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

家族健康史(FHH)，这是基因组医学的关键组成部分，对于识别 面临遗传疾病和基因检测结果背景分析风险的个人，继续 在临床护理中普遍未得到充分利用和重视。充分收集和综合数据的障碍 数量众多，涉及所有临床利益相关者：患者、提供者和卫生系统。值得注意的是，他们 包括普遍的观点，认为FHH不重要，除非在特定情况下，而且它很少促成 临床决策。在这种情况下，很少有供应商愿意将宝贵的时间分配给 收集详细的FHH或学习将FHH数据合成为可操作护理所需的复杂算法 计划。然而，在对未选定人群进行系统性FHH风险评估的研究中，25%的人 患者符合(可操作的)遗传性疾病的风险标准。基于FHH的风险评估计划有 出现了解决这些障碍，但设计不能满足低识字率、低资源的需求 人口。该提案的目标是开发可扩展的端到端解决方案，用于风险评估和 满足低资源设置需求的管理。我们的中心假设是结合FHH- 驱动型风险评估，使用语音到文本响应捕获的识字增强界面(如Siri)，家庭 参与(通过社交网络平台收集数据和分担风险)，以及基因检测 交付系统，将创建一个解决方案，以参与并增加不同患者的比例 被确定为风险增加的人，接受检测的人，以及在适当的情况下，启动级联筛查的人 在亲戚之间。在这份提案中，我们将定义和部署这种新的医疗服务模式，即基因组 医疗风险评估关爱每一个人“(Grace)。为此，我们将1)开发和部署 在具有高度多样化的患者群体的临床站点使用实施前评估的模式来选择 为患者和提供者提供最合适的集成选项和途径；以及2)执行 评估实施和效果的随机实施-有效性务实混合试验 与这些不同人群相关的结果。结果将包括覆盖范围、摄取、临床效用、 可获得性、基因检测频率、基因检测结果和成本效益。此外，我们还将 召集一个由来自行业(实验室、保险公司)、提供商、患者和 卫生系统了解可持续性并解决知识差距，以促进在试验时获得 已经结束了。

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.