursing · 2025Article
- Psychological Factors Influencing Appropriate Reliance on AI-enabled Clinical Decision Support Systems: Experimental Web-Based Study Among Dermatologists.Journal of medical Internet research · 2025Article
- Human Factors and Technological Characteristics Influencing the Interaction of Medical Professionals With Artificial Intelligence-Enabled Clinical Decision Support Systems: Literature Review.JMIR human factors · 2022Review
- Artificial Intelligence: A Shifting Paradigm in Cardio-Cerebrovascular Medicine.Journal of clinical medicine · 2021Review
- Using artificial intelligence for improving stroke diagnosis in emergency departments: a practical framework.Therapeutic advances in neurological disorders · 2020Review
- Reasons For Physicians Not Adopting Clinical Decision Support Systems: Critical Analysis.JMIR medical informatics · 2018Review
- A Shovel-Ready Solution to Fill the Nursing Data Gap in the Interdisciplinary Clinical Picture.International journal of nursing knowledge · 2018Article
- Participatory design of probability-based decision support tools for in-hospital nurses.Journal of the American Medical Informatics Association : JAMIA · 2017Article
- Framework for Mining and Analysis of Standardized Nursing Care Plan Data.Western journal of nursing research · 2017Article
- Toward Meaningful Care Plan Clinical Decision Support: Feasibility and Effects of a Simulated Pilot Study.Nursing researchArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
Abstract
Our long-term goal was to ensure nurse clinical decision support works as intended before full deployment in clinical practice. As part of a broader effort, this pilot project explored factors influencing acceptance/nonacceptance of eight clinical decision support suggestions displayed in an electronic health record-based nursing plan of care software prototype. A diverse sample of 21 nurses participated in this high-fidelity clinical simulation experience and completed a questionnaire to assess reasons for accepting/not accepting the clinical decision support suggestions. Of 168 total suggestions displayed during the experiment (eight for each of the 21 nurses), 123 (73.2%) were accepted, and 45 (26.8%) were not accepted. The mode number of acceptances by nurses was seven of eight, with only two of 21 nurses accepting all. The main reason for clinical decision support acceptance was the nurse's belief that the suggestions were good for the patient (100%), with other features providing secondary reinforcement. Reasons for nonacceptance were less clear, with fewer than half of the subjects indicating low confidence in the evidence. This study provides preliminary evidence that high-quality simulation and targeted questionnaires about specific clinical decision support selections offer a cost-effective means for testing before full deployment in clinical practice.
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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.