Impact of Medicare Polices on Beneficiaries with ADRD

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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.