Linking prediction models to government ordinances to support hospital operations during the COVID-19 pandemic
| Autor: | Hayley B. Gershengorn, Monisha C Bhatia, Dipen J. Parekh, Bhavarth Shukla, Kymberlee J Manni, Tanira Ferreira, Samira Patel, Prem R. Warde |
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| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
Computer science
Computer applications to medicine. Medical informatics Population R858-859.7 Staffing Health Informatics information systems Efficiency Organizational Health informatics 03 medical and health sciences 0302 clinical medicine Resource (project management) Health Information Management BMJ Health Informatics Information system medical informatics Humans Operations management Discrete event simulation education Pandemics 030304 developmental biology Original Research 0303 health sciences education.field_of_study business.industry SARS-CoV-2 COVID-19 information management Schedule (project management) information science Computer Science Applications 030220 oncology & carcinogenesis Models Organizational Health Resources business Emergency Service Hospital Predictive modelling Forecasting |
| Zdroj: | BMJ Health & Care Informatics BMJ Health & Care Informatics, Vol 28, Iss 1 (2021) |
| ISSN: | 2632-1009 |
| Popis: | 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 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.ConclusionPredictive models are essential tools in supporting decision making when coordinating clinical operations during a pandemic. |
| Databáze: | OpenAIRE |
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