Understanding the effect of rurality and social risk factors on barriers to care and surgical outcomes.

了解农村和社会风险因素对护理和手术结果障碍的影响。

• 批准号: 10431846
• 负责人: Daniel E Hall
• 金额: --
• 依托单位: VETERANS HEALTH ADMINISTRATION
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-10-01 至 2025-03-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10431846
• 关键词: Acute; Address; Administrator; Affect; American; Area; Assessment tool; Benchmarking; Caring; Communities; Community Surveys; Data; Data Collection; Data Set; Data Sources; Disparity; Distress; Education; Educational process of instructing; Eligibility Determination; Ethnic Origin; Failure; Fees; Goals; Health; Health Services Accessibility; Health system; Healthcare; Hospitals; Individual; Informatics; Inpatients; Institution; Institutional Practice; Lead; Length of Stay; Machine Learning; Manuscripts; Measures; Medical; Medical Records; Methodology; Methods; Minority; Mission; Modeling; Operative Surgical Procedures; Outcome; Outcome Assessment; Pathway interactions; Patients; Peer Group; Performance; Perioperative; Persons; Phase; Policies; Population; Postoperative Complications; Postoperative Period; Poverty; Procedures; Proxy; Race; Resource Allocation; Resources; Risk; Risk Adjustment; Risk Factors; Rural Health; Services; Socioeconomic Status; Surgical Models; Surgical complication; System; Techniques; United States; United States Centers for Medicare and Medicaid Services; United States Department of Veterans Affairs; Veterans; Veterans Health Administration; Vulnerable Populations; barrier to care; burden of illness; care delivery; care fragmentation; care outcomes; comorbidity; cost; data warehouse; deprivation; design; diverse data; economic disparity; economic evaluation; frailty; health care disparity; health care service utilization; health equity; high risk; hospital performance; hospital readmission; improved; improved outcome; indexing; innovation; low socioeconomic status; machine learning method; machine learning model; military veteran; mortality; multiple data sources; neighborhood disadvantage; outcome prediction; patient stratification; payment; point of care; poor health outcome; predictive modeling; programs; racial minority; residence; risk mitigation; rural area; rurality; safety net; social; statistics; surgery outcome; surgical risk; trend

Background: The Mission Act provides improved Veteran access to care both within the Veterans Administration (VA) and community systems. An underlying assumption is that faster care with more choices results in better care. However, care fragmentation is associated with increased length of stay, readmissions, and mortality. Postoperative complications and readmissions are higher in minority and low socioeconomic status (SES) patients. Low SES is also associated with frailty, one of the best predictors of 30-day postoperative complications and hospital readmissions. Despite having a profound influence on health outcomes, social risk factors are absent from risk adjustment for VA quality measures, further exacerbating disparities in minority and low SES populations. This strategy may further constrain resources to care for vulnerable populations, as many Veterans are economically disadvantaged and potentially adding avoidable costs to care delivery. Another major issue is care fragmentation. Nevertheless, the impact of non-VA care and care fragmentation is absent in performance metrics. Our goal is to identify social risk factors and levels of care fragmentation that affect surgical outcomes to inform VA quality metric policy and institutional resource allocation. We improve upon current practice by joining surgical outcomes data with 1) VA/Centers for Medicare & Medicaid Services (CMS) claims data, 2) VA fee-basis files to identify encounters outside of the VA health system and 3) using more granular proxy social risk factors and neighborhood disadvantage. Significance/Impact: Our significance is modeling surgical outcomes using social risk factors, rurality, living in a disadvantaged neighborhood and care fragmentation to identify factors contributing to health care disparities and to inform VA policy. The impact is to develop quality metrics using social risk factors and care fragmentation. HSR&D priority areas: Rural Health, Health Equity, Health Care Value and Health Care Informatics. Innovation: Joining diverse data sources to develop predictive models using both traditional parametric methods and exploratory machine learning techniques to provide clinicians and administrators with outcomes and economic analyses necessary to change institutional practices to benefit our most vulnerable Veterans. Specific Aims: Aim 1: Identify factors affecting surgical outcomes by assessing the contributions of ethnicity, race, SES, place of residence and care fragmentation to surgical complications, readmissions and mortality Hypothesis: Using ethnic/racial minority status, SES, place of residence and care fragmentation will identify important risk factors for postoperative complications, readmissions, and mortality Aim 2: Assess the impact of social risk factors and care fragmentation on hospital performance metrics for readmissions and mortality Hypothesis: Including social risk factors and care fragmentation in risk adjustment models significantly changes VA hospital performance rankings with respect to readmissions and mortality Aim 3: Determine the relationship of place of residence, care fragmentation, SES and minority status to acute and long-term VA surgical health care utilization to inform VA resource allocation Hypothesis: Low SES, rurality, care fragmentation and minority status are associated with higher VA resource utilization Methodology: Quantitative analyses using traditional parametric and exploratory machine learning techniques performed on diverse datasets to develop predictive models of surgical outcomes using care fragmentation, rurality and social risk factors risk adjusted for medical comorbidities and applied to VA quality metrics. Implementation/Next Steps: Deployment of quality metric models using social risk factors and care fragmentation within the VA system. Adjusting resource allocation to account for social risk factors.

