Optimizing communication strategies and designing a comprehensive program to facilitate cascade testing for familial hypercholesterolemia.

BACKGROUND: This project aimed to optimize communication strategies to support family communication about familial hypercholesterolemia (FH) and improve cascade testing uptake among at-risk relatives. Individuals and families with FH provided feedback on multiple strategies including: a family letter, digital tools, and direct contact. METHODS: Feedback from participants was collected via dyadic interviews (n = 11) and surveys (n = 98) on communication strategies and their proposed implementation to improve cascade testing uptake. We conducted a thematic analysis to identify how to optimize each strategy. We categorized optimizations and their implementation within the project's healthcare system using a Traffic Light approach. RESULTS: Thematic analysis resulted in four distinct suggested optimizations for each communication strategy and seven suggested optimizations that were suitable across all strategies. Four suggestions for developing a comprehensive cascade testing program, which would offer all optimized communication strategies also emerged. All optimized suggestions coded green (n = 21) were incorporated. Suggestions coded yellow (n = 12) were partially incorporated. Only two suggestions were coded red and could not be incorporated. CONCLUSIONS: This project demonstrates how to collect and analyze stakeholder feedback for program design. We identified feasible suggested optimizations, resulting in communication strategies that are patient-informed and patient-centered. Optimized strategies were implemented in a comprehensive cascade testing program.

Democratizing genomics: Leveraging software to make genetics an integral part of routine care.

Genetic testing can provide definitive molecular diagnoses and guide clinical management decisions from preconception through adulthood. Innovative solutions for scaling clinical genomics services are necessary if they are to transition from a niche specialty to a routine part of patient care. The expertise of specialists, like genetic counselors and medical geneticists, has traditionally been relied upon to facilitate testing and follow-up, and while ideal, this approach is limited in its ability to integrate genetics into primary care. As individuals, payors, and providers increasingly realize the value of genetics in mainstream medicine, several implementation challenges need to be overcome. These include electronic health record integration, patient and provider education, tools to stay abreast of guidelines, and simplification of the test ordering process. Currently, no single platform offers a holistic view of genetic testing that streamlines the entire process across specialties that begins with identifying at-risk patients in mainstream care settings, providing pretest education, facilitating consent and test ordering, and following up as a "genetic companion" for ongoing management. We describe our vision for using software that includes clinical-grade chatbots and decision support tools, with direct access to genetic counselors and pharmacists within a modular, integrated, end-to-end testing journey.

Hereditary Cancer Risk Using a Genetic Chatbot Before Routine Care Visits.

OBJECTIVE: To examine user uptake and experience with a clinical chatbot that automates hereditary cancer risk triage by collecting personal and family cancer history in routine women's health care settings. METHODS: We conducted a multicenter, retrospective observational study of patients who used a web-based chatbot before routine care appointments to assess their risk for hereditary breast and ovarian cancer, Lynch syndrome, and adenomatous polyposis syndromes. Outcome measures included uptake and completion of the risk-assessment and educational section of the chatbot interaction and identification of hereditary cancer risk as evaluated against National Comprehensive Cancer Network criteria. RESULTS: Of the 95,166 patients invited, 61,070 (64.2%) engaged with the clinical chatbot. The vast majority completed the cancer risk assessment (89.4%), and most completed the genetic testing education section (71.4%), indicating high acceptability among those who opted to engage. The mean duration of use was 15.4 minutes (SD 2 hours, 56.2 minutes) when gaps of inactivity longer than 5 minutes were excluded. A personal history of cancer was reported by 19.1% (10,849/56,656) and a family history of cancer was reported by 66.7% (36,469/54,652) of patients who provided the relevant information. One in four patients (14,850/54,547) screened with the chatbot before routine care appointments met National Comprehensive Cancer Network criteria for genetic testing. Among those who were tested, 5.6% (73/1,313) had a disease-causing pathogenic variant. CONCLUSION: A chatbot digital health tool can help identify patients at high risk for hereditary cancer syndromes before routine care appointments. This scalable intervention can effectively provide cancer risk assessment, engage patients with educational information, and facilitate a path toward preventive genetic testing. FUNDING SOURCE: Implementation of the chatbot in clinics was funded by industry support from commercial genetic testing laboratories Ambry, Invitae, and Progenity.

Developing and Optimizing Innovative Tools to Address Familial Hypercholesterolemia Underdiagnosis: Identification Methods, Patient Activation, and Cascade Testing for Familial Hypercholesterolemia.

BACKGROUND: Familial hypercholesterolemia (FH) is the most common cardiovascular genetic disorder and, if left untreated, is associated with increased risk of premature atherosclerotic cardiovascular disease, the leading cause of preventable death in the United States. Although FH is common, fatal, and treatable, it is underdiagnosed and undertreated due to a lack of systematic methods to identify individuals with FH and limited uptake of cascade testing. METHODS AND RESULTS: This mixed-method, multi-stage study will optimize, test, and implement innovative approaches for both FH identification and cascade testing in 3 aims. To improve identification of individuals with FH, in Aim 1, we will compare and refine automated phenotype-based and genomic approaches to identify individuals likely to have FH. To improve cascade testing uptake for at-risk individuals, in Aim 2, we will use a patient-centered design thinking process to optimize and develop novel, active family communication methods. Using a prospective, observational pragmatic trial, we will assess uptake and effectiveness of each family communication method on cascade testing. Guided by an implementation science framework, in Aim 3, we will develop a comprehensive guide to identify individuals with FH. Using the Conceptual Model for Implementation Research, we will evaluate implementation outcomes including feasibility, acceptability, and perceived sustainability as well as health outcomes related to the optimized methods and tools developed in Aims 1 and 2. CONCLUSIONS: Data generated from this study will address barriers and gaps in care related to underdiagnosis of FH by developing and optimizing tools to improve FH identification and cascade testing.