Estimating the Potential Medicare Savings from Comparative Effectiveness Research

描述(由申请人提供)：比较有效性研究(CER)旨在衡量替代医疗保健方法如何影响健康结果。CER有可能通过限制使用无效护理和不比成本较低的替代方案更有效的护理来减少支出。我们建议研究将CER应用于六个高度优先的医疗领域可能给医疗保险带来的好处。我们的具体目标是：1.确定CER中最有潜力提高向联邦医疗保险受益人提供医疗服务的效率和适当性的领域。2.估计可归因于在这些高度优先的临床领域使用竞争策略的(长期)增量支出。3.确定对每个高优先领域采用CER标准支持的临床战略将对医疗保险产生的支出影响，包括成本效益，并说明政策选择(例如参考定价、捆绑支付等)。为医疗保险实施这些发现。我们的分析主要基于1991年至2009年A部分和B部分以及2006至2009年D部分的完整联邦医疗保险索赔文件。用于癌症管理分析的数据(例如，局限性前列腺癌)将包括SEER-Medicare关联数据。我们将首先根据优先顺序进程选择要进行进一步研究的医疗领域，考虑每个潜在调查领域对医疗保险总支出的影响、增长潜力以及临床方法中的可变性或不确定性的证据。一个代表保健、健康保险和消费者观点的著名专家咨询委员会将为这一组成部分和该项目的其他几个方面提供密切指导。对于选择的每个高优先级医疗领域，我们将从Medicare索赔文件(或SEER-Medicare文件)中确定相关队列，并根据收到的治疗或诊断干预措施为个人分配管理策略。除了描述性统计分析(每项战略的使用频率、重要成果的比率以及卫生总支出和相关支出)外，该项目还将包括多变量统计分析，以调查战略、成果(包括支出)和关键变量之间的关系。在可能的情况下，我们将使用工具变量分析和相关技术来控制选择管理策略时的偏差。将对重要的亚组进行单独的模型估计。我们将通过构建人口模型来评估在每个医疗领域实施我们的发现对联邦医疗保险的潜在后果，该模型将患者转移到我们的发现支持的策略(成本最低，结果相同或额外成本等于基于成本效益分析的增量收益)(采用不同程度的采用)。最后，我们将调查联邦医疗保险可能用来实施这些发现的方法，包括支付改革选项，如捆绑支付、参考定价和基于价值的共同支付。

DESCRIPTION (provided by applicant): Comparative effectiveness research (CER) is intended to measure how alternative approaches to health care affect health outcomes. CER has the potential to reduce expenditures by limiting the use of both ineffective care and care that is no more effective than less costly alternatives. We propose to study the benefits that might accrue to Medicare from the application of CER to six high- priority medical areas. Our specific aims are to: 1. Identify areas of CER that have the greatest potential to improve the efficiency and appropriateness of care delivered to Medicare beneficiaries. 2. Estimate (long-term) incremental expenditures attributable to the use of competing strategies in these high-priority clinical areas. 3. Determine the expenditure implications for Medicare that would result from the adoption of clinical strategies supported by CER criteria, including cost-effectiveness, for each high-priority area, and to describe policy options (e.g. reference pricing, bundled payments, etc.) for implementing these findings for Medicare. Our analyses are principally based on complete Medicare claims files for Parts A and B from years 1991 to 2009 and Part D from 2006 to 2009. Data for the analysis of cancer management (e.g., localized prostate cancer), will include SEER-Medicare linked data. We will first select the medical areas for further research based upon a prioritization process considering, for each potential area of investigation, effects on total Medicare expenditures, potential for growth, and evidence of variability or uncertainty in clinical approaches. A distinguished expert Advisory Committee, representing health care, health insurance, and consumer perspectives, will provide close guidance for this component and several other aspects of the project. For each high-priority medical area selected, we will identify the relevant cohort from the Medicare claims files (or SEER-Medicare files) and assign individuals to management strategies based upon treatment or diagnostic interventions received. In addition to descriptive statistical analyses (frequency of the use of each strategy, rates of important outcomes, and total and related health expenditures), the project will include multivariate statistical analysis to investigate the relationship between strategy, outcome (including expenditures) and key variables. Where possible, we will use instrumental variable analysis and related techniques to control for bias in the selection of management strategies. Separate models will be estimated for important subgroups. We will estimate the potential consequences to Medicare of implementing our findings for each medical area by constructing a population model that shifts (with scenarios of varying degrees of adoption) patients to the strategy(ies) supported by our findings (least costly with equivalent outcomes or additional cost worth the incremental benefit based on cost-effectiveness analysis). Finally, we will investigate approaches that Medicare might use to implement these findings, including payment reform options such as bundled payments, reference pricing and value-based copayments.