Risk-Adjusting Hospital Outcomes for Veteran's Socioeconomic Status

退伍军人护理的预期影响：该项目将开发和测试应对风险的新方法- 将退伍军人社会人口学因素纳入VAMC级医院评估的调整 死亡率。这一贡献意义重大，因为严格的结果衡量是退伍军人管理局战略的核心 改善对退伍军人的护理，评估各个地点的质量，并为私营部门制定绩效基准。如果 风险调整没有考虑到社会人口学决定死亡率的因素，这些因素在不同地区存在差异 退伍军人管理局提供商，那么排除这些因素可能会惩罚不成比例地为弱势群体提供服务的退伍军人管理局网站 并对退伍军人管理局的护理质量做出不正确的推断。由于VA将性能结果用于 为了问责目的并就资源分配作出决定，退伍军人管理局必须使用 现有最可靠的风险调整方法。考虑到考虑社会人口统计的新势头 为了风险调整的目的，迫切需要建立一个经验证据基础 关于这种调整的影响。 项目背景：几乎美国所有的医院，包括所有退伍军人医疗中心(VAMCs)， 报告住院患者的死亡率，在这些结果指标上的表现通常很高 赌注。医院死亡率构成战略分析信息和学习(SAIL)的两个领域 退伍军人管理局用来评估所有VAMC提供的护理质量和效率的模型。有效医院 结果衡量标准必须充分考虑临床风险的差异。如果没有足够的风险调整， 绩效报告可能会错误地惩罚为高危人群服务的设施，或者更糟糕的是， 鼓励医疗机构接纳低风险患者。之前对风险调整的大部分担忧都涉及到 数据来源、适当协变量的选择或统计建模的最佳方法。 更少的研究考察了社会经济地位和其他社会人口的作用 风险调整中的因素，尽管这些因素预测较差的出院后结果，并有显著差异 在各个设施之间。 项目目标：该项目的总体目标是开发和测试新的风险调整 将退伍军人的社会人口学特征纳入医院评估的方法 死亡率。我们的目标是：(1)描述社会人口学特征的VAMC水平的变化 因心力衰竭和肺炎住院的退伍军人；(2)评估风险调整模型的表现 包括和不包括社会人口学特征；以及(3)评估纳入 退伍军人医疗中心相对绩效的社会人口统计数据。 项目方法：我们提出了一项回溯性、观察性研究，以开发和比较替代方案 风险调整模型预测心力衰竭和肺炎入院后30天内的死亡率。我们 然后将测试包含和不包含社会人口统计特征的模型的性能 评估纳入社会人口学特征对VAMC级别医院死亡率的影响 心力衰竭和肺炎的比率。目标1将评估退伍军人的社会人口特征 因心力衰竭和肺炎入院的患者在VAMC中的情况各不相同。目标2将比较现有的基于索赔的 VA/CMS心力衰竭和肺炎风险调整死亡率模型 和新的社会人口数据；并确定社会人口特征对 死亡率模型，既包括基于索赔的诊断，也包括从VA得出的临床协变量 电子健康记录。目标3通过确定VA的相对质量排名是否扩展了这些分析 当社会人口因素被纳入死亡风险调整模型时，医疗中心就会发生变化。 。

Anticipated Impacts of Veterans' Care: The project will develop and test novel approaches to risk- adjustment that include Veterans' sociodemographic factors into assessments of VAMC-level hospital mortality. This contribution is significant because rigorous outcomes measurement is central to VA's strategy to improve care for Veterans, assess quality across sites, and benchmark performance to the private-sector. If risk-adjustment fails to account for sociodemographic determinants of mortality that are known to vary across VA providers, then excluding these factors may penalize VA sites that disproportionately serve vulnerable patients and generate incorrect inferences about the quality of VA care. Since VA uses performance results for accountability purposes and to make determinations about allocation of resources, it is essential that VA uses the most robust risk-adjustment methods available. Given emerging momentum to consider sociodemographic characteristics for risk-adjustment purposes, there is a pressing need to develop an empirical evidence base about the implications of such adjustments. Project Background: Nearly all hospitals in the United States, including all VA Medical Centers (VAMCs), report mortality rates for hospitalized patients, and performance on these outcomes measures often carry high stakes. Hospital mortality constitutes two domains of the Strategic Analytic Information and Learning (SAIL) model that VA employs to evaluate the quality and efficiency of care provided across all VAMCs. Valid hospital outcome measures must adequately account for differences in clinical risk. Without adequate risk-adjustment, performance reports may erroneously penalize facilities that serve high-risk populations, or, even worse, incentivize facilities to admit low-risk patients. Much of the prior concern with risk-adjustment has involved the source of the data, the selection of appropriate covariates, or the optimal approach to statistical modeling. Substantially fewer studies have examined the role of socioeconomic status and other sociodemographic factors in risk-adjustment, though these factors predict worse post-discharge outcomes and vary markedly across facilities. Project Objective: The overarching goal of this project is to develop and test novel risk-adjustment approaches that incorporate Veterans' sociodemographic characteristics into assessments of hospital mortality. Our aims are: (1) describe VAMC-level variations in the sociodemographic characteristics of Veterans hospitalized with heart failure and pneumonia; (2) assess the performance of risk-adjustment models that do and do not include sociodemographic characteristics; and (3) evaluate the impact of incorporating sociodemographic data on the relative performance of VA Medical Centers. Project Methods: We propose a retrospective, observational study that will develop and compare alternative risk-adjustment models predicting mortality within thirty days of admission for heart failure and pneumonia. We will then test the performance of models that do and do not incorporate sociodemographic characteristics and assess the impact of including sociodemographic characteristics on profiling VAMC-level hospital mortality rates for heart failure and pneumonia. Aim 1 will assess how the sociodemographic characteristics of Veterans admitted with heart failure and pneumonia vary across VAMCs. Aim 2 will compare the existing claims-based VA/CMS risk-adjusted mortality models for heart failure and pneumonia with models that incorporate claims and novel sociodemographic data; and determine the contribution of sociodemographic characteristics to mortality models that include both claims-based diagnoses and clinical covariates derived from the VA's electronic health record. Aim 3 extends these analyses by determining whether relative quality rankings of VA medical centers change when sociodemographic factors are included in mortality risk-adjustment models. .