Healthcare Systems Engineering, Massachusetts General Hospital, Boston, MA, USA.
Department of Anesthesia, Critical Care and Pain Medicine, Massachusetts General Hospital, Boston, MA, USA.
Disaster Med Public Health Prep. 2022 Oct;16(5):2182-2184. doi: 10.1017/dmp.2021.51. Epub 2021 Feb 16.
Before coronavirus disease 2019 (COVID-19), few hospitals had fully tested emergency surge plans. Uncertainty in the timing and degree of surge complicates planning efforts, putting hospitals at risk of being overwhelmed. Many lack access to hospital-specific, data-driven projections of future patient demand to guide operational planning. Our hospital experienced one of the largest surges in New England. We developed statistical models to project hospitalizations during the first wave of the pandemic. We describe how we used these models to meet key planning objectives. To build the models successfully, we emphasize the criticality of having a team that combines data scientists with frontline operational and clinical leadership. While modeling was a cornerstone of our response, models currently available to most hospitals are built outside of their institution and are difficult to translate to their environment for operational planning. Creating data-driven, hospital-specific, and operationally relevant surge targets and activation triggers should be a major objective of all health systems.
在 2019 年冠状病毒病（COVID-19）之前，很少有医院对紧急扩充计划进行全面测试。扩充时间和程度的不确定性使规划工作变得复杂，使医院面临不堪重负的风险。许多医院无法获得针对特定医院、基于数据的未来患者需求预测，以指导运营规划。我们医院经历了新英格兰地区最大的一次扩充。我们开发了统计模型来预测大流行第一波期间的住院人数。我们介绍了如何使用这些模型来实现关键的规划目标。要成功建立模型，我们强调必须有一个团队，将数据科学家与一线运营和临床领导相结合。虽然建模是我们应对措施的基石，但大多数医院目前使用的模型是在其机构之外构建的，并且难以将其转换为其环境以进行运营规划。制定数据驱动、针对特定医院和与运营相关的扩充目标和激活触发因素应该是所有卫生系统的主要目标。
