Risk-Adjusting Hospital Outcomes for Veteran's Socioeconomic Status

根据退伍军人的社会经济地位调整医院结果的风险

• 批准号: 10162314
• 负责人: AMAL N. TRIVEDI
• 金额: --
• 依托单位: PROVIDENCE VA MEDICAL CENTER
• 依托单位国家: 美国
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-02-01 至 2020-09-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10162314
• 关键词: Accountability; Admission activity; Affordable Care Act; Age; Benchmarking; Calibration; Caring; Characteristics; Clinical; Collaborations; Consensus; Data; Diagnosis; Discrimination; Electronic Health Record; Enrollment; Ethnic Origin; Goals; Health Information System; Health Policy; Healthcare Systems; Heart Rate; Heart failure; Homelessness; Hospital Mortality; Hospitals; Incentives; Income; Learning; Measurement; Measures; Medicaid; Medical center; Medicare; Methods; Modeling; Mortality Determinants; Neighborhoods; Nursing Homes; Observational Study; Outcome; Outcome Measure; Patients; Performance; Pneumonia; Predictive Factor; Private Sector; Provider; Race; Records; Reporting; Research; Research Priority; Resource Allocation; Risk; Risk Adjustment; Role; Science; Site; Socioeconomic Status; Source; Statistical Models; Testing; United States; Variant; Veterans; base; care outcomes; clinical risk; deprivation; evidence base; expectation; high risk population; hospital performance; improved; innovation; inpatient service; mortality; mortality risk; novel; novel strategies; operation; patient population; payment; performance tests; policy implication; predictive modeling; programs; residence; sex; sociodemographic factors; sociodemographic variables; sociodemographics; tool

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. .

退伍军人护理的预期影响：该项目将开发和测试新的风险方法 将退伍军人的社会人口因素纳入 VAMC 级医院评估的调整 死亡。这一贡献意义重大，因为严格的结果衡量是 VA 战略的核心 改善对退伍军人的护理，评估各个站点的质量，并对私营部门的绩效进行基准测试。如果 风险调整未能考虑到死亡率的社会人口决定因素，而已知这些因素在不同国家之间存在差异 VA 提供商，然后排除这些因素可能会惩罚过度为弱势群体提供服务的 VA 网站 患者并对 VA 护理质量产生错误的推论。由于 VA 使用性能结果 为了实现问责目的并决定资源分配，VA 必须使用 可用的最稳健的风险调整方法。鉴于考虑社会人口统计学的新兴势头 出于风险调整目的，迫切需要开发经验证据基础 关于此类调整的影响。 项目背景：美国几乎所有医院，包括所有 VA 医疗中心 (VAMC)， 报告住院患者的死亡率，并且这些结果指标的表现通常很高 赌注。医院死亡率构成战略分析信息和学习 (SAIL) 的两个领域 VA 用于评估所有 VAMC 提供的护理质量和效率的模型。有效医院 结果测量必须充分考虑临床风险的差异。如果没有充分的风险调整， 绩效报告可能会错误地惩罚为高风险人群提供服务的设施，或者更糟糕的是， 激励机构接纳低风险患者。先前对风险调整的关注大多涉及 数据源、适当协变量的选择或统计建模的最佳方法。 很少有研究考察社会经济地位和其他社会人口统计学的作用 风险调整的因素，尽管这些因素预测出院后结果会更差，并且差异显着 跨设施。 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特征纳入医院评估的方法 死亡。我们的目标是：(1) 描述 VAMC 社会人口特征的变化 退伍军人因心力衰竭和肺炎住院； (2)评估风险调整模型的表现 包括或不包括社会人口特征； (3) 评估合并的影响 关于 VA 医疗中心相对绩效的社会人口统计数据。 项目方法：我们提出一项回顾性观察研究，该研究将开发和比较替代方案 风险调整模型预测因心力衰竭和肺炎入院三十天内的死亡率。我们 然后将测试包含和不包含社会人口特征的模型的性能， 评估社会人口统计特征对 VAMC 级医院死亡率分析的影响 心力衰竭和肺炎的发生率。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 VAMC 各不相同。目标 2 将比较现有的基于声明的 VA/CMS 心力衰竭和肺炎风险调整死亡率模型，模型包含索赔 以及新颖的社会人口统计数据；并确定社会人口特征对 死亡率模型，包括基于索赔的诊断和源自 VA 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 当死亡风险调整模型纳入社会人口因素时，医疗中心就会发生变化。 。

项目成果

Dual Use and Hospital Admissions among Veterans Enrolled in the VA's Homeless Patient Aligned Care Team.

加入退伍军人管理局无家可归患者协调护理团队的退伍军人的双重用途和入院。

• 发表时间: 2018
• 影响因子: 3.4
• 作者: Trivedi, Amal N;Jiang, Lan;Johnson, Erin E;Lima, Julie C;Flores, Michael;O'Toole, Thomas P
• 通讯作者: O'Toole, Thomas P

Racial/Ethnic Differences in 30-Day Mortality for Heart Failure and Pneumonia in the Veterans Health Administration Using Claims-based, Clinical, and Social Risk-adjustment Variables.

使用基于索赔、临床和社会风险调整变量的退伍军人健康管理局心力衰竭和肺炎 30 天死亡率的种族/民族差异。

• 发表时间: 2021-12-01
• 影响因子: 3
• 作者: Silva, Gabriella C;Jiang, Lan;Gutman, Roee;Wu, Wen;Mor, Vincent;Fine, Michael J;Kressin, Nancy R;Trivedi, Amal N
• 通讯作者: Trivedi, Amal N

Mortality Trends for Veterans Hospitalized With Heart Failure and Pneumonia Using Claims-Based vs Clinical Risk-Adjustment Variables.

使用基于索赔与临床风险调整变量的因心力衰竭和肺炎住院的退伍军人的死亡率趋势。

• 发表时间: 2020
• 影响因子: 39
• 作者: Silva, Gabriella C;Jiang, Lan;Gutman, Roee;Wu, Wen;Mor, Vincent;Fine, Michael J;Kressin, Nancy R;Trivedi, Amal N
• 通讯作者: Trivedi, Amal N