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

比较 VA 提供的护理和 VA 购买的护理之间的护理质量：需要更好的风险调整

• 批准号: 10536566
• 负责人: AMY K ROSEN
• 金额: --
• 依托单位: VA BOSTON HEALTH CARE SYSTEM
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-11-01 至 2026-04-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10536566
• 关键词: Address; Affect; Area; Calibration; Caring; Categories; Code; Collaborations; Communities; Community Healthcare; Community Pharmacy; Data; Data Set; Data Sources; Databases; Decision Making; Discrimination; Disparity; Emergency department visit; Ensure; Equity; Evaluation; Future; Goals; Health; Health Services Accessibility; Healthcare; Hip region structure; Improve Access; Individual; Measures; Medicare; Methodology; Methods; Modeling; Monitor; Odds Ratio; Outcome; Outsourcing; Patients; Performance; Pharmacy facility; Policies; Policy Maker; Population; Private Sector; Provider; Quality of Care; Research; Resources; Risk; Risk Adjustment; Services; Subgroup; System; Testing; Veterans; burden of illness; care providers; care systems; community-level factor; comorbidity; cost; design; health care availability; health disparity; health equity; high risk; hospital readmission; housing instability; improved; innovation; knee replacement arthroplasty; mortality; outcome prediction; programs; severe mental illness; shared decision making; social; social health determinants; tool; tool development; web site

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.

背景：2018年的《任务法》进一步扩大了退伍军人接受护理的机会 社区。尽管这可能导致了改善的护理机会，但也可能导致下降 退伍军人获得的护理质量。 意义：这项研究将是第一个改善VA使用的现有风险调整方法的研究 VA/Community Care（CC）质量比较的申请。随着资深注册人数的越来越多 CC，迫切需要知道VA购买的社区中提供的护理质量是否是 至少等于在VA中提供的护理质量。我们还将检查是否增加社交 确定健康（SDOH）的风险调整方法会影响质量和健康的评估 差异。 创新和影响：更好地理解哪些新数据源和SDOH变量有所改善 需要采用风险调整方法，以使两者的VA/CC质量更加准确，更准确地比较 国家和地区一级。我们将获得有关条件和其他个人的丰富图片 - 以及影响退伍军人风险或疾病燃烧的社区级因素，还包括来自 多系统使用（Medicare和All-Pailer声称数据库）。 具体目的：对于2020 - 2022年联邦财政年度，我们的具体目的是：1）检查是否添加 随时可用的VA，CC和药房数据以及个人和社区级的SDOH变量改善了 Gagne的歧视和校准（易于修改的合并症测量值）； 2）检查是否 添加非VA系统的使用可以改善Gagne的歧视和校准； 3）总体比较 使用“ Gagne1”（包括其他VA和SDOH）在国家和地区的VA/CC质量 数据），“ gagne2”（包括非VA系统使用数据集）和NOSOS风险分数（“现成” VA 风险调整方法）。我们选择了两个资深亚组研究：严重精神疾病的退伍军人 （SMI）和退伍军人经历了总臀部或总膝关节置换术（THA/TKA）。这两个子组都在 外包CC的高成本大量类别。护理质量由4种健康结果定义： 紧急部门（ED）的访问和再入院，对有SMI的退伍军人，并发症和并发症和 tha/tka之后的再入院。 方法论：对于AIM 1，我们将研究Gagne合并症系数的程度 当其他数据源（CC和药房数据）添加到VA利用率数据时，然后 添加SDOH变量以预测结果。我们还将比较歧视和校准 Gagne，Gagne1和Nosos。对于AIM 2，我们将研究添加非VA系统使用数据的效果 gagne1并评估它们对gagne2的模型歧视和校准的影响。对于目标3，我们将 使用gagne1，gagne2和nosos比较国家和地方一级的整体VA/CC质量。 下一步/实施：通过与主要运营合作伙伴（社区办公室办公室）合作 护理[OCC]，分析和绩效集成[API]，卫生公平办公室[OHE]，访问办公室和 严重的精神疾病研究和评估中心[SMITREC]），我们将为VA领导者和 具有公平质量比较的政策制定者可以将其集成到工具的持续开发中 以及在全国范围内实施或用于帮助促进实践的举措 使用）和政策（与将其他资源分配给VA提供的护理或扩展有关的决策 使用基于VA的护理）。我们的发现还将使更多知情的员工，临床和资深人士共享 关于在哪里获得护理的决策。