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

为不同的初级保健人群建立和部署基因组医学风险评估模型。

• 批准号: 10630415
• 负责人: Lori Ann Orlando
• 金额: $ 81.27万
• 依托单位: DUKE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-16 至 2023-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10630415
• 关键词: Address; Affect; Algorithms; Caring; Clinical; Complex; Data; Data Collection; Effectiveness; Family; Family health status; Frequencies; Genomic medicine; Genomics; Goals; Health system; Hybrids; Individual; Industry; Inherited; Insurance Carriers; Knowledge; Laboratories; Learning; Modeling; Outcome; Pathway interactions; Patients; Population; Population Heterogeneity; Preventive care; Primary Health Care; Provider; Randomized; Recording of previous events; Resource-limited setting; Resources; Risk; Risk Assessment; Risk Management; Service delivery model; Social Network; System; Test Result; Testing; Text; Time; Voice; base; clinical care; clinical decision-making; clinical research site; cost effectiveness; design; disorder risk; effectiveness outcome; genetic testing; hereditary risk; high risk; implementation evaluation; implementation outcomes; literacy; patient population; programs; response; risk sharing; screening; uptake

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.

家族健康史 (FHH) 是基因组医学的重要组成部分，对于识别家族健康史至关重要 面临遗传性疾病风险和基因检测结果背景的个人，仍然是 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。 包括这样一种普遍观点：FHH 除了在特定情况下并不重要，而且它很少有助于 从这个角度来看，很少有提供者愿意分配宝贵的时间来制定临床决策。 收集详细的 FHH 或学习将 FHH 数据合成为可行护理所需的复杂算法 然而，在对未选定人群进行基于 FHH 的系统风险评估的研究中，25% 的人没有计划。 患者符合基于 FHH 的风险评估计划的（可操作的）遗传性疾病的风险标准。 为解决这些障碍而出现，但其设计并不能满足低识字率、低资源的需求 该提案的目标是开发一个可扩展的端到端解决方案，用于风险评估和评估。 我们的中心假设是结合 FHH- 来满足低资源环境的需求。 驱动的风险评估、使用语音到文本响应捕获（如“Siri”）的读写能力增强界面、家庭 参与（通过社交网络平台进行数据收集和风险分担），以及基因检测 交付系统，将创建一个解决方案，吸引并增加不同患者的比例 被确定为风险增加的人，接受测试的人，以及在适当的情况下启动级联筛查的人 在本提案中，我们将这种新的护理服务模式定义并部署为“基因组”。 医学风险评估关爱每个人”（GRACE）为此，我们将 1) 开发和部署 模型使用临床地点的实施前评估来选择高度多样化的患者群体 对患者和提供者来说最合适的整合选项和途径；以及 2) 执行 评估实施情况和有效性的随机实施-有效性实用混合试验 与这些不同人群相关的成果将包括覆盖范围、吸收率、临床效用、 可及性、基因检测频率、基因检测结果和成本效益。 召集来自行业（实验室、保险公司）、提供商、患者和其他利益相关者的咨询小组 卫生系统了解可持续性并解决知识差距，这将促进试验时的获取 结束了。