Epidemic Surge Model Use to Improve Patient, Staff, and System Safety and Resiliency

使用流行病激增模型来提高患者、工作人员和系统的安全性和弹性

• 批准号: 10522738
• 负责人: JAMES C BENNEYAN
• 金额: $ 40万
• 依托单位: NORTHEASTERN UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-08-01 至 2027-05-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10522738
• 关键词: Epidemic; Surge; Model; Use; Improve

Project Summary Broad agreement exists that future epidemics will occur, better preparedness is needed for managing surges, and much should be learned from the COVID-19 pandemic. The SARS-CoV-2 virus to-date has caused over 46 million infections, 3.25 million hospitalizations, and 745,000 deaths in the U.S. alone, with regional surges of varied timing, magnitude, and duration profoundly straining healthcare capacity and impacting patient, staff, and system safety. As with other epidemics, local outbreaks and surges continuously change, often resulting in crisis management, makeshift rooming, sub-standard personal protection equipment, and rationing of limited resources. Among other needs, better real-time methods are needed to anticipate hospital, equipment, and staff capacities and shortages to allow earlier preemptive mitigation (gap). While analytic models are increasingly used, most are at the more macro policy level rather than facility-spe- cific operational level (gap), in the latter case with little known about their use in practice, accuracy, decision- making workflow, adoption, utility, and impact on operations, outcomes, and safety. In our own work, we devel- oped and widely deployed integrated models that predict facility-specific and unit-specific demand, adapt to real-time changes in these, and estimate 4-week ahead daily capacity, demand, and shortages (rooms, equip- ment, staff) within any given facility, downloaded by systems in all 50 states and 91 countries. While use of systems engineering models is well-accepted in other settings, their use, utility, and impact is significantly un- der-studied in this important context and healthcare more generally, with potentially important lessons for the future (gap). This project thus will take a multi-methods approach to (Aim 1) conduct modeling research to further refine re- sults to-date, optimize accuracy, and address identified technical needs, (Aim 2) evaluate impacts and accu- racy of the developed models on improved hospital capacity and safety under a wide range of simulated future and past surge scenarios, and (Aim 3) maximize future utility by studying how our models were used in prac- tice during COVID-19, the model adoption process, types of resulting actions, barriers to use, and user percep- tions of utility, accuracy, and model-based decision-making. The project will be led by an experienced interdis- ciplinary healthcare modeling team, working closely with varied hospital data sites and an advisory committee with expertise in patient safety, epidemic response, hospital surges, and modeling. Anticipated results include (1) validated robust models for preemptively anticipating and responding to care surges, (2) reduced unsafe hospital crisis management conditions during future epidemics, and (3) improved understanding of how to best use systems engineering models to address epidemic surges and other important public health and care deliv- ery problems.

项目概要 人们普遍认为，未来的流行病将会发生，需要更好的准备来管理激增， 我们应该从 COVID-19 大流行中学到很多东西。迄今为止，SARS-CoV-2 病毒已造成超过 仅在美国就有 4600 万人感染、325 万人住院、74.5 万人死亡，其中地区激增 不同的时间、幅度和持续时间极大地影响了医疗保健能力，并影响了患者、工作人员、 和系统安全。与其他流行病一样，局部暴发和高峰不断变化，往往导致 危机管理、临时房间、不合标准的个人防护设备以及有限的配给 资源。除其他需求外，还需要更好的实时方法来预测医院、设备和 人员能力和短缺，以便尽早采取先发制人的缓解措施（差距）。 虽然分析模型的使用越来越多，但大多数是在宏观政策层面而不是特定设施层面。 具体的操作水平（差距），在后一种情况下，对其在实践中的使用、准确性、决策知之甚少 制定工作流程、采用、实用性以及对运营、结果和安全的影响。在我们自己的工作中，我们开发—— 操作和广泛部署的集成模型，可以预测特定于设施和单位的特定需求，适应 这些的实时变化，并估计未来 4 周的每日容量、需求和短缺（房间、设备） 任何给定设施内的信息，由所有 50 个州和 91 个国家的系统下载。当使用 系统工程模型在其他环境中已被广泛接受，但它们的使用、效用和影响却远非如此。 在这一重要背景和更广泛的医疗保健领域进行了深入研究，为人们提供了潜在的重要教训 未来（差距）。 因此，该项目将采取多种方法（目标 1）进行建模研究，以进一步完善重新设计 取得最新成果，优化准确性并解决已确定的技术需求，（目标 2）评估影响并准确 所开发的模型在广泛的模拟未来下提高医院容量和安全性的有效性 和过去的激增情景，以及（目标 3）通过研究我们的模型如何在实践中使用来最大化未来的效用 在 COVID-19 期间，模型采用过程、结果操作类型、使用障碍和用户感知 效用、准确性和基于模型的决策。该项目将由经验丰富的跨部门领导 专业医疗保健建模团队，与各个医院数据站点和咨询委员会密切合作 拥有患者安全、流行病应对、医院激增和建模方面的专业知识。预期结果包括 (1) 验证稳健的模型，用于先发制人地预测和响应护理激增，(2) 减少不安全因素 未来流行病期间医院危机管理的情况，以及（3）提高了对如何最好地进行管理的理解 使用系统工程模型来解决流行病激增和其他重要的公共卫生和护理服务 非常问题。