Racial Bias in Risk Adjustment Algorithms and Implications for Racial Health Disparities: Evidence from Dual-Eligible Medicare/Medicaid Long-term Care Patients in New York

项目摘要/摘要 越来越多的证据表明，数据算法中存在种族偏见。在医疗保健方面，种族 由于分类和编码、数据可用性或数据准确性方面的系统性偏差，可能会产生偏差 在不同的种族群体中存在差异。例如，使用医疗成本数据而不是疾病数据的算法 预测需求倾向于将太少的资源分配给黑人患者，这些患者在我们当前的系统中没有得到充分的服务 并且产生的支出比同样健康状况的白人患者更低。这个问题日益严重。 相关是因为大多数美国公共医疗保险计划运行的是大写的管理型医疗系统，在 哪些受益人参加了私人保险计划，政府每月向保险公司支付固定的费用 每位参赛者的按人头数付费。这些人均支付通常是使用风险调整计算的。 算法，其中使用有关年龄、性别和选定健康状况的信息来预测患者成本 从过去参与者的数据中。然而，种族通常被排除在这些算法之外，这增加了这样一种可能性 经风险调整的管理式护理可能会扩大患者在护理和结果方面的种族差异。然而，有一种 很少有经验证据表明风险调整的管理型医疗系统对医疗保健中的种族差异的影响 以及健康结果，特别是在长期护理等高成本环境中。这项工程将会取得进展 通过研究风险调整管理性长期护理(MLTC)对以下问题的因果影响来了解这些问题 年符合双重资格的联邦医疗保险/医疗补助长期护理受益人的护理和结果的种族差异 纽约州，使用有关Medicaid和Medicare登记、索赔和评估的8年管理数据 唱片。该项目将确定经风险调整的MLTC对一系列护理利用和健康的影响 结果，包括住院、急性发作后、疗养院和居家护理、处方药使用和 死亡率，分别按患者种族/民族分列。利用逐县推出管理型医疗服务的优势 任务，分析将使用差异模型来比较县内的变化 管理医疗实施前后纽约患者的结果。我们会估计 按患者的种族/民族分开模型，检验MLTC效应的统计学差异。该项目 还将通过以下方式确定在风险调整算法中受种族偏见影响最严重的亚群 分组分析按性别、年龄、慢性病的存在和邮政编码水平比较影响 收入中位数。该项目还将研究管理保健计划功能在驾驶种族方面的作用 卫生保健利用和健康结果方面的差异。结果将帮助政策制定者，医疗保健 组织、提供者和患者了解风险调整算法中的偏差对 患者健康，确定受影响最严重的患者亚群，并了解有效的计划 可以遏制或消除管理医疗环境中的种族健康差异的功能。

PROJECT SUMMARY/ABSTRACT A growing body of evidence demonstrates the presence of racial bias in data algorithms. In healthcare, racial bias could arise due to systematic biases in classification and coding, data availability, or data accuracies that differ across racial groups. For instance, algorithms that use data on healthcare costs—rather than illness—to predict need tend to allocate too few resources to Black patients who are underserved by our current system and generate lower spending than white patients with the same health conditions. This issue is increasingly relevant because most U.S. public health insurance programs operate capitated managed care systems, in which beneficiaries enroll in private insurance plans, and the government pays insurers a fixed monthly capitation payment per enrollee. These per-capita payments are typically calculated using risk-adjustment algorithms, in which patient costs are predicted with information on age, gender, and selected health conditions from data on past enrollees. However, race is often excluded from these algorithms, raising the possibility that risk-adjusted managed care could widen racial disparities in care and outcomes among patients. Yet there is little empirical evidence on the impacts of risk-adjusted managed care systems on racial differences in care and health outcomes, especially in high-cost settings, such as long-term care. This project will advance knowledge on these issues by studying the causal effects of risk-adjusted managed long-term care (MLTC) on racial disparities in care and outcomes among dual-eligible Medicare/Medicaid long-term care beneficiaries in New York, using 8 years of administrative data on Medicaid and Medicare enrollment, claims, and assessment records. The project will identify the effects of risk-adjusted MLTC on a range of care utilization and health outcomes, including inpatient, post-acute, nursing home, and at-home care, prescription drug use, and mortality, separately by patient race/ethnicity. Leveraging the county-by-county rollout of managed care mandates, the analysis will use difference-in-differences models to compare within-county changes in outcomes of patients in New York from before to after managed care was implemented. We will estimate separate models by race/ethnicity of the patient, testing for statistical differences in MLTC effects. The project will also identify subgroups who are most severely affected by racial bias in risk-adjustment algorithms, through sub-group analyses that compare effects by gender, age, presence of chronic conditions, and zip code level median income. The project will additionally examine the role of managed care plan features in driving racial disparities in health care utilization and health outcomes. Results will help policymakers, healthcare organizations, providers, and patients to understand the implications of bias in risk-adjustment algorithms on patient health, identify subgroups of patients who are most severely impacted, and learn about effective plan features that could curb or eliminate racial health disparities in managed care settings.