Deploying a genomic-medicine risk assessment model for primary care populations

Family health history (FHH), a critical component of genomic medicine that is essential for both identifying individuals at risk for hereditary conditions and for contextualizing results of genetic testing, continues to be broadly underutilized and underappreciated in clinical care. Barriers to adequate data collection and synthesis are numerous and cross all clinical stakeholders: patients, providers, and health systems. Significantly, they include the pervasive view that FHH is unimportant except in select cases and that it rarely contributes to clinical decision making. With this perspective, few providers have been willing to allocate precious time to collect detailed FHHs or to learn the complex algorithms required to synthesize FHH data into actionable care plans. However, in studies of systematic FHH-based risk assessments in unselected populations, 25% of patients meet risk criteria for (actionable) hereditary conditions. FHH-based risk assessment programs have emerged to address these barriers, but are not always aaccessible. The goal of this proposal is to develop a scalable end-to-end solution for risk assessment and management that meets the needs of all patients. Our central hypothesis is that combining FHH-driven risk assessment, a literacy-enhanced interface, family engagement (through social networking platforms for data gather and risk sharing), and a genetic testing delivery system, will create a solution that engages and increases access to genetic testing and genetic guided care. In this proposal we will define and deploy this new care delivery model as the “Genomic medicine Risk Assessment Care for Everyone” (GRACE). To this end we will 1) develop and deploy the model using pre-implementation assessments at different types of clinical sites to select the most appropriate integration options and pathways for both patients and providers; and 2) perform a randomized implementation-effectiveness pragmatic hybrid trial to assess implementation and effectiveness outcomes relevant to these underserved populations. Outcomes will include reach, uptake, clinical utility, accessibility, genetic testing frequency, genetic testing results, and cost-effectiveness.