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.

在COVID期间向医院的准备学习： 长期资源不足的护士和患者安全 这项研究将评估医院护士如何度过COVID-199公共卫生紧急情况，无论是和 医院护士资源（人员配备，工作环境，磁铁指定）在多大程度上使护士的缓冲护士 在大流行期间的结局不佳（例如倦怠）和促进恢复后3年 COVID紧急情况以及患者结果，安全，质量和护理指标的价值的程度 在研究期间，护士成果和医院护士资源的平行变化。我们将完成 这些目标是利用已有33,000多名医院护士的244位医院的现有数据 约克（York）和伊利诺伊州（Illinois），[在Covid之前收集的Wave 1数据（2019年12月2020年12月）；第2波收集1年 在发作后]，并通过进行重复测量的主要数据收集[要收集的波3 在COVID发作后（2022年10月2022年10月）]。每个浪潮都重复衡量护士结果 （例如，倦怠，工作不满，意图离开工作），医院护士资源（人员配备，工作环境， 磁铁），测量患者安全和护理质量，包括来自AHRQ患者安全文化的物品 民意调查。这些数据的这些横截面将与同期（1）CMS的患者级数据联系起来 MEDPAR Medicare研究常见医疗住院的患者中的风险调整患者预后， 手术和共同诊断； （2）医院比较数据以评估患者的医院水平措施 满意度和医疗保健价值（每个受益人的医疗保险支出），（3）美国医院协会数据 用于考虑医院的组织特征，以及（4）公开可用的共同住院数据 解释了整个医院的共同负担的变化。结合使用，我们将拥有3个横截面数据 在244家医院（有波动的护士和患者人口）之前，就在 共同紧急情况的发作。我们的分析方法使用多层嵌套（层次相关）线性 和逻辑回归模型（具有相互作用项）。 COVID紧急情况提供了独特的机会 在我们对护士之间可能因果关系的科学理解中取得了重大进步 结局和患者的结果，到目前为止，这些结果仅在交叉中进行了严格评估 部分。共同紧急情况造成的巨大冲击，加上我们的临时数据， 使我们能够评估大流行如何影响医院护士，以及哪些医院因素促成 在联盟紧急情况之后的几年中，更有利的恢复。这些证据在一起将为高度通知 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 可以忍受未来公共卫生紧急情况并在普通时期蓬勃发展的系统。