Nursing

Psychological intervention

Context (archaeology)

Intensive care medicine

2019-20 coronavirus outbreak

Health care

Public health

Health economics

Virology

Archaeology

Economics

Computer science

Medical emergency

Surge Capacity

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)

Outbreak

Business

Coronavirus disease 2019 (COVID-19)

Computer network

Health informatics

Capacity planning

Geography

Health administration

Infectious disease (medical specialty)

Internal medicine

Operations management

Medicine

Economic growth

Pandemic

Capacity management

Disease

Intensive care

Pathology

Health Care Management Science

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.

COVID-19 scenario modelling for the mitigation of capacity-dependent deaths in intensive care