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

项目摘要 存在广泛的共识，即未来的流行病将会发生，需要更好的准备来管理激增， 我们应该从新冠肺炎大流行中学到很多东西。到目前为止，SARS-CoV-2病毒已经造成了 仅在美国就有4600万人感染，325万人住院，74.5万人死亡，该地区的 不同的时间、大小和持续时间使医疗保健能力严重紧张，并影响患者、工作人员、 和系统安全。与其他流行病一样，局部暴发和激增不断变化，往往导致 危机管理、临时房间、不符合标准的个人防护设备和有限的配给 资源。在其他需求中，需要更好的实时方法来预测医院、设备和 工作人员的能力和短缺，以允许更早的先发制人的缓解(GAP)。 虽然分析模型越来越多地被使用，但大多数都是在更宏观的政策层面上，而不是在设施空间层面上。 具体的业务水平(GAP)，在后一种情况下，对其在实践中的使用、准确性、决策- 制定工作流程、采用、效用以及对运营、结果和安全的影响。在我们自己的工作中，我们致力于- OpEd和广泛部署的集成模型，可预测特定于设施和特定于单元的需求，适应 这些方面的实时变化，并提前4周估计每日容量、需求和短缺(房间、设备- 所有50个州和91个国家的系统下载。在使用时 系统工程模型在其他环境中被很好地接受，它们的使用、效用和影响明显不被接受。 DER-在这一重要背景下进行的研究，以及更广泛的医疗保健，具有潜在的重要教训 未来(差距)。 因此，本项目将采取多方法方法(目标1)进行建模研究，以进一步完善再 结果到目前为止，优化准确性，并满足已确定的技术需求，(目标2)评估影响并确定 在大范围模拟未来下改进医院能力和安全的开发模型的可靠性 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- 新冠肺炎期间的技巧、模型采用过程、产生的操作类型、使用障碍和用户感知- 实用性、准确性和基于模型的决策。该项目将由一位经验丰富的中间人领导。 纪律医疗模型团队，与不同的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、医院突发事件和建模方面的专业知识。预期结果包括 (1)经过验证的稳健模型，可先发制人地预测和应对护理激增；(2)减少不安全因素 未来疫情期间的医院危机管理条件，以及(3)提高了对如何最好地 使用系统工程模型来应对疫情激增和其他重要的公共卫生和护理交付- 非常麻烦。

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.