Integrating Medicare and Medicaid coverage in managed care: effects on quality, utilization, and disparities

项目摘要/摘要 政策制定者正在采取战略，将双重资格人员的保险范围整合起来--个人拥有联邦医疗保险和 医疗补助-更好地协调这些计划的护理。符合双重条件的特殊需要计划(D-SNP)- 专门为双重保险提供服务的Medicare Advantage计划-已成为最大的计划类别 旨在作为整合的平台。然而，D-SNP在其与 医疗补助。虽然许多D-SNP只满足最低程度的整合(主要与护理有关 协调)，越来越多的计划通过覆盖联邦医疗保险和医疗补助实现了更大程度的整合 为同样的病人花钱。CMS继续完善D-SNP和政策制定者的整合标准 呼吁设计这些标准来提高双胞胎护理的质量、效率和公平性-a 考虑到这一人群的医疗和社会脆弱性，这是优先事项。然而，几乎没有证据表明 D-SNP之间的差异，包括影响整合的计划属性和国家政策，与哪些相关 推动支出的质量、护理模式或差异。此外，没有证据表明政策是如何发挥杠杆作用的 扩大综合D-SNP的注册人数影响护理。其中一个杠杆是默认注册--一种机制 这允许特定的医疗补助管理保健计划自动将医疗补助受益人登记到 当D-SNPs具有双重资格时(例如，在65岁时)。尽管只有一些D-SNP被允许使用 默认注册，有兴趣扩大这一机制。为了向政策提供信息，该项目将提供新的 D-SNPs表现差异的证据，包括影响这些计划水平的因素 医疗补助整合(目标1)；评估计划执行情况和弱势二人组的差距， 包括黑人和西班牙裔双胞胎、残疾双胞胎和有行为健康问题的双胞胎(目标2)； 并调查使用默认投保来整合覆盖范围的效果，利用 允许使用这一机制的计划和准试验性差异设计(目标3)。至 衡量与集成相关的计划绩效的关键方面，我们将使用Medicaid T-MSIS数据、Medicare 优势遭遇数据和护理设施评估数据在受益者一级联系(目标1-3)和 CAHPS患者体验调查(目标1-2)。这些数据的持续更新使我们能够监控 作为新D-SNP集成标准中CMS阶段的性能变化，使我们能够评估和 为不断演变的政策提供信息。该项目通过以下方式与AHRQ的优先事项和优先人口直接相关 关注D-SNPs的设计--一种针对复杂患者的新兴管理护理模式--如何影响质量， 效率和护理的公平性。为了最大限度地发挥这个项目的影响，我们将与一个政策咨询委员会合作 来自政府、研究和保险部门的整合政策专家，建立在团队的 将卫生服务研究转化为政策信息的跟踪记录。

PROJECT SUMMARY / ABSTRACT Policymakers are pursuing strategies to integrate coverage for dual-eligibles—individuals with Medicare and Medicaid—to better coordinate care across these programs. Dual-Eligible Special Needs Plans (D-SNPs)— Medicare Advantage plans that exclusively serve duals—have emerged as the largest category of plans intended to serve as a platform for integration. However, D-SNPs vary in their level of integration with Medicaid. While many D-SNPs meet only a minimum level of integration (primarily related to care coordination), a growing number of plans have attained greater integration by covering Medicare and Medicaid spending for the same patients. CMS continues to refine integration standards for D-SNPs and policymakers have called for designing these standards to improve the quality, efficiency, and equity of care for duals—a priority given this population’s medical and social vulnerability. Yet little evidence exists about the extent to which differences among D-SNPs, including plan attributes and state policies affecting integration, are linked to quality, care patterns that drive spending, or disparities. Further, there is no evidence about how policy levers to expand enrollment in integrated D-SNPs affect care. One such lever is default enrollment—a mechanism that allows certain Medicaid managed care plans to automatically enroll Medicaid beneficiaries into integrated D-SNPs when they become dual eligibles (e.g., at age 65). Although only some D-SNPs are permitted to use default enrollment, there is interest in expanding this mechanism. To inform policy, this project will provide new evidence about variations in the performance of D-SNPs, including by factors affecting these plans’ level of Medicaid integration (Aim 1); assess plan performance and disparities in vulnerable subgroups of duals, including Black and Hispanic duals, duals with a disability, and those with behavioral health conditions (Aim 2); and investigate the effects of using default enrollment to integrate coverage, leveraging variation between plans permitted to use this mechanism and a quasi-experimental difference-in-differences design (Aim 3). To measure key aspects of plan performance relevant to integration, we will use Medicaid T-MSIS data, Medicare Advantage encounter data, and nursing facility assessment data linked at the beneficiary level (Aims 1-3) and CAHPS patient experience surveys (Aims 1-2). Continuous updating of these data allows us to monitor changes in performance as CMS phases in new D-SNP integration standards, enabling us to evaluate and inform evolving policy. This project is directly related to AHRQ priorities and priority populations through its focus on how the design of D-SNPs—an emerging managed care model for complex patients—affects quality, efficiency, and equity of care. To maximize this project’s impact, we will work with a Policy Advisory Committee of experts in integration policy from the government, research, and insurance sectors, building on the team’s track record of translating health services research to inform policy.