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: 10187736
• 负责人: Daniel E Hall
• 金额: --
• 依托单位: SOUTH TEXAS VETERANS HEALTH CARE SYSTEM
• 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/10187736
• 关键词: Acute; Address; Administrator; Affect; American; Area; Assessment tool; Benchmarking; Caring; Communities; Community Surveys; Data; Data Collection; Data Set; Data Sources; Disadvantaged; Distress; Economically Deprived Population; Education; Educational process of instructing; Eligibility Determination; Ethnic Origin; Failure; Fees; Goals; Health; Health Services Accessibility; Health system; Healthcare; Hospitals; Individual; Informatics; Inpatients; Institutional Practice; Lead; Length of Stay; Machine Learning; Manuscripts; Measures; Medical; Medical Records; Methodology; Methods; Minority; Mission; Modeling; Operative Surgical Procedures; Outcome; Pathway interactions; Patients; Peer Group; Performance; Phase; Policies; Population; Postoperative Complications; Postoperative Period; Poverty; Procedures; Proxy; Race; Resource Allocation; Resources; Risk; Risk Adjustment; Risk Factors; Rural Health; Services; Social Identification; 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; base; burden of illness; care delivery; care fragmentation; care outcomes; comorbidity; cost; data warehouse; deprivation; design; diverse data; 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; military veteran; mortality; multiple data sources; neighborhood disadvantage; outcome prediction; patient stratification; payment; point of care; predictive modeling; programs; racial and ethnic; racial minority; residence; 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》改善了退伍军人获得护理的机会， 管理（VA）和社区系统。一个潜在的假设是，更快的护理与更多的选择， 从而得到更好的护理。然而，护理碎片化与住院时间延长、再入院、 and mortality.术后并发症和再入院率在少数民族和社会经济地位低的人群中较高 状态（SES）患者。低SES也与虚弱有关，这是30天内最好的预测因素之一。 术后并发症和再次入院。尽管对健康有着深远的影响 结果，VA质量指标的风险调整中不存在社会风险因素，进一步加剧了 少数民族和低社会经济地位人口的差距。这一战略可能会进一步限制资源，以照顾 弱势群体，因为许多退伍军人在经济上处于不利地位， 提供护理的费用。另一个主要问题是护理碎片化。然而，非VA护理的影响和 在性能度量中不存在护理碎片。我们的目标是确定社会风险因素和 影响手术结局的护理碎片化，以告知VA质量指标政策和机构 资源配置我们通过将手术结局数据与1）VA/中心结合来改进当前实践 对于Medicare & Medicaid Services（CMS）索赔数据，2）VA费用基础文件，用于识别 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：我们的意义是使用社会风险因素，农村，生活在 弱势社区和护理碎片化，以确定导致医疗保健差异的因素 并告知退伍军人事务部政策其影响是使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：农村卫生，卫生公平，卫生保健价值和卫生保健信息学。 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， 经济分析必须改变机构的做法，使我们最脆弱的退伍军人受益。 具体目标： 目的1：通过评估种族、人种、社会经济地位、地点的贡献，确定影响手术结局的因素 住院和护理碎片化对手术并发症、再入院和死亡率的影响 假设：使用少数民族/种族地位、社会经济地位、居住地和护理分散将确定 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碎片化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著包括社会风险因素和护理碎片化 改变VA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、护理碎片化、社会经济地位和少数民族地位与急性 和长期VA外科医疗保健利用，以告知VA资源分配 假设：低社会经济地位，农村，照顾碎片和少数民族地位与较高的VA资源相关 利用 方法：使用传统的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，以使用护理分割开发手术结果的预测模型， 农村和社会风险因素的风险调整的医疗合并症，并适用于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度量模型 VA系统内的碎片化。调整资源分配以考虑社会风险因素。