OPTIMIZING PERSONALIZED CARE USING ECONOMIC STUDIES OF GENOMIC TESTING

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，以便在人群水平上改善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，但对其对健康和经济结果的实际影响缺乏经验知识，或在多个医疗保健系统中由不同医生在不同患者亚组中使用。没有研究估计基因组检测在社区中使用的成本效益，或者使用强大的美国人口模型中的原始数据来比较实际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，以评估医生，患者和系统因素对社区个性化癌症护理提供竞争策略的优先级的影响。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长，对重大公共卫生影响的潜力，以及我们利用专业知识的能力。 NCI在我们领导的几个大型研究项目中的投资。我们的方法旨在推广到其他慢性病的个性化健康干预。我们将（1）根据指南推荐的治疗与不进行基因组检测的常规治疗，使用我们验证的CISNET多队列人群模型来评估乳腺癌中的示例性基因表达谱检测，确定影响基因组检测成本效益的关键因素;（2）描述实际的社区护理，并衡量决定使用基因组测试的重要标准，使用现有的电子记录与来自两个确定的地理区域的不同护理结构的登记数据相关联，并对800名患者及其医生的嵌套样本进行调查;（3）检验人口成本是否-基于社区实践中基因组检测的实际使用和结果的有效性不同于假设所有人都接受指导性护理的预测，并评估与传统的成本效益分析相比，多标准决策分析的使用如何影响战略的优先次序。 这项研究将提供一个可推广的框架和工具，以获得证据的因素，影响成本效益的使用基因组检测在“真实的世界”的社区实践。我们的研究结果将为未来的经济分析提供重要的数据和方法基础，这些分析将NIH支持的研究转化为实践，并优先考虑使用个性化药物来更好地预防，筛查，诊断和治疗癌症。

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.