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

从COVID期间的医院准备工作中学习： 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应对COVID-19公共卫生紧急情况， 医院护士资源（人员配备、工作环境、磁铁指定）在多大程度上缓冲了 大流行期间的不良结果（如倦怠），并促进了发病后3年的恢复。 COVID紧急情况，以及患者结局、安全性、质量和护理指标价值的程度 研究期间护士结果和医院护士资源的变化。要全面完成 这些目标是通过利用来自纽约244家医院的33，000多名医院护士的现有数据来实现的。 约克和伊利诺伊州，[第1波数据在COVID前收集（2019年12月至2020年2月）;第2波数据收集1年 COVID发病后]和进行重复测量的初步数据收集[第3波待收集3 2022年10月至2022年12月]。每一波都包括对护士结果的重复测量 (e.g.,职业倦怠，工作不满意，离职意向），医院护士资源（人员配备，工作环境， 磁铁），患者安全和护理质量的措施，包括AHRQ患者安全文化的项目 调查.这些横截面数据将与CMS的同期（1）患者水平数据相关联 MedPAR医疗保险研究风险调整后的患者结果住院患者的普通医疗， 手术和COVID诊断;（2）医院比较数据，以评估医院一级的患者措施 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 考虑医院的组织特征，以及（4）公开的COVID住院数据， 解释了各医院COVID负担的差异。结合起来，我们将有3个横截面的数据 从244家医院（护士和患者人数不断变化）的前，1年和3年后， 新冠疫情紧急情况的爆发。我们的分析方法使用多层嵌套（分层相关）线性 和逻辑回归模型（具有交互作用项）。COVID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，这在很大程度上只在交叉评估， 科.新型冠状病毒疫情带来的巨大冲击，加上我们及时的数据， 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导致了 在COVID紧急情况发生后的几年里，经济复苏更为有利。这些证据将为高- 影响可操作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。

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.