RAPID: A Portal to Support Models for Assessing Strategies for Hospitals in the COVID-19 and other Pandemics - MASH-Pandemics

RAPID：支持评估医院应对 COVID-19 和其他流行病策略的模型的门户 - MASH-Pandemics

• 批准号: 2027624
• 负责人: Elise Miller-Hooks
• 金额: $ 20万
• 依托单位: George Mason University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-05-01 至 2022-04-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2027624&HistoricalAwards=false
• 关键词: RAPID; Portal; Support; Models; Assessing

This Rapid Response Research (RAPID) grant will develop the Models for Assessing Strategies for Hospitals (MASH) in Pandemics (MASH-Pandemics) Portal with requisite modeling capabilities urgently needed by hospitals and regions in responding to the COVID-19 pandemic. Important perishable, time-sensitive data and information to support this effort will be collected. MASH-Pandemics will build on previously developed sophisticated, detailed discrete-event simulation-based hospital capacity and capability analysis models of typical U.S. urban hospitals. This RAPID project will support the re-specification of these models, data collection, model runs, and results analysis, outcomes from which will aid hospital administrators and regions in making optimal operational changes and collaboration plans enabled through state and national emergency declarations in response to the COVID-19 outbreak. An online portal will be constructed on which details of the modeling capabilities, practical findings and recommendations, along with potential policy implications, for responding to the COVID-19 pandemic will be posted. Additionally, run requests from hospitals, hospital collaborations and geographical regions will be taken through the portal. This work will generate crucial synthetic data needed to develop quick recommendations and analyses in a period where time is of the essence. Key outputs will include, for example: potential for various modified operational strategies to benefit hospital performance and patient survival, hospital collaboration strategies to aid regional response, anticipating critical supply needs to mobilize and prioritize support from supply chains (or Federal response capabilities), and recommendations for effective implementation of capacity enhancement strategies (alternative standards of care, modified operations, demand management). The project will provide input to educational activities in the future, once the project is complete and the pandemic subsides. The focus of this work during the period of performance will be on providing, as quickly as possible, crucially needed recommendations to hospitals and regions based on results from runs of high-quality models. This RAPID award will advance mathematical modeling techniques for capturing critical hospital services during crises. It employs concepts of open queuing networks, discrete event simulation, stochastic modeling, transient system analysis, and statistical methods. The work will collect critical, perishable data, and will generate crucial synthetic data for rapid analysis and prediction urgently needed in this period of a global COVID-19 pandemic. With its quantitative approach, the project will enhance hospital readiness, capacity and capability, by identifying means for efficiently using severely limited, critical personnel and physical resources, the allocation of which will affect the survival of potentially thousands of lives and the safety of health care workers along with support staff. Findings from this effort will directly support hospitals at the front line, or regions in COVID-19 “hot spots,” by providing the opportunity to request runs and receive analyses of the effectiveness of COVID-19 response strategies and collaboration mechanisms. It is anticipated that the run requests will come in a variety of forms, requiring data collection, modeling work, investigation to capture stochastic processes with input distributions and parameters, and results analyses. The models can be quickly enhanced and mobilized, and initial findings and recommendations made public in only weeks.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.

这笔快速响应研究 (RAPID) 赠款将开发大流行病医院评估策略模型 (MASH-Pandemics) 门户，具有医院和地区在应对 COVID-19 大流行时迫切需要的必要建模能力。将收集支持这项工作的时间敏感数据和信息，该模型将建立在先前开发的复杂、详细的基于离散事件模拟的医院能力和能力分析模型的基础上。该 RAPID 项目将支持这些模型的重新规范、数据收集、模型运行和结果分析，其结果将帮助医院管理者和地区通过州和州实现最佳的运营变革和合作计划。将建立一个在线门户，发布应对 COVID-19 大流行的建模能力、实际结果和建议以及潜在的政策影响的详细信息，处理医院、医院合作的请求这项工作将生成在时间至关重要的时期制定快速建议和分析所需的重要综合数据，例如：各种修改后的运营战略受益的潜力。医院绩效和患者生存率、协助区域应对的医院合作战略、预测关键供应需求以动员和优先考虑供应链（或联邦应对能力）的支持，以及有效实施能力增强战略的建议（替代护理标准、改进的操作） 、需求管理）。该项目将为教育活动提供投入。未来，一旦项目完成且疫情消退，执行期间的工作重点将是根据高质量运行的结果，尽快向医院和地区提供急需的建议。该 RAPID 奖项将采用开放排队网络、离散事件模拟、随机建模、瞬态系统分析和统计方法的概念，推进在危机期间捕获关键医院服务的数学建模技术。并将产生关键的合成全球 COVID-19 大流行期间迫切需要数据进行快速分析和预测，该项目将通过定量方法，通过确定有效利用严重有限的关键人员和物质资源的方法，增强医院的准备情况、能力和能力，其分配将影响潜在数千人的生存以及医护人员和支持人员的安全。这项工作的结果将直接支持前线医院或 COVID-19“热点地区”。提供请求运行和接收有效性分析的机会COVID-19 响应策略和协作机制。预计运行请求将以多种形式出现，需要数据收集、建模工作、调查以捕获输入分布和参数的随机过程以及结果分析。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

Models for Assessing Strategies for Improving Hospital Capacity for Handling Patients during a Pandemic

评估提高医院在大流行期间处理患者能力的策略的模型

• DOI: 10.1017/dmp.2022.12
• 发表时间: 2022-01-10
• 期刊: Disaster Medicine and Public Health Preparedness
• 影响因子: 2.7
• 作者: Bahar Shahverdi;Elise Miller;Mersedeh Tariverdi;H. Ghayoomi;D. Prentiss;T. Kirsch
• 通讯作者: T. Kirsch

Assessing resilience of hospitals to cyberattack

评估医院抵御网络攻击的能力

• DOI: 10.1177/20552076211059366
• 发表时间: 2021-01
• 期刊: DIGITAL HEALTH
• 作者: Ghayoomi H;Laskey K;Miller-Hooks E;Hooks C;Tariverdi M
• 通讯作者: Tariverdi M