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, we developed 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, modelling results suggested 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.
The main paper of this very early COVID-19 study was published in Health Care Management Science in Jul 2020 (submitted in April 2020).