RAPID: Documenting Hospital Surge Operations in Responding to the COVID-19 Pandemic

持续的COVID-19大流行已导致许多医院大幅改变其正常业务，包括推迟选择性和非紧急手术，以适应重症传染病患者的激增，同时仍在接收和治疗与COVID-19无关的其他严重伤害和疾病。这项快速反应研究（Rapid）赠款将支持从俄勒冈州的撒玛利亚卫生服务中心和华盛顿特区/马里兰州的MedStar卫生系统这两个主要医院系统收集时间敏感数据，以记录因COVID-19导致的入院模式变化、管理激增的实践操作变化以及大流行期间的患者结果。该项目有可能为医院应对未来的流行病和其他大规模伤亡事件提供重要的决策支持，并应使人们更好地了解医院系统如何对这些重大干扰作出有效反应。pi将收集定性和定量数据，以记录医院业务，并评估COVID-19大流行期间患者的特征和结果。收集的数据将来自以下方面的医疗记录：接受治疗的患者人数、健康结果（包括诊断）、住院时间、治疗以及大流行前规划指南、重症监护室和医院病床容量分配和人员配置的实时业务变化、设备的可用性（包括个人防护设备和检测设备），以及其他医院、州和联邦的干预措施。一旦收集到这些数据，对这些数据的分析有望通过更好的模型和模拟产生知识，从而改善对未来流行病的应对。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

The ongoing COVID-19 pandemic has caused many hospitals to dramatically shift their normal operations, including delaying elective and non-emergency surgeries, to accommodate a surge in critically ill infectious disease patients while still admitting and treating other severe injuries and illnesses not related to COVID-19. This Rapid Response Research (RAPID) grant will support the collection of time-sensitive data from two major hospital systems, Samaritan Health Services in Oregon and MedStar Health System in Washington DC/Maryland, to document changes in admissions patterns due to COVID-19, operational changes in practices to manage the surge, and patient outcomes during the pandemic. The project has the potential to provide important decision support to hospitals as they respond to future pandemics and other mass casualty events and should lead to better understanding of how hospital systems can mount an effective response to these major disturbances.The PIs will collect qualitative and quantitative data to document hospital operations as well as assess patient characteristics and outcomes during the COVID-19 pandemic. Data collection will be sourced from medical records characterizing the patient population treated, health outcomes, including diagnoses, length of stay, treatment, as well as pre-pandemic planning guidelines, real-time operational changes in ICU and hospital bed capacity allocation and staffing, equipment availability, including personal protective equipment and testing equipment, and other hospital, state, and federal interventions. Once collected, analysis of these data is expected to lead to knowledge, through better models and simulation, that can improve response to future pandemics.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.