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

从新冠肺炎期间的医院准备中吸取教训： 长期资源不足的护士与病人安全 本研究将评估医院护士如何应对新冠肺炎突发公共卫生事件，是否和 医院护士资源(人员配备、工作环境、磁铁名称)在多大程度上缓冲了护士的 在大流行期间结果不佳(如倦怠)，并在疫情开始3年后促进恢复 COVID紧急情况，以及患者结局、安全性、质量和护理价值指标的程度 在研究期间，护士结果和医院护士资源的变化是平行的。我们将完成 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，实现这些目标 约克和伊利诺伊州，[第一波数据在COVID之前收集(2019年12月-2020年2月)；第二波收集1年 在COVID发病后]并通过对重复测量进行初步数据收集[待收集的第三波3 新冠肺炎发病数年后(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 (例如，职业倦怠、工作不满、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衡量标准，包括AHRQ患者安全文化中的项目 调查。这些横截面数据将与来自CMS的同期(1)患者级别数据相关联 MEDPAR Medicare将研究因普通医疗住院的患者的风险调整后患者结局， 外科和COVID诊断；(2)医院比较数据以评估患者的医院级别措施 满意度和医疗价值(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)可公开获得的COVID住院数据 解释了不同医院之间COVID负担的差异。总而言之，我们将拥有3个数据横截面 来自244家医院(护士和病人人数波动)，在治疗前、1年和3年后 冠状病毒紧急情况的开始。我们的分析方法使用多层嵌套(层次相关)线性 和Logistic回归模型(带交互作用项)。COVID紧急情况提供了一个独特的机会 在我们对护士和护士之间潜在因果关系的科学理解方面取得重大进展 结果和患者结果，到目前为止，这些结果基本上只在交叉试验中进行了严格的评估- 一节。COVID紧急情况带来的巨大冲击，加上我们幸运的定时数据， 使我们能够评估大流行如何影响医院护士，以及哪些医院因素对 在COVID紧急情况发生后的几年里，恢复情况更加有利。总而言之，这一证据将告诉HIGH- 影响构建和维持安全、高价值医疗保健的可操作政策和组织解决方案 能够承受未来公共卫生突发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.