Deploying a genomic-medicine risk assessment model for diverse primary care populations and settings

为不同的初级保健人群和环境部署基因组医学风险评估模型

基本信息

批准号：
10470752
负责人：
Lori Ann Orlando
金额：
$ 72.15万
依托单位：
DUKE UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2021
资助国家：
美国
起止时间：
2021-08-17 至 2026-06-30
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10470752
关键词：
Address Affect Algorithms Caring Caucasians Characteristics Clinical Complex Computerized Medical Record Counseling Data Data Collection Development Disease Management Effectiveness Family Family health status Family member Frequencies Funding General Population Genetic Counseling Genomic medicine Genomics Goals Guidelines Health system Healthcare Systems Hybrids Individual Inherited Learning Minority Minority Groups Modeling Online Systems Outcome Participant 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 Reduce health disparities Resource-limited setting Resources Risk Risk Assessment Risk Management Service delivery model Social Network Syndrome System Technology Test Result Testing Time Underserved Population United States National Institutes of Health Work base care providers clinical care clinical decision support clinical decision-making clinical research site cost cost effectiveness data standards design disorder risk education access effectiveness implementation trial effectiveness outcome evidence base evidence based guidelines genetic testing health disparity hereditary risk high risk implementation evaluation implementation framework implementation outcomes implementation science improved literacy meetings patient population population health pragmatic implementation primary care setting programs risk sharing routine care screening testing access tool uptake

Address Affect Algorithms Caring Caucasians Characteristics Clinical Complex Computerized Medical Record Counseling Data Data Collection Development Disease Management Effectiveness Family Family health status Family member Frequencies Funding General Population Genetic Counseling Genomic medicine Genomics Goals Guidelines Health system Healthcare Systems Hybrids Individual Inherited Learning Minority Minority Groups Modeling Online Systems Outcome Participant 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 Reduce health disparities Resource-limited setting Resources Risk Risk Assessment Risk Management Service delivery model Social Network Syndrome System Technology Test Result Testing Time Underserved Population United States National Institutes of Health Work base care providers clinical care clinical decision support clinical decision-making clinical research site cost cost effectiveness data standards design disorder risk education access effectiveness implementation trial effectiveness outcome evidence base evidence based guidelines genetic testing health disparity hereditary risk high risk implementation evaluation implementation framework implementation outcomes implementation science improved literacy meetings patient population population health pragmatic implementation primary care setting programs risk sharing routine care screening testing access tool uptake

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项目摘要

Abstract 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, 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.

抽象的家庭健康史（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-- 驱动的风险评估，识字增强的界面，家庭参与度（通过社交网络平台对于数据收集和风险共享）以及一个基因测试输送系统，将创建一种参与和增加了被确定为风险增加，接受测试的潜水员患者的比例，以及在适当的情况下，谁在亲戚之间启动级联筛选。在此提案中，我们将定义和部署这种新的护理交付模型是“每个人的基因组医学风险评估护理”（恩典）。为此，我们将使用临床站点的预料前评估来开发和部署模型有高度潜水员的患者人群选择两者的最合适的整合选项和途径患者和提供者； 2）执行随机实施效应实用混合试验评估与这些不同人群相关的实施和有效性结果。结果会包括覆盖范围，吸收，临床效用，可及性，基因测试频率，基因检测结果以及成本 - 效力。