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）是基因组医学的一个重要组成部分，对于识别 面临遗传性疾病风险和基因检测结果背景风险的个人， 在临床护理中被广泛地利用和低估。妨碍充分收集和综合数据的障碍 有很多，涉及所有临床利益相关者：患者，提供者和卫生系统。重要的是，他们 包括一种普遍的观点，即除了在某些情况下，FHH不重要，而且它很少对 临床决策从这个角度来看，很少有供应商愿意分配宝贵的时间， 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 布局然而，在对健康人群进行系统性FHH风险评估的研究中， 患者符合（可采取行动的）遗传性疾病的风险标准。基于FHH的风险评估计划 出现了解决这些障碍，但作为设计不满足低识字率，低资源， 人口。本提案的目标是为风险评估开发可扩展的端到端解决方案， 管理，满足低资源设置的需要。我们的核心假设是，结合FHH- 驱动的风险评估，使用语音到文本响应捕获（如“语音”）的识字增强界面，家庭 参与（通过社交网络平台进行数据收集和风险共享），以及基因检测 输送系统，将创造一个解决方案，参与和增加的比例，不同的病人谁是 被确定为风险增加，接受检测，并在适当时启动级联筛查 亲戚之间。在本提案中，我们将定义和部署这种新的医疗服务模式，即“基因组 “人人享有医疗风险评估”（GRACE）。为此，我们将1）开发和部署 在具有高度多样化患者人群的临床研究中心使用实施前评估的模型， 为患者和提供者提供最合适的整合选项和途径;以及2）执行 随机实施-有效性实用混合试验，以评估实施和有效性 与这些不同人群相关的结果。结果将包括覆盖率、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。此外，我们将 召集一个由来自行业（实验室、保险公司）、供应商、患者和 卫生系统了解可持续性并解决知识差距，这将在试验时促进获得 结束了