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

DRMS 中的 DDRIG：算法决策对长期护理不平等的影响

• 批准号: 2314890
• 负责人: Emily Rauscher
• 金额: $ 3.19万
• 依托单位: Brown University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-06-15 至 2025-05-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2314890&HistoricalAwards=false
• 关键词: DDRIG; DRMS; implications; algorithmic; decision

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.

在美国，各州越来越多地使用算法来确定老年人和残疾人是否有资格获得医疗补助计划资助的长期医疗保健。与更普遍的医疗补助不同，长期护理的资格除了基于收入和资产之外，还基于功能能力和残疾状况。以前，各州可能完全依靠医生来决定长期护理申请人是否符合功能条件，但现在许多州使用算法来做出这一决定。虽然算法通常被认为比人类更少偏见，但研究表明，算法往往会增加不平等，尤其是人口类别之间的不平等。然而，算法决策对不平等的人口影响尚不清楚。医疗补助长期护理是解决这一证据差距的一个重要案例；医疗补助计划是数百万美国人，特别是边缘群体成员的重要医疗保健来源。此外，随着未来几十年老年人口的增长，需要长期护理的人数可能会增加，这使得这个问题显得尤为及时。这项研究揭示了算法决策，以及算法的哪些特征，是如何更好或更差地获得长期护理的公平。本文使用混合方法来回答三个主要研究问题：1)州医疗补助计划如何以及为什么在长期护理资格确定中使用算法？2)医疗补助计划的官员如何使用算法进行长期护理资格评估？3)算法实现与长期护理可及性人口类别不平等之间的关系是什么？通过将医疗补助索赔数据与各州使用的算法的原始数据联系起来，该项目估计了算法实施与长期护理不平等之间的关系（RQ3）。对三个州的医疗补助管理人员的深入访谈阐明了这种关系的机制（RQ1和RQ2）。结果有助于确定组织层面的健康社会决定因素，这些决定因素可能被改变以提高公平性。此外，为回答RQ3而收集的主要数据将公开，以支持未来对长期护理算法的研究，并使医疗补助长期护理受益人更好地了解他们的福利是如何确定的。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。