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 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.