NIH grant: Evaluating the Contribution of Care Pathways and Clinical Features To Home Health Quality Outcomes for People with Dementia (1F31AG099736-01)

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NIH grant 1F31AG099736-01: Evaluating the Contribution of Care Pathways and Clinical Features To Home Health Quality Outcomes for People with Dementia
Institute: {'code': 'AG', 'abbreviation': 'NIA', 'name': 'National Institute on Aging', 'admin_org_id': '12500002', 'admin_funding_url': ''}. PI: Maxwell  Cutty. Award: $45938.
Start: 2027-03-02T00:00:00.
PROJECT SUMMARY/ABSTRACT
The medically necessary, home-based skilled healthcare services offered under Medicare’s Home Health (HH)
benefit provide essential support to people with dementia. However, recent changes in Medicare reimbursement,
specifically the Patient-Driven Groupings Model (PDGM) in 2020, have raised concerns regarding reduced
access and quality of HH care for people with dementia. Previous research on these impacts primarily relies on
generalized billing data, which, unlike clinical assessment data, lack the necessary patient details—such as
functional status, cognitive deficits, or the presence of neuropsychiatric symptoms like apathy, depression, and
aggression—to account for the known heterogeneity in this population. Instead, these studies group people with
dementia into a single homogeneous category. However, these clinical features likely drive variation in care
needs and utilization. The fact that these features do not appear in most health services research (HSR) on
people with dementia underscores a critical knowledge gap. Until more granular HSR evidence is generated,
variations in clinical presentation that may be closely linked to HH care utilization pathways (care pathways) and
quality outcomes will remain unaccounted for; HH policy and practice improvements could suffer as a result.
This study addresses that gap by using latent class analysis (LCA) to identify care pathways of people with
dementia who used HH in 2021—2022 and then evaluating how care pathways relate to quality outcome
measures (e.g., potentially preventable hospitalizations, successful discharge to the community) (Aim 1) and
clinical features (dementia severity, neuropsychiatric symptom burden) (Aim 2) using multivariable and
multinomial logistic regression respectively. LCA will identify care pathways based on HH utilization metrics,
including types of services received (e.g., nursing, physical therapy, occupational therapy, speech therapy,
medical social work, and home health aide), visit volumes, and care duration. Findings will enhance knowledge
of how HH policy and practice interact to impact HH quality outcomes, providing evidence to guide policy
adjustments and care practices that can improve quality of life for this growing population and their caregivers.
These research aims will also support my fellowship training objectives. These objectives include expanding
technical skills (by applying advanced HSR methodologies to large Medicare datasets), professional skills (by
enriching my understanding of home-based care policy and research and increasing my research autonomy
through structured training in ethics, team science, grantsmanship, and mentorship), as well as operational skills
(through clinically-informed activities that translate research findings into actionable policies and practices that
positively impact home-based care). My strong mentorship team, a robust and rigorous training environment,
unique collaboration and dissemination opportunities, and well-curated coursework provide me with the support
I need to quickly advance toward my long-term goal: to be an independent, well-funded health services
researcher whose work improves the effectiveness of home-based care for older adults with complex needs.

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