Anne C. Madeo

Background: Digital health tools, such as health chatbots, may improve access to scalable health support, but adoption remains inconsistent. Existing models do not fully integrate technology acceptance factors with health motivation factors relevant to digital health use. Objective: This study proposed and tested the health technology acceptance model and examined whether normative message framing and chatbot type were associated with health motivation, technology acceptance, and intention to use a health chatbot. Methods: In October 2025, we conducted a 4 × 2 between-participants online experiment with 1000 US adults recruited from a nationally representative YouGov panel. Participants were randomized to 1 of 8 conditions varying norm message type (self-oriented, peer-oriented, expert-oriented, or family-oriented) and chatbot type (AI-powered or rule-based) in a cancer prevention and genetic risk information scenario. Outcomes included descriptive norms, injunctive norms, perceived susceptibility, perceived severity, perceived benefits, self-efficacy, perceived ease of use, trust, privacy concerns, and usage intention. Data were analyzed using a multivariate ANOVA with Bonferroni-adjusted post hoc tests and multiple linear regression. Results: Peer-oriented and family-oriented messages produced higher usage intention than expert-oriented messages, and peer-oriented messages also increased descriptive norms, injunctive norms, self-efficacy, and trust. AI-powered chatbots were associated with higher usage intention ( P =.02) and greater trust ( P =.008) than rule-based chatbots. In regression analyses, the model explained 50.8% of the variance in usage intention. Usage intention was positively associated with descriptive norms (β=0.087; P =.003), injunctive norms (β=0.078; P =.009), perceived susceptibility (β=0.051; P =.03), perceived benefits (β=0.253; P <.001), and trust (β=0.33; P <.001), and negatively associated with perceived severity (β=–0.047; P =.049) and privacy concerns (β=−0.11; P <.001). Perceived ease of use and self-efficacy were not significant predictors. Conclusions: The health technology acceptance model was a useful framework for explaining the intention to use a health chatbot by combining technology acceptance and health motivation constructs. Both social design features and chatbot design features shaped adoption-related beliefs, with peer-oriented and family-oriented framing and AI-powered chatbots showing particular promise. Trust and privacy concerns remained central determinants of intended use.

PURPOSE: Over 90% of people with hereditary cancer syndromes in the United States remain unidentified. The Genetic Cancer Risk Detector (GARDE) is an open-source, electronic health record (EHR)–integrated, digital health platform that can facilitate genetic cancer risk assessment and genetic testing. This study evaluates its budget impact on health care institutions. METHODS: A budget impact analysis was performed from the perspective of a US health care provider system over a 3-year horizon. Data from the BRIDGE randomized controlled trial data from the University of Utah Health (UHealth) were used, where eligible primary care patients were screened for genetic cancer risk via GARDE. Costs of GARDE were categorized across planning, implementation, and operational phases. Revenue projections were based on Centers for Medicare & Medicaid Services reimbursement rates. Scenario analyses varied uptake of interventions, surveillance intervals, reimbursement rates, and implementation scale. RESULTS: Of 1,444 patients identified by GARDE at UHealth and enrolled in the BRIDGE trial, 205 completed genetic testing, with 15 found to carry pathogenic variants. The total 3-year implementation cost was $29,217 US dollars (USD). Revenue from guideline-recommended procedures totaled $86,563 USD, yielding a net positive budget impact of $57,347 USD. Most revenue (76.4%) was generated by surgical risk-reduction procedures. Scenario analyses revealed high sensitivity to cancer risk-reducing surgery uptake and implementation scale. Modeling 100% uptake of risk-reducing surgeries increased revenue to $128,102 USD, while 20-fold scaling of the implementation population increased revenue to $1.7 million USD. Commercial insurance reimbursement assumptions further amplified revenue. CONCLUSION: GARDE enables scalable hereditary cancer risk assessment within a health care provider system. Even with modest uptake, it yields a positive financial return, and significantly greater revenue is achievable with broader implementation. These findings support adoption of EHR-integrated tools to enhance clinical outcomes in precision cancer prevention and risk management, in an economically viable manner.

PURPOSE: Cascade genetic testing enables identification of relatives at risk of hereditary cancer syndromes, creating opportunities for early detection and prevention. However, uptake of cascade testing remains low, with approximately one-third of eligible relatives completing testing, largely because of reliance on patient-mediated communication. Although clinician-mediated outreach has demonstrated improved efficacy, it is often limited by resource demands. Scalable digital health tools are a promising strategy to address this gap in testing uptake. METHODS: In this quality improvement initiative, we developed a digital cascade chatbot to deliver gene-specific education and facilitate access to genetic services among at-risk relatives. Between October 2024 and January 2025, 100 consecutive probands with a hereditary cancer pathogenic variant seen in a gynecologic oncology clinic were offered a cascade chatbot to share with their relatives. The primary outcome was proband acceptance of the cascade chatbot. Secondary outcomes included sharing of the cascade chatbot with at-risk relatives and relatives' subsequent utilization of genetic services. Outcomes were evaluated through telephone follow-up at 2 weeks and 3 months after chatbot introduction. RESULTS: Fifty-nine of 100 probands reported having relatives who had not undergone genetic testing. Among this group, 58 (98.3%) accepted the cascade chatbot. At 2-week follow-up, 44 of 58 probands (75.9%) had shared the cascade chatbot with at least one relative, and an additional eight (13.8%) reported plans to share. At 3-month follow-up with probands, 48 (82.8%) probands had shared the cascade chatbot with at least one relative. A total of 122 relatives received the cascade chatbot and 96 (78.7%) were reached for 3-month follow-up. Among the 96 relatives reached, 49 (51.0%) had scheduled or completed a genetics appointment, and of them, 36 (73.5%) had completed testing. CONCLUSION: A cascade chatbot was highly acceptable to probands and effectively engaged relatives. Scalable digital health tools may enhance cascade testing and support precision cancer prevention.

