Key Points Question Can population-level genomic screening identify those at risk for disease? Findings In this cross-sectional study of an unselected population cohort of 50 726 adults who underwent exome sequencing, pathogenic and likely pathogenic BRCA1 and BRCA2 variants were found in a higher proportion of patients than was previously reported. Meaning Current methods to identify BRCA1/2 variant carriers may not be sufficient as a screening tool; population genomic screening for hereditary breast and ovarian cancer may better identify patients at high risk and provide an intervention opportunity to reduce mortality and morbidity.

A barrier to incorporating genomics more broadly is limited access to providers with genomics expertise. Chatbots are a technology‐based simulated conversation used in scaling communications. Geisinger and Clear Genetics, Inc. have developed chatbots to facilitate communication with participants receiving clinically actionable genetic variants from the MyCode® Community Health Initiative (MyCode®). The consent chatbot walks patients through the consent allowing them to opt to receive more or less detail on key topics (goals, benefits, risks, etc.). The follow‐up chatbot reminds participants of suggested actions following result receipt and the cascade chatbot can be sent to at‐risk relatives by participants to share their genetic test results and facilitate cascade testing. To explore the acceptability, usability, and understanding of the study consent, post‐result follow‐up and cascade testing chatbots, we conducted six focus groups with MyCode® participants. Sixty‐two individuals participated in a focus group (n = 33 consent chatbot, n = 29 follow‐up and cascade chatbot). Participants were mostly female (n = 42, 68%), Caucasian (n = 58, 94%), college‐educated (n = 33,53%), retirees (n = 38, 61%), and of age 56 years or older (n = 52, 84%). Few participants reported that they knew what a chatbot was (n = 10, 16%), and a small number reported that they had used a chatbot (n = 5, 8%). Qualitative analysis of transcripts and notes from focus groups revealed four main themes: (a) overall impressions, (b) suggested improvements, (c) concerns and limitations, and (d) implementation. Participants supported using chatbots to consent for genomics research and to interact with healthcare providers for care coordination following receipt of genomic results. Most expressed willingness to use a chatbot to share genetic information with relatives. The consent chatbot presents an engaging alternative to deliver content challenging to comprehend in traditional paper or in‐person consent. The cascade and follow‐up chatbots may be acceptable, user‐friendly, scalable approaches to manage ancillary genetic counseling tasks.

There is growing interest in communicating clinically relevant DNA sequence findings to research participants who join projects with a primary research goal other than the clinical return of such results. Since Geisinger's MyCode Community Health Initiative (MyCode) was launched in 2007, more than 200,000 participants have been broadly consented for discovery research. In 2013 the MyCode consent was amended to include a secondary analysis of research genomic sequences that allows for delivery of clinical results. Since May 2015, pathogenic and likely pathogenic variants from a set list of genes associated with monogenic conditions have prompted "genome-first" clinical encounters. The encounters are described as genome-first because they are identified independent of any clinical parameters. This article (1) details our process for generating clinical results from research data, delivering results to participants and providers, facilitating condition-specific clinical evaluations, and promoting cascade testing of relatives, and (2) summarizes early results and participant uptake. We report on 542 participants who had results uploaded to the electronic health record as of February 1, 2018 and 291 unique clinical providers notified with one or more participant results. Of these 542 participants, 515 (95.0%) were reached to disclose their results and 27 (5.0%) were lost to follow-up. We describe an exportable model for delivery of clinical care through secondary use of research data. In addition, subject and provider participation data from the initial phase of these efforts can inform other institutions planning similar programs.

Purpose Three genetic conditions—hereditary breast and ovarian cancer syndrome, Lynch syndrome, and familial hypercholesterolemia—have tier 1 evidence for interventions that reduce morbidity and mortality, prompting proposals to screen unselected populations for these conditions. We examined the impact of genomic screening on risk management and early detection in an unselected population. Methods Observational study of electronic health records (EHR) among individuals in whom a pathogenic/likely pathogenic variant in a tier 1 gene was discovered through Geisinger’s MyCode project. EHR of all eligible participants was evaluated for a prior genetic diagnosis and, among participants without such a diagnosis, relevant personal/family history, postdisclosure clinical diagnoses, and postdisclosure risk management. Results Eighty-seven percent of participants (305/351) did not have a prior genetic diagnosis of their tier 1 result. Of these, 65% had EHR evidence of relevant personal and/or family history of disease. Of 255 individuals eligible to have risk management, 70% ( n = 179) had a recommended risk management procedure after results disclosure. Thirteen percent of participants (41/305) received a relevant clinical diagnosis after results disclosure. Conclusion Genomic screening programs can identify previously unrecognized individuals at increased risk of cancer and heart disease and facilitate risk management and early cancer detection.

Health care delivery is increasingly influenced by the emerging concepts of precision health and the learning health care system. Although not synonymous with precision health, genomics is a key enabler of individualized care. Delivering patient-centered, genomics-informed care based on individual-level data in the current national landscape of health care delivery is a daunting challenge. Problems to overcome include data generation, analysis, storage, and transfer; knowledge management and representation for patients and providers at the point of care; process management; and outcomes definition, collection, and analysis. Development, testing, and implementation of a genomics-informed program requires multidisciplinary collaboration and building the concepts of precision health into a multilevel implementation framework. Using the principles of a learning health care system provides a promising solution. This article describes the implementation of population-based genomic medicine in an integrated learning health care system-a working example of a precision health program.