Impact of Medicare Polices on Beneficiaries with ADRD

摘要 患有阿尔茨海默病和相关痴呆症（ADRD）的医疗保险受益人经历了渐进式的 认知障碍，经常需要帮助来管理他们在家中的复杂护理需求。 医疗保险每年向家庭健康机构（HHA）支付数十亿美元，以提供家庭护理，包括 熟练的护理和康复治疗。由于担心HHA服务的质量和价值， 医疗保险有两个新的支付政策：a）切换到病例组合支付系统，患者驱动 分组模式（PDGM）在2020年;以及B）实施家庭健康机构基于价值的支付 2023年的HHVBP计划。然而，这两个方案都没有解决由于以下原因而导致的结果的潜在差异： 社会因素，如个人层面的种族/族裔或地区层面的社会资本，尽管过去的经验 新的医疗保险支付政策忽视了社会因素，加剧了贫富差距。的证据 新政策对HHA服务使用的影响有限，特别是在国家HHVBP实施的情况下 2023年开始任何评估都需要考虑到其他政策的同期变化， 关注流行病如何影响接受HHA服务的人或如何提供护理。从而 为了研究HHA服务对受益人结果的影响，我们提出了三个目标：1）描述 有资格获得HHA服务并登记的社区居住受益人人口的变化 传统的按服务收费的Medicare（TM），用于转换到Medicare Advantage（MA），过渡到 长期疗养院，以及在引入更大的财政支持后，新的ADRD诊断增加 ADRD编码的激励措施; 2）评估PDGM和HHVBP政策对TM中HHA使用的影响 与参加MA的类似受益人相比，ADRD受益人;以及，3）检查 HHA服务在临床事件上的使用，如住院、长期疗养院入院或死亡， 在患有ADRD的TM和MA受益人中。我们的三个目标是相辅相成的，并针对一个关键的服务 同时仔细检查发生的人口和交付变化 随着时间我们将使用多个数据资源来捕获更改;然后解决 数据，我们将使用集成机器学习方法结合纵向目标最大值 评估平均HHA治疗效应以及潜在效应的似然估计方法 在个人和地区层面衡量的社会因素的异质性。研究结果可以 告知政策制定者、临床医生、患者和家庭HHA服务的长期影响， 这种影响可能会因社会因素而有所不同。

Abstract Medicare beneficiaries with Alzheimer’s Disease and Related Dementias (ADRD) experience progressive cognitive impairment and frequently require assistance to manage their complex care needs at home. Medicare pays Home Health Agencies (HHAs) billions of dollars annually to provide home care, including skilled nursing and rehabilitation therapies. Because of concerns about the quality and value of HHA services, Medicare has two new payment policies: a) switch to a case-mix payment system, the Patient Driven Groupings Model (PDGM) in 2020; and b) implementation of the Home Health Agency Value Based Payment (HHVBP) program in 2023. Neither program, however, addresses potential differences in outcomes due to social factors, such as individual-level race/ethnicity or area-level social capital, despite past experiences where new Medicare payment policies that ignored social factors exacerbated disparities. Evidence on the impact of the new policies on HHA service use is limited, especially as the national HHVBP implementation starts in 2023. Any evaluation will require accounting for contemporaneous changes in other policies, and attention to how the pandemic is impacting who receives HHA services or how care is delivered. Thus, to examine the impact of HHA services on beneficiary outcomes, we propose three aims: 1) to characterize changes in the population of community-dwelling beneficiaries eligible for HHA services and enrolled in traditional, fee-for-service Medicare (TM), accounting for switches to Medicare Advantage (MA), transitions to long-term nursing homes, and increases in new ADRD diagnoses after the introduction of greater financial incentives for ADRD coding; 2) to evaluate the impact of PDGM and HHVBP policies on HHA use in TM beneficiaries with ADRD compared with similar beneficiaries enrolled in MA; and, 3) to examine the effect of HHA service use on clinical events, such as hospitalizations, long-term nursing home admission, or mortality, among TM and MA beneficiaries with ADRD. Our three aims are complementary and address a critical service for vulnerable beneficiaries with ADRD while carefully examining population and delivery changes occurring over time. We will use multiple data resources to capture the changes; then to address the complexity of the data, we will use ensemble machine learning approaches combined with longitudinal targeted maximum likelihood estimation methods to assess the average HHA treatment effect as well as potential effect heterogeneity with respect to social factors measured at the individual and area levels. Study findings could inform policy makers, clinicians, patients, and families about the impact of HHA services over time and potential variation in this impact with respect to social factors.