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：《任务法》为退伍军人提供了改进的退伍军人访问 管理（VA）和社区系统。一个基本的假设是更快的护理有更多选择 导致更好的护理。但是，护理分裂与住院时间增加，再入院时间增加有关 和死亡率。在少数民族和社会经济低的少数群体中，术后并发症和再入院率更高 状态（SES）患者。低SES也与脆弱有关，这是30天的最佳预测因素之一 术后并发症和医院再入院。尽管对健康有深远的影响 结果，与VA质量措施的风险调整不存在社会风险因素，进一步加剧了 少数民族和低人类种群的差异。该策略可能会进一步限制资源来照顾 弱势群体，因为许多退伍军人在经济上处于不利地位，并有可能增加 护理交付费用。另一个主要问题是护理分裂。然而，非VA护理和 性能指标缺乏护理分裂。我们的目标是确定社会风险因素和水平 护理分裂影响手术结果，以告知VA质量指标政策和机构 资源分配。我们通过将手术结果数据与1）VA/Centers一起加入手术结果数据来改善当前实践 有关Medicare＆Medicaid服务（CMS）索赔数据，2）VA费用文件，以识别以外的相遇 VA卫生系统和3）使用更多颗粒状的代理社会风险因素和社区劣势。 意义/影响：我们的意义是使用社会风险因素，农村，生活在外科手术结果中建模 处境不利的社区和护理分裂，以识别导致医疗保健差异的因素 并为VA政策提供信息。影响是使用社会风险因素和护理分裂来开发质量指标。 HSR＆D优先领域：农村健康，健康公平，卫生保健价值和医疗保健信息学。 创新：加入多种数据源以使用两种传统参数方法开发预测模型 以及探索机器学习技术，为临床医生和管理人员提供结果和 改变机构实践所需的经济分析，以使我们最脆弱的退伍军人受益。 具体目的： 目标1：通过评估种族，种族，SES，地点的贡献来确定影响手术结果的因素 居住和护理碎片对手术并发症，再选中和死亡率 假设：使用种族/种族少数群体地位，SES，居住地和护理分裂将确定 术后并发症，再选中和死亡率的重要风险因素 目标2：评估社会风险因素和护理分裂对医院绩效指标的影响 再入院和死亡率 假设：在风险调整模型中包括社会风险因素和护理分裂 更改VA医院的绩效排名 目标3：确定居住地，护理分裂，SES和少数群体与急性的关系 和长期VA手术保健利用以告知VA资源分配 假设：低SES，乡村，护理分裂和少数群体状况与更高的VA资源有关 利用率 方法论：使用传统参数和探索机器学习技术进行定量分析 在不同的数据集上进行，以开发使用护理碎片的手术结果的预测模型， 针对医疗合并症调整的乡村和社会风险因素风险，并应用于VA质量指标。 实施/下一步：使用社会风险因素和护理部署高质量的度量模型 VA系统中的分裂。调整资源分配以说明社会风险因素。