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。

基本信息

批准号：
9594066
负责人：
Lori Ann Orlando
金额：
$ 36.44万
依托单位：
DUKE UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2018
资助国家：
美国
起止时间：
2018-09-24 至 2023-06-30
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/9594066
关键词：
Address Adoption Affect Algorithms Beds Caring Clinical Complex Computerized Medical Record Counseling Data Data Collection Development Disease Management Effectiveness Family Family health status Family member Frequencies Genetic Counseling Genetic screening method Genomic medicine Genomics Goals Guidelines Health system Healthcare Systems Hybrids Individual Industry Inherited Insurance Carriers Intervention Knowledge Laboratories Learning Measures Minority Modeling Online Systems Outcome Pathway interactions Patient Care Patient Care Planning Patient risk Patient-Focused Outcomes Patients Population Population Heterogeneity Preventive care Primary Health Care Provider Quality of life Randomized Recording of previous events Research Design Resources Risk Risk Assessment Risk Management Service delivery model Social Network Syndrome System Technology Test Result Testing Text Time Voice base care providers clinical care clinical decision support clinical decision-making clinical research site cohort cost cost effectiveness design disorder risk effectiveness trial evidence base evidence based guidelines health disparity high risk implementation science improved literacy meetings patient population population health primary care setting programs response risk sharing routine care screening tool uptake

项目摘要

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.

家庭健康史（FHH），这是基因组医学的关键组成部分，这对于识别处于养生状况的风险和基因检测结果的背景下的个人，继续是在临床护理中广泛未充分利用和低估。足够数据收集和合成的障碍众多，跨越了所有临床利益相关者：患者，提供者和卫生系统。很重要的是，他们包括普遍的观点，即FHH不重要，除了在某些情况下，它很少有助于临床决策。从这个角度来看，很少有提供商愿意分配宝贵的时间收集详细的FHHS或学习将FHH数据合成所需的复杂算法为可行的护理计划。但是，在对未选择人群中基于FHH的系统风险评估的研究中，有25％患者符合（可行的）遗传条件的风险标准。基于FHH的风险评估计划出现以解决这些障碍，但根据设计，无法满足识字少，资源低的需求人群。该提案的目的是为风险评估开发可扩展的端到端解决方案和满足低资源设置需求的管理。我们的核心假设是将FHH-- 驱动风险评估，一种使用语音到文本响应捕获（例如“ Siri”）的识字增强界面参与度（通过社交网络平台进行数据收集和风险共享）和基因测试交付系统将创建一个解决方案，以吸引并增加的潜水员患者比例确定风险增加，正在接受测试以及在适当的时候启动级联筛查的人在亲戚中。在此提案中，我们将定义和部署这种新的护理交付模型为“基因组医学风险评估每个人的护理”（恩典）。为此，我们将1）开发和部署在患者种群高度多样的临床部位使用实体前评估以选择的模型为患者和提供者提供最合适的整合选项和途径； 2）执行随机实施效果务实的混合试验，以评估实施和有效性与这些潜水员人群相关的结果。结果将包括覆盖范围，吸收，临床实用程序，可及性，基因测试频率，基因检测结果和成本效益。另外，我们将召集来自行业（实验室，保险），提供者，患者和的利益相关者的咨询小组卫生系统了解可持续性并解决审判时将促进访问的知识差距结束了。