DDRIG in DRMS: The implications of algorithmic decision-making for inequality in long-term care

Brown University, Providence RI

Investigators

Abstract

States are increasingly using algorithms to determine eligibility for Medicaid-funded long-term healthcare for elderly people and people with disabilities in the United States. As opposed to Medicaid more generally, eligibility for long-term care is based on functional capacity and disability status in addition to income and assets. Previously, states may have relied solely on physicians to decide whether a long-term care applicant is functionally eligible, but many states now use algorithms to make this determination. Though algorithms are often purported to be less biased than humans, research has shown that algorithms often increase inequality, especially inequality between demographic categories. However, the population-level consequences of algorithmic decision-making for inequality are still unclear. Medicaid long-term care is an important case to address this evidence gap; Medicaid is a crucial source of healthcare for millions of Americans, especially for members of marginalized groups. Additionally, the number of people who require long-term care will likely increase as the elderly population grows over the next few decades, making this issue particularly timely. This research sheds light on how algorithmic decision-making, and which characteristics of algorithms, are better or worse for equity in access to long-term care. This dissertation uses mixed methods to answer three primary research questions: 1) How and why do state Medicaid programs use algorithms in long-term care eligibility determination? 2) How do Medicaid bureaucrats use algorithms to perform long-term care eligibility assessments? 3) What is the relationship between algorithm implementation and inequality between demographic categories in long-term care access? By linking Medicaid claims data with primary data on the algorithms used by each state, this project estimates how algorithm implementation is associated with inequality in long-term care (RQ3). In-depth interviews with Medicaid administrators in three states elucidate the mechanisms underlying this relationship (RQ1 and RQ2). Results help identify organization-level social determinants of health that can potentially be altered to improve equity. Further, the primary data collected to answer RQ3 will be made publicly available to enable future research on algorithms in long-term care and give Medicaid long-term care beneficiaries better insight into how their benefits are determined. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.