Building and Deploying a Genomic-Medicine Risk Assessment Model for Diverse Primary Care Populations.

点击翻译按钮获取中文摘要

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 as designed do not meet the needs of low literacy, low resource populations. The goal of this proposal is to develop a scalable end-to-end solution for risk assessment and management that meets the needs of low resource settings. Our central hypothesis is that combining FHH- driven risk assessment, a literacy-enhanced interface using voice-to-text response capture (like ‘Siri’), 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 the proportion of diverse patients who are identified as at increased risk, who undergo testing, and, when appropriate, who initiate cascade screening among relatives. 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 clinical sites with highly diverse patient populations 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 diverse populations. Outcomes will include reach, uptake, clinical utility, accessibility, genetic testing frequency, genetic testing results, and cost-effectiveness. In addition we will convene an advisory panel of stakeholders from industry (laboratories, insurers), providers, patients, and health system to understand sustainability and address knowledge gaps that will promote access when the trial is over.