背景：《使命法》改善了退伍军人在退伍军人内部获得护理的机会 行政（VA）和社区系统。一个基本的假设是，有更多选择就能提供更快的护理 带来更好的护理。然而，护理碎片化与住院时间延长、再入院、 和死亡率。少数族裔和社会经济地位较低的人群术后并发症和再入院率较高 状态（SES）患者。低 SES 也与虚弱有关，这是 30 天健康状况的最佳预测因素之一 术后并发症和再入院。尽管对健康有着深远的影响 结果，VA 质量措施的风险调整中缺少社会风险因素，进一步加剧了 少数族裔和低社会经济地位人口的差异。这一策略可能会进一步限制护理资源 弱势群体，因为许多退伍军人在经济上处于不利地位，并可能增加可避免的 护理服务的费用。另一个主要问题是护理碎片化。尽管如此，非 VA 护理的影响和 绩效指标中不存在护理碎片。我们的目标是确定社会风险因素和水平 影响手术结果的护理碎片化，为 VA 质量指标政策和机构提供信息 资源分配。我们通过将手术结果数据与 1) VA/中心结合来改进当前的实践 用于 Medicare 和 Medicaid 服务 (CMS) 索赔数据，2) VA 费用基础文件，用于识别超出范围的遭遇 VA 卫生系统和 3) 使用更细粒度的代理社会风险因素和邻里劣势。 意义/影响：我们的意义是利用社会风险因素、农村、居住环境对手术结果进行建模 弱势社区和护理分散，以确定导致医疗保健差异的因素 并告知 VA 政策。其影响是利用社会风险因素和护理碎片化来制定质量指标。 HSR&D 优先领域：农村健康、健康公平、医疗保健价值和医疗保健信息学。 创新：结合不同的数据源，使用传统的参数方法开发预测模型 和探索性机器学习技术，为临床医生和管理人员提供结果和 改变制度实践以使我们最脆弱的退伍军人受益所必需的经济分析。 具体目标： 目标 1：通过评估民族、种族、社会经济地位、地点的贡献来确定影响手术结果的因素 居住和护理分散对手术并发症、再入院和死亡率的影响 假设：利用族裔/种族少数群体身份、社会经济地位、居住地和护理分散性将识别 术后并发症、再入院和死亡率的重要危险因素 目标 2：评估社会风险因素和护理分散对医院绩效指标的影响 再入院和死亡率 假设：在风险调整模型中显着包含社会风险因素和护理分散化 改变退伍军人管理局医院在再入院和死亡率方面的绩效排名 目标 3：确定居住地、护理分散、SES 和少数民族状况与急性发作的关系 以及长期 VA 外科医疗保健利用，为 VA 资源分配提供信息 假设：低社会经济地位、农村、护理分散和少数族裔身份与较高的退伍军人资源相关 利用率 方法：使用传统参数和探索性机器学习技术进行定量分析 在不同的数据集上进行，以使用护理碎片开发手术结果的预测模型， 农村和社会风险因素根据医疗合并症进行风险调整并应用于 VA 质量指标。 实施/后续步骤：使用社会风险因素和护理部署质量指标模型 VA 系统内部的碎片化。调整资源配置以考虑社会风险因素。