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Details

Autor(en) / Beteiligte
Titel
Facilitating family communication of familial hypercholesterolemia genetic risk: Assessing engagement with innovative chatbot technology from the IMPACT-FH study
Ist Teil von
  • PEC innovation, 2023-12, Vol.2, p.100134-100134, Article 100134
Ort / Verlag
Netherlands: Elsevier B.V
Erscheinungsjahr
2023
Link zum Volltext
Quelle
Alma/SFX Local Collection
Beschreibungen/Notizen
  • 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). 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. 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. 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. FSC and OMC employ innovative digital health technology that can facilitate family communication about hereditary conditions. •Chatbots can facilitate family communication about hereditary conditions.•Most preferred to receive chatbots via the patient electronic health record portal.•Older individuals tended to opt out of chatbot communications more frequently.•Providing reminders and additional touchpoints may improve chatbot engagement.
Sprache
Englisch
Identifikatoren
ISSN: 2772-6282
eISSN: 2772-6282
DOI: 10.1016/j.pecinn.2023.100134
Titel-ID: cdi_doaj_primary_oai_doaj_org_article_4f5ef3314c2841cca458a381cb9d2e1f

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