Linking prediction models to government ordinances to support hospital operations during the COVID-19 pandemic
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...
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| Format: | Article |
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BMJ Publishing Group
2021-03-01
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| Series: | BMJ Health & Care Informatics |
| Online Access: | https://informatics.bmj.com/content/28/1/e100248.full |
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| author | Hayley B Gershengorn Monisha C Bhatia Prem Rajendra Warde Samira S Patel Tanira D Ferreira Dipen J Parekh Kymberlee J Manni Bhavarth S Shukla |
| author_facet | Hayley B Gershengorn Monisha C Bhatia Prem Rajendra Warde Samira S Patel Tanira D Ferreira Dipen J Parekh Kymberlee J Manni Bhavarth S Shukla |
| author_sort | Hayley B Gershengorn |
| collection | DOAJ |
| description | 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 schedule is modified according to models that predict the number of new patients with Covid-19 who require admission. This enabled our hospital to coordinate resources to continue to support the community at large. Challenges included the need to frequently adjust or create new models to meet rapidly evolving requirements, communication, and adoption, and to coordinate the needs of multiple stakeholders. The model we created can be adapted to other health systems, provide a mechanism to predict local peaks in cases and inform hospital leadership regarding bed allocation, surgical volumes, staffing, and supplies one for COVID-19 patients within a hospital able to care for the regular non-COVID-19 population.Conclusion Predictive models are essential tools in supporting decision making when coordinating clinical operations during a pandemic. |
| format | Article |
| id | doaj-art-4b7df0facb704741a737c867966b274c |
| institution | OA Journals |
| issn | 2632-1009 |
| language | English |
| publishDate | 2021-03-01 |
| publisher | BMJ Publishing Group |
| record_format | Article |
| series | BMJ Health & Care Informatics |
| spelling | doaj-art-4b7df0facb704741a737c867966b274c2025-08-20T02:11:57ZengBMJ Publishing GroupBMJ Health & Care Informatics2632-10092021-03-0128110.1136/bmjhci-2020-100248Linking prediction models to government ordinances to support hospital operations during the COVID-19 pandemicHayley B Gershengorn0Monisha C Bhatia1Prem Rajendra Warde2Samira S Patel3Tanira D Ferreira4Dipen J Parekh5Kymberlee J Manni6Bhavarth S Shukla7Division of Pulmonary, Critical Care, and Sleep Medicine, Department of Medicine, University of Miami Miller School of Medicine, Miami, Florida, USADepartment of Medicine, Jackson Memorial Hospital, Miami, Florida, USADepartment of Clinical Care Transformation, University of Miami Hospital and Clinics, Miami, Florida, USADepartment of Clinical Care Transformation, University of Miami Hospital and Clinics, Miami, Florida, USADivision of Pulmonary, Critical Care, and Sleep Medicine, Department of Medicine, University of Miami Miller School of Medicine, Miami, Florida, USADepartment of Urology, University of Miami Miller School of Medicine, Miami, Florida, USAUniversity of Miami Hospital and Clinics, Miami, Florida, USADivision of Infectious Diseases, Department of Medicine, University of Miami Miller School of Medicine, Miami, Florida, USAObjectives 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 schedule is modified according to models that predict the number of new patients with Covid-19 who require admission. This enabled our hospital to coordinate resources to continue to support the community at large. Challenges included the need to frequently adjust or create new models to meet rapidly evolving requirements, communication, and adoption, and to coordinate the needs of multiple stakeholders. The model we created can be adapted to other health systems, provide a mechanism to predict local peaks in cases and inform hospital leadership regarding bed allocation, surgical volumes, staffing, and supplies one for COVID-19 patients within a hospital able to care for the regular non-COVID-19 population.Conclusion Predictive models are essential tools in supporting decision making when coordinating clinical operations during a pandemic.https://informatics.bmj.com/content/28/1/e100248.full |
| spellingShingle | Hayley B Gershengorn Monisha C Bhatia Prem Rajendra Warde Samira S Patel Tanira D Ferreira Dipen J Parekh Kymberlee J Manni Bhavarth S Shukla Linking prediction models to government ordinances to support hospital operations during the COVID-19 pandemic BMJ Health & Care Informatics |
| title | Linking prediction models to government ordinances to support hospital operations during the COVID-19 pandemic |
| title_full | Linking prediction models to government ordinances to support hospital operations during the COVID-19 pandemic |
| title_fullStr | Linking prediction models to government ordinances to support hospital operations during the COVID-19 pandemic |
| title_full_unstemmed | Linking prediction models to government ordinances to support hospital operations during the COVID-19 pandemic |
| title_short | Linking prediction models to government ordinances to support hospital operations during the COVID-19 pandemic |
| title_sort | linking prediction models to government ordinances to support hospital operations during the covid 19 pandemic |
| url | https://informatics.bmj.com/content/28/1/e100248.full |
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