Comparing Quality of Care between VA-Delivered and VA-Purchased Care: The Need for Better Risk Adjustment

背景资料：2018年使命法案进一步扩大了退伍军人接受护理的机会 社区。虽然这可能改善了获得护理的机会，但也可能导致 退伍军人接受的护理质量。 意义：这项研究将第一次改进退伍军人管理局使用的现有风险调整方法 退伍军人事务部/社区护理(CC)质量比较申请。随着越来越多的退伍军人使用 CC，迫切需要知道退伍军人管理局购买的社区提供的护理质量是否 至少相当于退伍军人管理局提供的护理质量。我们还将研究是否添加社交 健康决定因素(SDOH)对风险调整方法影响质量和健康的评估 差距。 创新和影响：更好地了解哪些新数据源和SDOH变量有所改进 需要风险调整方法，以便能够更公平和更准确地比较两者的VA/CC质量 国家和地方两级。我们将对条件和其他个人有更丰富的了解- 以及影响退伍军人风险或疾病负担的社区因素，也包括来自 多系统使用(医疗保险和所有付款人索赔数据库)。 具体目标：对于2020-2022年联邦财政年度，我们的具体目标是：1)审查是否增加 随时可用的VA、CC和药房数据以及个人和社区级别的SDOH变量改善了 Gagne的辨别和校准(一种易于修改的共病测量)；2)检查 增加非VA系统的使用改善了Gagne的辨别和校准；以及3)总体比较 使用“Gagne1”(包括额外的VA和SDOH)在国家和地方层面上的VA/CC质量 数据)、“Gagne2”(包括非退伍军人管理局系统使用的数据集)和NOOS风险分值(“现成”退伍军人事务部 风险调整方法)。我们选择了两个退伍军人小组进行研究：患有严重精神疾病的退伍军人 (SMI)和接受全髋关节或全膝关节置换术(THA/TKA)的退伍军人。这两个小组都在 高成本、大批量的外包CC品类。医疗质量由4个健康结果定义： 患有SMI的退伍军人的急诊科(ED)就诊和再入院，以及并发症和 THA/TKA后再入院。 方法：对于目标1，我们将检查加涅合并症的系数的程度 在将其他数据源(CC和药房数据)添加到VA利用率数据时更改，然后更改 增加了SDOH变量来预测结果。我们还将比较区分和校准之间的差异 Gagne、Gagne1和Nosos。对于目标2，我们将检查将非VA系统使用数据添加到 Gagne1，并评价它们对Gagne2的模型判别和校准的影响。对于目标3，我们将 使用Gagne1、Gagne2和NOOS比较国家和地方级别的整体VA/CC质量。 下一步/实施：通过与主要业务伙伴的协作(社区办公室 CARE[OCC]、分析和绩效集成[API]、医疗公平办公室[OHE]、访问办公室和 严重精神疾病研究和评估中心[SMITREC])，我们将为退伍军人管理局领导和 政策制定者对可整合到工具持续开发中的质量进行公平比较 以及正在全国范围内实施或用于帮助促进实践的计划(风险调整器 使用)和政策(与是否向退伍军人管理局提供的护理或扩展分配额外资源有关的决定 使用退伍军人管理局购买的护理)。我们的发现还将使更多知情的员工、临床医生和退伍军人能够分享 关于在哪里接受护理的决策。

Background: The MISSION Act of 2018 further expanded the opportunity for Veterans to receive care in the community. While this may have led to improved access to care, it may also have resulted in decreases in the quality of care that Veterans receive. Significance: This study will be the first to improve existing risk adjustment methods used by VA with an application for VA/Community Care (CC) quality comparisons. As increasing numbers of Veteran enrollees use CC, there is an urgent need to know if the quality of care delivered in the community that is purchased by VA is at least equivalent to the quality of care delivered in VA. We will also examine whether adding social determinants of health (SDOH) to the risk adjustment methods impacts assessments of quality and health disparities. Innovation and Impact: A better understanding of which new data sources and SDOH variables improve risk adjustment methods is needed to enable fairer and more accurate comparisons of VA/CC quality at both the national and local area levels. We will obtain a much richer picture of the conditions and other individual- and community-level factors that affect the risk or disease burden of Veterans by also including data from multi-system use (Medicare and All-Payer Claims Databases). Specific Aims: For federal fiscal years 2020-2022, our specific aims are to: 1) Examine whether adding readily available VA, CC, and pharmacy data and individual- and community-level SDOH variables improve the discrimination and calibration of Gagne (an easily modifiable comorbidity measure); 2) Examine whether adding non-VA system use improves the discrimination and calibration of Gagne; and 3) Compare overall VA/CC quality at the national and local area levels using “Gagne1” (which includes additional VA and SDOH data), “Gagne2” (which includes non-VA system use datasets), and the Nosos risk score (an “off-the-shelf” VA risk adjustment method). We selected two Veteran subgroups to study: Veterans with serious mental illness (SMI) and Veterans undergoing total hip or total knee arthroplasty (THA/TKA). Both of these subgroups are in high-cost, high-volume categories of outsourced CC. Quality of care is defined by 4 health outcomes: emergency department (ED) visits and readmissions for Veterans with SMI, and complications and readmissions after THA/TKA. Methodology: For Aim 1, we will examine the extent to which the coefficients on the Gagne comorbidities change when additional data sources (CC and pharmacy data) are added to VA utilization data and then when SDOH variables are added to predict outcomes. We will also compare discrimination and calibration between Gagne, Gagne1, and Nosos. For Aim 2, we will examine the effect of adding non-VA system use data to Gagne1 and evaluate their effect on model discrimination and calibration of Gagne2. For Aim 3, we will compare overall VA/CC quality at the national and local levels using Gagne1, Gagne2, and Nosos. Next Steps/Implementation: Through collaboration with key operational partners (the Office of Community Care [OCC], Analytics and Performance Integration [API], Office of Health Equity [OHE], Access Office, and Serious Mental Illness Research and Evaluation Center [SMITREC]), we will provide VA leaders and policymakers with equitable comparisons of quality that can be integrated into ongoing development of tools and initiatives that are being implemented nationwide or used to help facilitate practice (which risk adjuster to use) and policy (decisions related to whether to allocate additional resources to VA-provided care or expand use of VA-purchased care). Our findings will also enable more informed staff, clinician, and Veteran shared decision-making about where to receive care.