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Warde PR, Patel S, Ferreira T, et al. Linking prediction models to government ordinances to support hospital operations during the COVID-19 pandemic. BMJ Health Care Inform. 2021;28(1)doi: 10.1136/bmjhci-2020-100248.
Warde, P. R., Patel, S., Ferreira, T., Gershengorn, H., Bhatia, M. C., Parekh, D., Manni, K., & Shukla, B. (2021). Linking prediction models to government ordinances to support hospital operations during the COVID-19 pandemic. BMJ health & care informatics, 28(1), . https://doi.org/10.1136/bmjhci-2020-100248
Warde, Prem Rajendra, et al. "Linking prediction models to government ordinances to support hospital operations during the COVID-19 pandemic." BMJ health & care informatics vol. 28,1 (2021). doi: https://doi.org/10.1136/bmjhci-2020-100248
Warde PR, Patel S, Ferreira T, Gershengorn H, Bhatia MC, Parekh D, Manni K, Shukla B. Linking prediction models to government ordinances to support hospital operations during the COVID-19 pandemic. BMJ Health Care Inform. 2021 May;28(1). doi: 10.1136/bmjhci-2020-100248. PMID: 33972270; PMCID: PMC8111872.
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BMJ Health Care Inform. 2021 May;28(1). doi: 10.1136/bmjhci-2020-100248.

Linking prediction models to government ordinances to support hospital operations during the COVID-19 pandemic.

BMJ health & care informatics

Prem Rajendra Warde, Samira Patel, Tanira Ferreira, Hayley Gershengorn, Monisha Chakravarthy Bhatia, Dipen Parekh, Kymberlee Manni, Bhavarth Shukla

Affiliations

  1. Department of Clinical Care Transformation, University of Miami Hospital and Clinics, Miami, Florida, USA [email protected].
  2. Department of Clinical Care Transformation, University of Miami Hospital and Clinics, Miami, Florida, USA.
  3. Division of Pulmonary, Critical Care, and Sleep Medicine, Department of Medicine, University of Miami Miller School of Medicine, Miami, Florida, USA.
  4. Department of Medicine, Jackson Memorial Hospital, Miami, Florida, USA.
  5. Department of Medicine, University of Miami School of Medicine, Miami, Florida, USA.
  6. Department of Urology, University of Miami Miller School of Medicine, Miami, Florida, USA.
  7. University of Miami Health System, Miami, Florida, USA.
  8. University of Miami Hospital and Clinics, Miami, FL, USA.
  9. Division of Infectious Diseases, Department of Medicine, University of Miami Miller School of Medicine, Miami, Florida, USA.

PMID: 33972270 PMCID: PMC8111872 DOI: 10.1136/bmjhci-2020-100248

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.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.

CONCLUSION: Predictive models are essential tools in supporting decision making when coordinating clinical operations during a pandemic.

© Author(s) (or their employer(s)) 2021. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.

Keywords: BMJ Health Informatics; information management; information science; information systems; medical informatics

Conflict of interest statement

Competing interests: None declared.

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