Creating needs-based tiered models for assisted living reimbursement

This research provides state policy makers and others interested in developing needs-based reimbursement models for Medicaid-funded assisted living with an evaluation of different methodologies that affect the structure and outcomes of these models.

We used assessment data from Medicaid-enrolled assisted living residents and waiver-eligible community-dwelling individuals (N = 726) in order to evaluate five methodologies in the design of these tiered needs-based models. We used ordinary least squares regression analyses in order to evaluate each model's ability to predict the time needed to care for individuals with varying needs (e.g., activities of daily living limitations, dementia, special services.)

These models varied in fit from .127 to a high of .357 using the adjusted R2 statistic. Both count and weighted models adequately predicted service needs and discriminated individuals into their appropriate tiers well. Weighted models with the largest score range worked best and provided more flexibility.

Policy makers can tailor the generic tiered models developed with these methods to a state's population. Any state considering adoption of a needs-based tiered model will need to refine its model based on its assisted living population characteristics, its resources, and how the model fits its long-term care system. For the industry, these models can serve to identify levels of care needed in planning for staff time and skill mix required for assisted living as well as other long-term care populations.