ArticleJAMIA open2021
Risky business: a scoping review for communicating results of predictive models between providers and patients.
Article in JAMIA open, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
What it found
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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
14 citing papers in PubMed.
- The Irreducible Encounter: Human Skills, Artificial Intelligence, and the Future of Ophthalmic Care.Ophthalmology and therapy · 2026Article
- A qualitative interview study investigating patient, health professional, and developer perspectives on real-world implementation of patient-centered AI systems.NPJ digital medicine · 2026Article
- Beyond the diagnostic threshold: paradigm shift in laboratory medicine from passive reaction to predictive capability.Frontiers in medicine · 2026Article
- A qualitative Interview Study Investigating Patient, Health Professional, and Developer Perspectives on Real-World Implementation of Patient-Centered AI Systems.Research square · 2025Article
- Article
- The Value of Clinical Prediction Models in General Practice: A Qualitative Study Exploring the Perspectives of People With Lived Experience of Depression and General Practitioners.Health expectations : an international journal of public participation in health care and health policy · 2024Article
- Patient Perspectives on AI for Mental Health Care: Cross-Sectional Survey Study.JMIR mental health · 2024Article
- Equity and AI governance at academic medical centers.The American journal of managed care · 2024Article
- Visualizing machine learning-based predictions of postpartum depression risk for lay audiences.Journal of the American Medical Informatics Association : JAMIA · 2024Article
- Differing perspectives on artificial intelligence in mental healthcare among patients: a cross-sectional survey study.Frontiers in digital health · 2024Article
- Women's perspectives on the use of artificial intelligence (AI)-based technologies in mental healthcare.JAMIA open · 2023Article
- Human-Centered Design to Address Biases in Artificial Intelligence.Journal of medical Internet research · 2023Article
- Recentering responsible and explainable artificial intelligence research on patients: implications in perinatal psychiatry.Frontiers in psychiatry · 2023Article
- Application of Predictive Analytics in Pregnancy, Birth, and Postpartum Nursing Care.MCN. The American journal of maternal child nursingArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveGiven widespread excitement around predictive analytics and the proliferation of machine learning algorithms that predict outcomes, a key next step is understanding how this information is-or should be-communicated with patients. MATERIALS AND
methodsWe conducted a scoping review informed by PRISMA-ScR guidelines to identify current knowledge and gaps in this domain.
resultsTen studies met inclusion criteria for full text review. The following topics were represented in the studies, some of which involved more than 1 topic: disease prevention ( DISCUSSION: As predictive models are increasingly published, marketed by industry, and implemented, this paucity of relevant research poses important gaps. Published studies identified the importance of (1) identifying the most effective source of information for patient communications; (2) contextualizing risk information and associated design elements based on users' needs and problem areas; and (3) understanding potential impacts on risk factor modification and behavior change dependent on risk presentation.
conclusionAn opportunity remains for researchers and practitioners to share strategies for effective selection of predictive algorithms for clinical practice, approaches for educating clinicians and patients in effectively using predictive data, and new approaches for framing patient-provider communication in the era of artificial intelligence.
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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.