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Facilitating family communication of familial hypercholesterolemia genetic risk: Assessing engagement with innovative chatbot technology from the IMPACT-FH study.
- Source :
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PEC innovation [PEC Innov] 2023 Feb 04; Vol. 2, pp. 100134. Date of Electronic Publication: 2023 Feb 04 (Print Publication: 2023). - Publication Year :
- 2023
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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).<br />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.<br />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.<br />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.<br />Innovation: FSC and OMC employ innovative digital health technology that can facilitate family communication about hereditary conditions.<br />Competing Interests: The following authors have conflicts of interest to report: Tara J. Schmidlen and Sarah K. Savage are employees and shareholders of Invitae. Amy C. Sturm is an employee and shareholder of 23andMe. Laney K. Jones is a consultant for Novartis.<br /> (© 2023 The Authors.)
Details
- Language :
- English
- ISSN :
- 2772-6282
- Volume :
- 2
- Database :
- MEDLINE
- Journal :
- PEC innovation
- Publication Type :
- Academic Journal
- Accession number :
- 37214500
- Full Text :
- https://doi.org/10.1016/j.pecinn.2023.100134