ArticleBMJ health & care informatics2021
Linking prediction models to government ordinances to support hospital operations during the COVID-19 pandemic.
Article in BMJ health & care informatics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Evaluation of a pragmatic approach to predicting COVID-19-positive hospital bed occupancy.BMJ health & care informatics · 2025Observational
- The utility of infectious disease modelling in informing decisions for outbreak response: A scoping review.PLOS global public health · 2025Article
- A simulation model for predicting hospital occupancy for Covid-19 using archetype analysis.Healthcare analytics (New York, N.Y.) · 2023Article
- Using simulation modelling and systems science to help contain COVID-19: A systematic review.Systems research and behavioral science · 2022Article
- Estimating COVID Risk During a Period of Pandemic Decline.Frontiers in public health · 2021Article
Corrections and comments
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectivesWe describe a hospital's implementation of predictive models to optimise emergency response to the COVID-19 pandemic.
methodsWe were tasked to construct and evaluate COVID-19 driven predictive models to identify possible planning and resource utilisation scenarios. We used system dynamics to derive a series of chain susceptible, infected and recovered (SIR) models. We then built a discrete event simulation using the system dynamics output and bootstrapped electronic medical record data to approximate the weekly effect of tuning surgical volume on hospital census. We evaluated performance via a model fit assessment and cross-model comparison.
resultsWe outlined the design and implementation of predictive models to support management decision making around areas impacted by COVID-19. The fit assessments indicated the models were most useful after 30 days from onset of local cases. We found our subreports were most accurate up to 7 days after model run.DiscusssionOur model allowed us to shape our health system's executive policy response to implement a 'hospital within a hospital'-one for patients with COVID-19 within a hospital able to care for the regular non-COVID-19 population. The surgical scheduleis modified according to models that predict the number of new patients withCovid-19 who require admission. This enabled our hospital to coordinateresources to continue to support the community at large. Challenges includedthe need to frequently adjust or create new models to meet rapidly evolvingrequirements, communication, and adoption, and to coordinate the needs ofmultiple stakeholders. The model we created can be adapted to other health systems,provide a mechanism to predict local peaks in cases and inform hospitalleadership regarding bed allocation, surgical volumes, staffing, and suppliesone for COVID-19 patients within a hospital able to care for the regularnon-COVID-19 population.
conclusionPredictive models are essential tools in supporting decision making when coordinating clinical operations during a pandemic.
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