Modelling and Analytics, UK National Health Service (BNSSG CCG), Bristol, UK.
Centre for Healthcare Innovation and Improvement (CHI2), School of Management, University of Bath, Bath, UK.
Health Care Manag Sci. 2020 Sep;23(3):315-324. doi: 10.1007/s10729-020-09511-7. Epub 2020 Jul 8.
Managing healthcare demand and capacity is especially difficult in the context of the COVID-19 pandemic, where limited intensive care resources can be overwhelmed by a large number of cases requiring admission in a short space of time. If patients are unable to access this specialist resource, then death is a likely outcome. In appreciating these 'capacity-dependent' deaths, this paper reports on the clinically-led development of a stochastic discrete event simulation model designed to capture the key dynamics of the intensive care admissions process for COVID-19 patients. With application to a large public hospital in England during an early stage of the pandemic, the purpose of this study was to estimate the extent to which such capacity-dependent deaths can be mitigated through demand-side initiatives involving non-pharmaceutical interventions and supply-side measures to increase surge capacity. Based on information available at the time, results suggest that total capacity-dependent deaths can be reduced by 75% through a combination of increasing capacity from 45 to 100 beds, reducing length of stay by 25%, and flattening the peak demand to 26 admissions per day. Accounting for the additional 'capacity-independent' deaths, which occur even when appropriate care is available within the intensive care setting, yields an aggregate reduction in total deaths of 30%. The modelling tool, which is freely available and open source, has since been used to support COVID-19 response planning at a number of healthcare systems within the UK National Health Service.
管理医疗需求和能力在 COVID-19 大流行的背景下尤其困难，在这种情况下，有限的重症监护资源可能会被大量需要在短时间内入院的病例所淹没。如果患者无法获得这种专科资源，那么死亡是很可能的结果。在认识到这些“依赖能力”的死亡时，本文报告了临床主导的开发随机离散事件模拟模型的情况，该模型旨在捕捉 COVID-19 患者重症监护入院过程的关键动态。该模型应用于英格兰一家大型公立医院在大流行的早期阶段，这项研究的目的是估计通过涉及非药物干预的需求方举措和增加激增能力的供应方措施来减轻这种依赖能力的死亡的程度。基于当时可获得的信息，结果表明，通过将容量从 45 张增加到 100 张、将住院时间缩短 25%以及将每日需求峰值降低至 26 人，总共可以减少 75%的依赖能力的死亡。考虑到即使在重症监护环境中可以获得适当的护理也会发生的额外的“非依赖能力”死亡，总死亡人数的综合减少量为 30%。该建模工具是免费的，并且是开源的，此后已被用于支持英国国民保健系统内的多个医疗保健系统的 COVID-19 应对规划。
