Optimizing Personalized Care Using Economic Studies of Genomic Testing: Supporting Patient-Provider

描述（由申请人提供）：个性化医疗的挑战是知道何时以及如何应用筛查和诊断测试以及有针对性的治疗，以便在人口水平上产生更好的结果和具有成本效益的资源投资。迄今为止，基因组检测已被引入实践，但缺乏关于其对健康和经济结果的实际影响的经验知识，或者在多个卫生保健系统中由不同的医生在不同的患者亚组中使用。没有研究估计基因组检测在社区中使用时的成本效益，也没有研究使用强大的美国人口模型中的原始数据来比较现实生活中的成本效益与使用试验数据或假设队列的分析。我们的项目将使用一个创新的多标准决策分析框架来评估医生、患者和系统因素对在社区提供个性化癌症治疗的竞争策略优先级的影响。我们专注于癌症，因为这种疾病的基因组应用数量庞大且不断增加，对公共卫生有重大影响的潜力，以及我们利用专业技术的能力

DESCRIPTION (provided by applicant): The challenge of personalized medicine is knowing when and how to apply screening and diagnostic tests and targeted treatments so that they result improved outcomes and cost-effective investment of resources at a population level. To date, genomic tests have been introduced into practice with scant empirical knowledge about their actual impact on health and economic outcomes, or use in diverse patient subgroups by varying physicians in multiple health care systems. No study has estimated the cost-effectiveness of genomic tests as they are used in the community, or used primary data in a robust US population model to compare real-life cost-effectiveness to analyses using trial data or hypothetical cohorts. Our project will use an innovative multi-criteria decision analysis framework to evaluate the impact of physician, patient, and system factors on the prioritization of competing strategies for the delivery of personalized cancer care in the community. We concentrate on cancer because of the large and growing number of genomic applications for this disease, the potential for major public health impact, and our ability to leverage specialized resources based on NCI's investment in several large research programs we lead. Our approaches are designed to be generalizable to personalized health interventions for other chronic diseases. We will (1) Identify critical factors that influence the cost-effectiveness of genomic testing based on guideline- recommended care vs. usual care without genomic testing, using our validated CISNET multi-cohort population model to evaluate an exemplar gene expression profile test in breast cancer; (2) Describe actual community care, and measure criteria important for decisions about use of genomic tests, using existing electronic records linked with registry data from diverse structures of care in two defined geographic regions, and surveys of a nested sample of 800 patients and their physicians; and (3) Test whether population cost-effectiveness based on actual use and outcomes of genomic testing in community practice is different from that predicted assuming all receive guideline care, and evaluate how the use of multi-criteria decision analysis compared with traditional cost-effectiveness analysis affects the prioritization of strategies. This research will provide a generalizable framework and tools for obtaining evidence on factors affecting the cost-effective use of genomic testing in "real world" community practice. Our results will add an important foundation in data and methods for future economic analyses that inform that the translation of NIH-supported research into practice and address priorities to use personalized medicine to better prevent, screen, diagnose, and treat cancer.