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

这笔快速反应研究(RAPID)赠款将开发评估大流行医院战略门户网站的模型，该门户网站具有医院和地区在应对新冠肺炎大流行方面迫切需要的必要建模能力。将收集支持这一努力的重要的易腐烂、对时间敏感的数据和信息。MASH-流行病将建立在以前开发的复杂的、基于离散事件模拟的医院能力和典型美国城市医院的能力分析模型的基础上。这个快速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人员和地区通过州和国家紧急声明实现最佳运营变化和协作计划，以应对新冠肺炎疫情。将建立一个在线门户网站，在该门户网站上将发布应对新冠肺炎大流行的建模能力、实际结果和建议的详细信息，以及潜在的政策影响。此外，来自医院、医院协作和地理区域的运行请求将通过门户网站接受。这项工作将产生所需的关键综合数据，以便在时间紧缺的时期制定快速建议和分析。例如，主要产出将包括：各种改进的业务战略的潜力，以有利于医院的业绩和患者的生存；医院协作战略，以帮助区域应对；预测关键的供应需求，以动员供应链(或联邦应对能力)的支持并确定其优先顺序；以及有效实施能力增强战略的建议(替代护理标准、改进的业务、需求管理)。一旦该项目完成并且疫情消退，该项目将为今后的教育活动提供投入。在执行期间，这项工作的重点将是根据运行高质量模型的结果，尽快向医院和地区提供急需的建议。这一快速奖励将推进数学建模技术，以便在危机期间捕获关键医院服务。它使用了开放排队网络、离散事件模拟、随机建模、暂态系统分析和统计方法的概念。这项工作将收集关键的、易腐烂的数据，并将生成关键的合成数据，用于快速分析和预测，这是全球新冠肺炎大流行时期迫切需要的。通过其量化方法，该项目将通过确定有效利用严重有限的关键人员和实物资源的方法，提高医院的准备、能力和能力，这些资源的分配将影响到潜在数千人的生命生存以及医护人员和支助人员的安全。这项工作的结果将通过提供请求运行和获得对新冠肺炎应对战略和协作机制的有效性的分析的机会，直接支持一线或新冠肺炎“热点”地区的医院。预计运行请求将以各种形式出现，需要数据收集、建模工作、调查以捕获具有输入分布和参数的随机过程以及结果分析。这些模型可以迅速增强和动员起来，最初的发现和建议只需几周就能公布。这一奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

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.