Learning from Hospital Preparedness during COVID: Chronically Under-Resourced Nurses and Patient Safety

从医院在新冠肺炎期间的准备中学习：长期资源不足的护士和患者安全

• 批准号: 10698033
• 负责人: Karen Blanchette Lasater
• 金额: $ 34.57万
• 依托单位: UNIVERSITY OF PENNSYLVANIA
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-06 至 2025-08-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10698033
• 关键词: Learning; Hospital; Preparedness; during; COVID

Learning from Hospital Preparedness during COVID: Chronically Under-Resourced Nurses and Patient Safety This study will evaluate how hospital nurses weathered the COVID-19 public health emergency, whether and to what extent hospital nurse resources (staffing, work environment, Magnet designation) buffered nurses from poor outcomes (such as burnout) during the pandemic and facilitated recovery 3 years after the onset of the COVID emergency, and the extent to which patient outcomes, safety, quality, and value of care indicators paralleled changes in nurse outcomes and hospital nurse resources over the study period. We will accomplish these objectives by leveraging already existing data from over 33,000 hospital nurses in 244 hospitals in New York and Illinois, [Wave 1 data collected just before COVID (Dec 2019-Feb 2020); Wave 2 collected 1 year after COVID onset] and by conducting primary data collection of repeat measures [Wave 3 to be collected 3 years after COVID onset (Oct 2022-Dec 2022)]. Each Wave includes repeated measures of nurse outcomes (e.g., burnout, job dissatisfaction, intent to leave job), hospital nurse resources (staffing, work environment, Magnet), measures of patient safety and quality of care, including items from the AHRQ Patient Safety Culture survey. These cross-sections of data will be linked with contemporaneous (1) patient-level data from CMS MedPAR Medicare to study risk-adjusted patient outcomes among patients hospitalized for common medical, surgical, and COVID diagnoses; (2) Hospital Compare data to evaluate hospital-level measures of patient satisfaction and healthcare value (Medicare spending per beneficiary), (3) American Hospital Association data for considering organizational features of hospitals, and (4) publicly available COVID hospitalization data to account for variation in COVID burden across hospitals. In combination, we will have 3 cross-sections of data from 244 hospitals (with fluctuating nurse and patient populations) just before, 1 year and 3 years after the onset of the COVID emergency. Our analytic approach uses multi-level nested (hierarchically-related) linear and logistic regression models (with interaction terms). The COVID emergency offers a unique opportunity to make a major advance in our scientific understanding of the potentially causal relationships between nurse outcomes and patient outcomes, which have until now largely only been rigorously evaluated in the cross- section. The tremendous shock imposed by the COVID emergency, combined with our propitiously timed data, enable us to evaluate how the pandemic impacted hospital nurses and what hospital factors contribute to a more favorable recovery in the years following the COVID emergency. Together, this evidence will inform high- impact actionable policy and organizational solutions for building and sustaining safe, high value healthcare systems that can endure future public health emergencies and thrive during ordinary times.

从新冠肺炎期间医院的准备中学习： 长期资源不足的护士和患者安全 本研究将评估医院护士如何应对 COVID-19 公共卫生紧急情况，是否以及 医院护士资源（人员、工作环境、磁铁指定）在多大程度上缓冲了护士 疫情期间的不良后果（例如倦怠），并在疫情发生三年后促进了恢复 新冠肺炎紧急情况，以及患者治疗结果、安全性、质量和护理价值指标的程度 研究期间护士结果和医院护士资源的平行变化。我们将完成 通过利用新州 244 家医院 33,000 多名医院护士的现有数据来实现这些目标 约克和伊利诺伊州，[在新冠肺炎爆发之前收集的第一波数据（2019 年 12 月至 2020 年 2 月）；第 2 波收集 1 年 新冠肺炎发病后]并通过重复措施进行初步数据收集[将收集第 3 波 3 新冠肺炎发病后数年（2022 年 10 月至 2022 年 12 月）]。每波都包括对护士结果的重复测量 （例如，倦怠、工作不满、离职意向）、医院护士资源（人员配置、工作环境、 Magnet），患者安全和护理质量的衡量标准，包括来自 AHRQ 患者安全文化的项目 民意调查。这些横截面数据将与来自 CMS 的同期 (1) 患者级数据相关联 MedPAR Medicare 将研究因普通医疗、住院治疗而住院的患者的风险调整后的患者结局 外科和新冠肺炎诊断； (2) 医院比较数据以评估患者的医院级别测量 满意度和医疗保健价值（每位受益人的医疗保险支出），(3) 美国医院协会数据 考虑医院的组织特征，以及 (4) 公开的新冠肺炎住院数据 考虑到各医院的新冠肺炎负担的差异。结合起来，我们将得到 3 个横截面的数据 来自 244 家医院（护士和患者人数存在波动）之前、一年和三年后的数据 新冠肺炎紧急情况的爆发。我们的分析方法使用多级嵌套（层次相关）线性 和逻辑回归模型（带有交互项）。新冠疫情紧急情况提供了一个独特的机会 在我们对护士之间潜在因果关系的科学理解方面取得了重大进展 结果和患者的结果，到目前为止，这些结果很大程度上只是在交叉研究中进行了严格的评估 部分。新冠疫情紧急情况带来的巨大冲击，加上我们及时提供的数据， 使我们能够评估这一流行病如何影响医院护士以及哪些医院因素导致了 在新冠疫情紧急情况发生后的几年里，复苏会更加顺利。总之，这些证据将为高 影响可行的政策和组织解决方案，以建立和维持安全、高价值的医疗保健 能够承受未来突发公共卫生事件并在平时蓬勃发展的系统。