tetano
Editor, Senior Moderator
Health Care Manag Sci
. 2020 Jul 8.
doi: 10.1007/s10729-020-09511-7. Online ahead of print.
COVID-19 Scenario Modelling for the Mitigation of Capacity-Dependent Deaths in Intensive Care
Richard M Wood[SUP] 1 2 [/SUP], Christopher J McWilliams[SUP] 3 [/SUP], Matthew J Thomas[SUP] 4 [/SUP], Christopher P Bourdeaux[SUP] 4 [/SUP], Christos Vasilakis[SUP] 5 [/SUP]
Affiliations
Abstract
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.
Keywords: COVID-19; Capacity management; Coronavirus; Intensive care; Operations research; Simulation.
. 2020 Jul 8.
doi: 10.1007/s10729-020-09511-7. Online ahead of print.
COVID-19 Scenario Modelling for the Mitigation of Capacity-Dependent Deaths in Intensive Care
Richard M Wood[SUP] 1 2 [/SUP], Christopher J McWilliams[SUP] 3 [/SUP], Matthew J Thomas[SUP] 4 [/SUP], Christopher P Bourdeaux[SUP] 4 [/SUP], Christos Vasilakis[SUP] 5 [/SUP]
Affiliations
- PMID: 32642878
- DOI: 10.1007/s10729-020-09511-7
Abstract
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.
Keywords: COVID-19; Capacity management; Coronavirus; Intensive care; Operations research; Simulation.