ArticleHealth care management science2020
COVID-19 scenario modelling for the mitigation of capacity-dependent deaths in intensive care.
Article in Health care management science, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers, 1 of them a synthesis that pooled it.
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Who cites it
29 citing papers in PubMed, 1 synthesis or guideline pooled it.
- A systematic literature review on public health and healthcare resources for pandemic preparedness planning.BMC public health · 2024Pooled it
- Enhancing pandemic surveillance and testing: a simulation modeling study utilizing german multicenter data with federated machine learning.Health care management science · 2026Article
- Risk Assessment of Emergency Medical Resource Exhaustion Under Major Infectious Disease Outbreaks: Based on Discrete-Event Simulation Models.Risk management and healthcare policy · 2026Article
- A simulation model for predicting hospital occupancy for Covid-19 using archetype analysis.Healthcare analytics (New York, N.Y.) · 2023Article
- A queuing model for ventilator capacity management during the COVID-19 pandemic.Health care management science · 2023Article
- Artificial intelligence and discrete-event simulation for capacity management of intensive care units during the Covid-19 pandemic: A case study.Journal of business research · 2023Article
- Effectiveness of feedback control and the trade-off between death by COVID-19 and costs of countermeasures.Health care management science · 2023Article
- Rationing scarce healthcare capacity: A study of the ventilator allocation guidelines during the COVID-19 pandemic.Production and operations management · 2023Article
- Identification of Key Components in Health System Using System Thinking Approach: A Scoping Review.Medical journal of the Islamic Republic of Iran · 2023Article
- COVID-19 health policy evaluation: integrating health and economic perspectives with a data envelopment analysis approach.The European journal of health economics : HEPAC : health economics in prevention and care · 2022Article
- Article
- Associations between the COVID-19 Pandemic and Hospital Infrastructure Adaptation and Planning-A Scoping Review.International journal of environmental research and public health · 2022Article
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- Cure models to estimate time until hospitalization due to COVID-19: A case study in Galicia (NW Spain).Applied intelligence (Dordrecht, Netherlands) · 2022Article
- A Community-Based Model of Care During the Fourth Wave of the COVID-19 Outbreak in Ho Chi Minh City, Vietnam.Frontiers in artificial intelligence · 2022Article
- Management of hospital beds and ventilators in the Gauteng province, South Africa, during the COVID-19 pandemic.PLOS global public health · 2022Article
- Predicting Hospital Resource Use During COVID-19 Surges: A Simple but Flexible Discretely Integrated Condition Event Simulation of Individual Patient-Hospital Trajectories.Value in health : the journal of the International Society for Pharmacoeconomics and Outcomes Research · 2021Article
- Considerations for acute care staffing during a pandemic.Best practice & research. Clinical anaesthesiology · 2021Review
- The development and deployment of a model for hospital-level COVID-19 associated patient demand intervals from consistent estimators (DICE).Health care management science · 2021Article
- Balancing scarce hospital resources during the COVID-19 pandemic using discrete-event simulation.Health care management science · 2021Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
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.
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