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

风险调整算法中的种族偏见以及对种族健康差异的影响：来自纽约双重资格医疗保险/医疗补助长期护理患者的证据

• 批准号: 10474727
• 负责人: Ajin Lee
• 金额: $ 41.67万
• 依托单位: STANFORD UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-06-01 至 2026-05-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10474727
• 关键词: Acute; Affect; Age; Algorithms; Area; Authorization documentation; Automobile Driving; Behavior; Black race; Caring; Characteristics; Chronic; Classification; Code; Contracts; County; Cream; Data; Data Set; Drug Prescriptions; Drug usage; Enrollment; Ethnic Origin; Ethnic group; Gender; Government; Health; Health Care Costs; Health Insurance; Health Status; Healthcare; Incentives; Income; Inpatients; Insurance Carriers; Knowledge; Lead; Learning; Link; Literature; Long-Term Care; Managed Care; Managed Care Programs; Mediation; Medicaid; Medicare; Medicare/Medicaid; Modeling; New York; Nursing Homes; Outcome; Patient Care; Patient-Focused Outcomes; Patients; Plant Roots; Privatization; Provider; Quality of Care; Race; Randomized; Records; Research; Resources; Risk; Risk Adjustment; Role; Service provision; Socioeconomic Factors; Subgroup; System; Systematic Bias; Testing; base; beneficiary; black patient; care outcomes; care systems; comorbidity; cost; design; dual eligible; financial incentive; health care disparity; health care service; health care service organization; health care service utilization; hospitalization rates; insurance plan; mortality; patient home care; patient subsets; payment; predictive modeling; programs; public health insurance; racial and ethnic; racial bias; racial difference; racial disparity; racial health disparity; sex

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.

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。在医疗保健中，种族 偏差可能是由于系统的分类和编码，数据可用性或数据准确性而引起的偏见。 各个种族群体不同。例如，使用有关医疗保健成本数据的算法（而不是疾病） 预测需求倾向于为我们当前系统服务不足的黑人患者分配太少的资源 比具有相同健康状况的白人患者产生的支出较低。这个问题越来越多 相关是因为大多数美国公共卫生保险计划都经营着主管的托管护理系统， 哪些受益人招募了私人保险计划，政府支付了确保每月固定的 每个注册人的资本付款。这些人均付款通常是使用风险调整来计算的 算法，其中预测患者成本，并提供有关年龄，性别和选定健康状况的信息 从过去注册的数据中。但是，种族通常被排除在这些算法之外，从而提出了这样的可能性 经过风险调整的托管护理可能会扩大患者的护理和结果的种族分布。但是有 关于风险调整的托管护理系统对种族差异的护理的影响的经验证据很少 和健康成果，尤其是在高成本环境中，例如长期护理。这个项目将进步 通过研究风险调整后的长期护理（MLTC）对这些问题的了解 双重资格的Medicare/Medicaid长期护理受益人之间在护理和成果的种族分布 纽约，使用有关医疗补助和医疗保险注册的8年行政数据，索赔和评估 记录。该项目将确定经过风险调整的MLTC对一系列护理利用和健康的影响 结局，包括住院，急性后，护士住所以及在家护理，处方药的使用以及 死亡率，分别由患者种族/种族。利用县县推出的托管护理 授权，该分析将使用差异差异模型来比较县内的变化 实施了纽约的患者的结果。我们将估计 通过患者的种族/民族种族/民族的单独模型，测试MLTC效应的统计差异。项目 还将通过风险调整算法中最严重影响种族偏见的亚组 亚组分析可以比较性别，年龄，存在慢性条件和邮政编码水平的效果 中位收入。该项目将进一步研究托管护理计划在推动种族中的作用 医疗保健利用和健康成果的差异。结果将有助于决策者，医疗保健 组织，提供者和患者了解偏见对风险调整算法的影响 患者健康，确定受影响最严重的患者的亚组，并了解有效计划 可以在托管护理环境中遏制或消除拉面健康差异的功能。