RAPID: Variation in Resilience Under Shortages in the Medical Supply Chain

RAPID：医疗供应链短缺情况下的弹性变化

• 批准号: 2027927
• 负责人: Cassandra Thiel
• 金额: $ 20万
• 依托单位: New York University Medical Center
• 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: 2020-04-01 至 2021-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2027927&HistoricalAwards=false
• 关键词: RAPID; Variation; Resilience; Under; Shortages

The research objectives of this proposal are to measure the effects of Covid-19 on the medical Personal Protective Equipment (PPE) supply chain, analyze emerging hospital resource conservation policies, and identify short-term and long-term solutions to increase resilience in the medical supply chain. The research team will focus specifically on the effects of Covid-19 as a stressor to healthcare end-users of PPE (i.e., hospitals and medical clinics). The team will focus on the uncertainty of supply and demand and seeks to identify the best policies to reduce the impact for these end-users while ensuring quality of care and protection of medical staff. The primary aim of this study is to collect data and use optimization and analytical methods to assist medical providers during the Covid-19 crisis while also considering the longer-term potential of mitigating environmental impact and increasing resilience in healthcare supply chains. The approach of the study is two-fold: (1) data collection on PPE usage and supply chains, and (2) development of novel robust optimization models. A contribution of this study is to be collection of data on the physical PPE supply chain, hospitals' historic and current (emergency) use of PPE, and emerging PPE conservation policies and practices enacted by hospitals during the Covid-19 pandemic. To gather additional data, the research team has developed a novel crowd sourcing data collection platform that can later be adapted for other healthcare data collection as well. An additional feature parallel of this work lies in the development of mathematical scenario-based and robust optimization models for PPE supply chain and resource allocation. The research team will develop linear and robust optimization models to allocate the scarce resources given large uncertainty in demand and supply. These resource-allocation models will use the collected data. The team will also develop scenario-base models to test and validate the different hospital policies for the use of PPE during Covid-19 outbreak. The intended immediate impact of this research is to inform healthcare workers about effective policies for PPE conservation that lead to optimal health outcomes for patients and staff during the Covid-19 pandemic. The findings and the data collected as a result of this work will be made available to the medical community as soon as possible to help manage the need for PPE. Specifically, PPE conservation policies will be compiled, and a paper immediately released to the medical community to broadly help conserve resources during the Covid-19 pandemic. In the longer term, the strategies developed to effectively mitigate resource consumption and climate impacts of healthcare activities will be shared with the medical and the wider academic communities. The results of this study target increased resilience of medical supply chain and healthcare operations during times of crisis, including future pandemics and climate related disasters, as well as a general increase in emissions mitigation by continuing reasonable resource conservation practices beyond the pandemic.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.

该提案的研究目标是衡量新冠肺炎对医疗个人防护装备（PPE）供应链的影响，分析新兴的医院资源保护政策，并确定短期和长期解决方案，以提高医疗供应链的弹性。研究团队将特别关注新冠肺炎对个人防护设备医疗终端用户的压力（即，医院和诊所）。该小组将重点关注供需的不确定性，并寻求确定最佳政策，以减少对这些最终用户的影响，同时确保护理质量和对医务人员的保护。本研究的主要目的是收集数据，并使用优化和分析方法，在新冠肺炎危机期间为医疗服务提供者提供帮助，同时考虑减轻环境影响和提高医疗保健供应链弹性的长期潜力。该研究的方法是双重的：（1）收集PPE使用和供应链的数据，以及（2）开发新的鲁棒优化模型。本研究的贡献之一是收集有关PPE物理供应链、医院历史和当前（紧急）PPE使用情况以及医院在新冠肺炎大流行期间制定的PPE保护政策和做法的数据。为了收集更多的数据，研究团队开发了一种新型的众包数据收集平台，以后也可以用于其他医疗数据收集。 这项工作的另一个特点是为PPE供应链和资源分配开发基于数学的鲁棒优化模型。该研究团队将开发线性和稳健的优化模型，以在供需存在很大不确定性的情况下分配稀缺资源。这些资源分配模型将使用收集到的数据。该团队还将开发基于医院的模型，以测试和验证在新冠肺炎爆发期间使用PPE的不同医院政策。这项研究的预期直接影响是告知医护人员保护PPE的有效政策，从而在新冠肺炎大流行期间为患者和工作人员带来最佳健康结果。这项工作的结果和收集的数据将尽快提供给医学界，以帮助管理PPE的需求。具体而言，将编制个人防护设备保护政策，并立即向医学界发布一份文件，以在新冠肺炎大流行期间广泛帮助保护资源。从长远来看，为有效减轻医疗保健活动的资源消耗和气候影响而制定的战略将与医学界和更广泛的学术界分享。这项研究的结果旨在提高医疗供应链和医疗保健业务在危机时期的复原力，包括未来的流行病和气候相关灾害，该奖项反映了NSF的法定使命，并通过利用基金会的知识价值和更广泛的知识价值进行评估，被认为值得支持。影响审查标准。