Newborn screening (NBS), a mandated public health intervention, allows the identification of babies with potentially life-threatening disorders and facilitates disease diagnosis and management before the onset of symptoms. While NBS saves lives, the process can be fraught with anxiety and unanswered questions from parents or guardians of newborns, especially as they wait for an appointment with a clinician. This study aimed to describe the development and testing of an educational chatbot (NBSchat) to address the emotional support and information needs of parents of newborns identified with sickle cell trait via NBS. NBSchat, a fully scripted (ie, rule-based) chatbot, was developed by a multidisciplinary team and evaluated through a sequential multiple methods study, including interviews and a survey. To inform chatbot design, we conducted semistructured interviews with 11 adults-5 clinicians who work with parents of infants identified with sickle cell trait through NBS and 6 parents of infants aged 12 months or less-using the critical incident technique and think-aloud tasks while using a prototype of NBSchat. Transcripts underwent thematic analysis. In a survey, 250 parents of infants aged 12 months or less without abnormal NBS results were shown a mock newborn screening result letter and then interacted with NBSchat, after which they self-reported emotional and attitudinal outcomes before and after the simulated exposure. Feedback from interviews confirmed that parents are distressed by trait results and actively seek information and reassurance. Thematic analysis indicated that NBSchat provided reliable, accurate information that parents wanted and had the potential to reduce negative emotions (eg, provide relief and reduce stress). Key strengths included addressing an immediate health concern and offering reassurance. The results of the postintervention survey indicated that, compared to pre-exposure scores, participants reported significantly lower negative emotions (mean 7.0 [SD 3.2] vs 5.8 [SD 3.2] out of 12; mean difference -1.2, 95% CI -1.57 to -0.83; P<.001), improved positive emotions (reflected by a decrease in the reverse-coded positive emotion score; mean 8.6 [SD 4.2] vs 7.8 [SD 4] out of 16; mean difference -0.8, 95% CI -1.27 to -0.37; P<.001), and reduced uncertainty (mean 6.5 [SD 3] vs 5.5 [SD 3.4] out of 12; mean difference -1, 95% CI -1.42 to -0.58; P<.001). Parents noted that NBSchat provided immediate reassurance and was convenient to access. They further reported that the predefined, structured questions in the script helped guide their learning and understanding. Overall, participants who interacted with NBSchat found it to be acceptable, with improved emotional measures after its use. Future research will investigate the outcomes of using the chatbot and its implementation in a pragmatic randomized controlled trial.

BACKGROUND Newborn screening (NBS), a mandated public health intervention, allows the identification of babies with potentially life-threatening disorders and facilitates disease diagnosis and management before the onset of symptoms. While NBS saves lives, the process can be fraught with anxiety and unanswered questions from parents or guardians of newborns, especially while they wait for an appointment with a clinician. OBJECTIVE To describe the development and testing of an educational chatbot (NBSchat) to address the emotional support and information needs of parents of newborns identified with sickle cell trait (SCT) via NBS. METHODS NBSChat, a fully scripted (i.e, rule-based) chatbot, was developed by a multidisciplinary team and evaluated through a sequential multiple methods study, including interviews and a survey. To inform chatbot design, we conducted semi-structured interviews with 11 adults; five clinicians who work with parents of infants identified with SCT through NBS and six parents of infants ≤12 months—using the critical incident technique and think-aloud tasks while using a prototype of NBSchat. Transcripts underwent thematic analysis. 250 parents of infants ≤12 months without abnormal NBS results completed the survey. Parents were asked to self-report emotional and attitudinal outcomes before and after interacting with NBSchat. RESULTS Feedback from interviews confirmed that parents are distressed by trait results and actively seek information and reassurance. Thematic analysis indicated that NBSchat provided reliable, accurate information that parents wanted and had the potential to reduce negative emotions (e.g., provide relief and reduce stress). Key strengths included addressing an immediate health concern and offering reassurance. Results of the post-intervention survey indicated that, compared to pre-exposure scores, participants reported significantly lower negative emotions (mean 7.0 vs 5.8 out of 12; t-test, P<.001), higher positive emotions (mean 8.6 vs 7.8 out of 16; t-test, P<.001), and reduced uncertainty (mean 6.5 vs 5.5 out of 12; t-test, P<.001). Parents noted that NBSchat provided immediate reassurance and was convenient to access. They further reported that the predefined, structured questions in the script helped guide their learning and understanding. CONCLUSIONS Overall, participants who interacted with NBSchat found it to be acceptable, with improved emotional measures after its use. Future research will investigate outcomes of using the chatbot and its implementation in a pragmatic randomized controlled trial.