Emerson Borsato Borsato

Introduction: Lung cancer remains a leading cause of cancer death, though lung cancer screening (LCS) uptake remains low, particularly among low-resource patient populations. LungSMART Utah uses a multi-level approach to promote LCS to patients served by Community Health Centers (CHCs). This paper reports on the formative evaluation and cultural adaptation of the project’s patient-facing digital health communication content and delivery, in service of the larger population health effort to promote shared decision-making for LCS. Methods: We used a mixed-method approach (cross-sectional surveys and usability observations) to evaluate developed content and ensure cultural adaptation to serve all patients within the CHCs. First, a messaging study ( N =79) used an online survey to evaluate patient perceptions of content quality and to collect feedback on the quality of content in both English and Spanish. Second, a usability assessment ( N =17) employed think-aloud protocols to evaluate user experience and technical delivery. Results: Patients in the messaging study generally rated content favorably. Content evaluations were similar in English and Spanish, though some participants did suggest phrasing changes. The usability assessment identified critical technical and delivery hurdles, including feeling overloaded by choices for some project components and message sequencing concerns. Cultural adaptation insights from Spanish-speaking participants highlighted the need for conversational alternatives to some formal medical terms, explicit transparency about cost, and addressing access anxiety. All participants indicated noting message content came from the CHCs helped foster trust. Conclusion: Mixed-method formative evaluation and cultural adaptation provide important insights for developing digital population health tools. Addressing technical delivery gaps and specific community concerns will improve the reach and efficacy of LCS promotion in low-resource settings.

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.

The increasing demand for population-wide genomic screening and the limited availability of genetic counseling resources have created a pressing need for innovative service delivery models. Chatbots powered by large language models (LLMs) have shown potential in genomic services, particularly in pretest counseling, but their application in returning positive population-wide genomic screening results remains underexplored. Leveraging advanced LLMs like GPT-4 offers an opportunity to address this gap by delivering accurate, contextual, and user-centered communication to individuals receiving positive genetic test results. This project aimed to design, implement, and evaluate a chatbot integrated with GPT-4, tailored to support the return of positive genomic screening results in the context of South Carolina’s In Our DNA SC program. This initiative offers free genetic screening to 100,000 individuals, with over 33,000 results returned and numerous positive findings for conditions such as Lynch syndrome, hereditary breast and ovarian cancer syndrome, and familial hypercholesterolemia. A 3-step prompt engineering process using retrieval-augmented generation and few-shot techniques was used to create the chatbot. Training materials included patient frequently asked questions, genetic counseling scripts, and patient-derived queries. The chatbot underwent iterative refinement based on 13 training questions, while performance was evaluated through expert ratings on responses to 2 hypothetical patient scenarios. The 2 scenarios were intended to represent common but distinct patient profiles in terms of gender, race, ethnicity, age, and background knowledge. Domain experts rated the chatbot using a 5-point Likert scale across 8 predefined criteria: tone, clarity, program accuracy, domain accuracy, robustness, efficiency, boundaries, and usability. The chatbot achieved an average score of 3.86 (SD 0.89) across all evaluation metrics. The highest-rated criteria were tone (mean 4.25, SD 0.71) and usability (mean 4.25, SD 0.58), reflecting the chatbot’s ability to communicate effectively and provide a seamless user experience. Boundary management (mean 4.0, SD 0.76) and efficiency (mean 3.88, SD 1.08) also scored well, while clarity and robustness received ratings of 3.81 (SD 1.05) and 3.81 (SD 0.66), respectively. Domain accuracy was rated 3.63 (SD 0.96), indicating satisfactory performance in delivering genetic information, whereas program accuracy received the lowest score of 3.25 (SD 1.39), highlighting the need for improvements in delivering program-specific details. This project demonstrates the feasibility of using LLM-powered chatbots to support the return of positive genomic screening results. The chatbot effectively handled open-ended patient queries, maintained conversational boundaries, and delivered user-friendly responses. However, enhancements in program-specific accuracy are essential to maximize its utility. Future research will explore hybrid chatbot designs that combine the strengths of LLMs with rule-based components to improve scalability, accuracy, and accessibility in genomic service delivery. The findings underscore the potential of generative artificial intelligence tools to address resource limitations and improve the accessibility of genomic health care services.

Journal of Medical Internet Research - International Scientific Journal for Medical Research, Information and Communication on the Internet #Preprint #PeerReviewMe: Warning: This is a unreviewed preprint. Readers are warned that the document has not been peer-reviewed by expert/patient reviewers or an academic editor, may contain misleading claims, and is likely to undergo changes before final publication, if accepted, or may have been rejected/withdrawn. Readers with interest and expertise are encouraged to sign up as peer-reviewer, if the paper is within an open peer-review period. Please cite this preprint only for review purposes or for grant applications and CVs (if you are the author).

Background: The creation of a computerized clinical database with the ability to collect prospective information from patients and with the possibility of rescue and crossing data enables scientific studies of higher quality and credibility in less time. Aim: To validate, in a single master protocol, the clinical data referring to Surgery of Digestive System in a multidisciplinary way, incorporating in the SINPE© platform, and to verify the incidence of digestive diseases based on the prospectively performed collections. Method: Organize in one software, in a standardized structure, all the pre-existing items in the SINPE© database; the theoretical basis was computerized through the MIGRASINPE© module creating a single multiprofessional master protocol for use as a whole. Results: The existing specific protocols were created and/or adapted - they correspond to the most prevalent digestive diseases - unifying them. The possibility of multiprofessional use was created by integrating all data collected from medicine, nursing, physiotherapy, nutrition and health management in a prospective way. The total was 4,281 collections, distributed as follows: extrahepatic biliary tract, n=1,786; esophagus, n=1015; anorectal, n=736; colon, n=550; small intestine, n=86; pancreas, n=71; stomach, n=23; liver, n=14. Conclusions: The validation of the unification and structuring in a single master protocol of the clinical data referring to the Surgery of the Digestive System in a multiprofessional and prospective way was possible and the epidemiological study carried out allowed to identify the most prevalent digestive diseases.