ArticleJAMIA open2023
Design and development of a digital shared decision-making tool for stroke prevention in atrial fibrillation.
Article in JAMIA open, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT04096781 (Engaging Patients to Help Achieve Increased Patient Choice and Engagement for AFib Stroke Prevention), which is not on this map. Cited by 3 papers.
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.
Engaging Patients to Help Achieve Increased Patient Choice and Engagement for AFib Stroke Prevention
Who cites it
3 citing papers in PubMed, 8 citations in OpenAlex.
- Article
- Patient-Selection of a Clinical Trial Primary Outcome: The ENHANCE-AF Outcomes Survey.PloS one · 2025Article
- Development of web- and mobile-based shared decision-making tools in the neurological intensive care unit.Neurotherapeutics : the journal of the American Society for Experimental NeuroTherapeutics · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
11 authors at 6 institutions in 1 country.
Funding
Abstract
Background: Shared decision-making (SDM) is an approach in which patients and clinicians act as partners in making medical decisions. Patients receive the information needed to decide and are encouraged to balance risks, benefits, and preferences. Informative materials are vital to SDM. Atrial fibrillation (AF) is the most common cardiac arrhythmia and responsible for 10% of ischemic strokes, however 1/3 of patients are not on appropriate anticoagulation. Decision sharing may facilitate treatment acceptance, improving outcomes. Aims: To develop a framework of the components needed to create novel SDM tools and to provide practical examples through a case-study of stroke prevention in AF. Methods: We analyze the design values of a web-based SDM tool created to better inform AF patients about anticoagulation. The tool was developed in partnership with patient advocates, multi-disciplinary investigators, and private design firms. It was refined through iterative, recursive testing in patients with AF. Its effectiveness is being evaluated in a multisite clinical trial led by Stanford University and sponsored by the American Heart Association. Findings: The main components considered when creating the Stanford AFib tool included: design and software; content identification; information delivery; inclusive communication, user engagement; patient feedback; clinician experience; and anticipation of implementation and dissemination. We also highlight the ethical principles underlying SDM; matters of diversity and inclusion, linguistic variety, accessibility, and health literacy. The Stanford AFib Guide patient tool is available at: https://afibguide.com and the clinician tool at https://afibguide.com/clinician. Conclusion: Attention to a range of vital development and design factors can facilitate tool adoption and information acquisition by diverse cultural, educational, and socioeconomic subpopulations. With thoughtful design, digital tools may decrease decision regret and improve treatment outcomes across many decision-making situations in healthcare.
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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.