ArticlePEC innovation2023
Facilitating family communication of familial hypercholesterolemia genetic risk: Assessing engagement with innovative chatbot technology from the IMPACT-FH study.
Article in PEC innovation, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 1 of them a synthesis that pooled it.
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
10 citing papers in PubMed, 1 synthesis or guideline pooled it.
- A systematic review on the generative AI applications in human medical genetics.Frontiers in genetics · 2025Pooled it
- Genetic Cascade Screening for Familial Hypercholesterolemia: A Randomized Clinical Trial.JAMA network open · 2026Trial
- Social vulnerability and genetic service utilization among unaffected BRIDGE trial patients with inherited cancer susceptibility.BMC cancer · 2025Trial
- Uptake of Cancer Genetic Services for Chatbot vs Standard-of-Care Delivery Models: The BRIDGE Randomized Clinical Trial.JAMA network open · 2024Trial
- Patients' and family members' experiences with cascade testing for Lynch syndrome in the USA: a qualitative interview study.Journal of community genetics · 2026Article
- Cascade Chatbot: A Scalable Approach to Family-Based Genetic Testing for Hereditary Cancer Syndromes.JCO clinical cancer informatics · 2026Article
- Implementing a chatbot to promote hereditary breast & ovarian cancer genetic screening in women's health: identifying barriers and facilitators to screening adoption.BMC public health · 2025Article
- What are patient perspectives on privacy and trust in digital genomic tools? A qualitative study.Journal of genetic counseling · 2025Article
- Roles, Users, Benefits, and Limitations of Chatbots in Health Care: Rapid Review.Journal of medical Internet research · 2024Review
- Communication in genomic and precision medicine: Editorial -PEC innovation · 2023Article
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.
Funding
Abstract
Objective: To assess use of two web-based conversational agents, the Family Sharing Chatbot (FSC) and One Month Chatbot (OMC), by individuals with familial hypercholesterolemia (FH). Methods: FSC and OMC were sent using an opt-out methodology to a cohort of individuals receiving a FH genetic result. Data from 7/1/2021 through 5/12/2022 was obtained from the electronic health record and the chatbots' HIPAA-secure web portal. Results: Of 175 subjects, 21 (12%) opted out of the chatbots. Older individuals were more likely to opt out. Most (91/154, 59%) preferred receiving chatbots via the patient EHR portal. Seventy-five individuals (49%) clicked the FSC link, 62 (40%) interacted, and 36 (23%) shared a chatbot about their FH result with at least one relative. Ninety-two of the subjects received OMC, 22 (23%) clicked the link and 20 (21%) interacted. Individuals who shared were majority female and younger on average than the overall cohort. Reminders tended to increase engagement. Conclusion: Results demonstrate characteristics relevant to chatbot engagement. Individuals may be more inclined to receive chatbots if integrated within the patient EHR portal. Frequent reminders can potentially improve chatbot utilization. Innovation: FSC and OMC employ innovative digital health technology that can facilitate family communication about hereditary conditions.
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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.