Linking prediction models to government ordinances to support hospital operations during the COVID-19 pandemic. Issue 1 (10th May 2021)

• Record Type: Journal Article
• Authors: Warde, Prem Rajendra; Patel, Samira; Ferreira, Tanira; Gershengorn, Hayley; Bhatia, Monisha Chakravarthy; Parekh, Dipen; Manni, Kymberlee; Shukla, Bhavarth
• Is Part Of: BMJ health & care informatics. Volume 28:Issue 1(2021)
• Publication Date: 2021-05-10
• Subjects: BMJ Health Informatics -- medical informatics -- information management -- information science -- information systems; Medical informatics -- Great Britain -- Periodicals; Information storage and retrieval systems -- Medical care -- Periodicals; Primary care (Medicine) -- Great Britain -- Data processing -- Periodicals; 362.10285
• DOI: 10.1136/bmjhci-2020-100248
• Languages: English
• ISSNs: 2632-1009

Abstract

Abstract : Objectives: We describe a hospital's implementation of predictive models to optimise emergency response to the COVID-19 pandemic. Methods: We 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. Results: We 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. Discusssion Our 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. Conclusion: Predictive models are essential tools in supporting decision making when coordinating clinical operations during a pandemic.