Forecasting ward-level bed requirements to aid pandemic resource planning: Lessons learned and future directions

Michael R. Johnson (), Hiten Naik, Wei Siang Chan, Jesse Greiner, Matt Michaleski, Dong Liu, Bruno Silvestre and Ian P. McCarthy ()
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Michael R. Johnson: Beedie School of Business, Simon Fraser University
Hiten Naik: University of British Columbia
Wei Siang Chan: University of British Columbia
Jesse Greiner: Providence Health Care
Matt Michaleski: Vancouver General Hospital
Dong Liu: University of British Columbia
Bruno Silvestre: University of Manitoba
Ian P. McCarthy: Beedie School of Business, Simon Fraser University

Health Care Management Science, 2023, vol. 26, issue 3, No 6, 477-500

Abstract: Abstract During the COVID-19 pandemic, there has been considerable research on how regional and country-level forecasting can be used to anticipate required hospital resources. We add to and build on this work by focusing on ward-level forecasting and planning tools for hospital staff during the pandemic. We present an assessment, validation, and deployment of a working prototype forecasting tool used within a modified Traffic Control Bundling (TCB) protocol for resource planning during the pandemic. We compare statistical and machine learning forecasting methods and their accuracy at one of the largest hospitals (Vancouver General Hospital) in Canada against a medium-sized hospital (St. Paul’s Hospital) in Vancouver, Canada through the first three waves of the COVID-19 pandemic in the province of British Columbia. Our results confirm that traditional statistical and machine learning (ML) forecasting methods can provide valuable ward-level forecasting to aid in decision-making for pandemic resource planning. Using point forecasts with upper 95% prediction intervals, such forecasting methods would have provided better accuracy in anticipating required beds on COVID-19 hospital units than ward-level capacity decisions made by hospital staff. We have integrated our methodology into a publicly available online tool that operationalizes ward-level forecasting to aid with capacity planning decisions. Importantly, hospital staff can use this tool to translate forecasts into better patient care, less burnout, and improved planning for all hospital resources during pandemics.

Keywords: Forecasting; Machine learning; COVID-19; Ward-level forecasting; Traffic Control Bundling; Pandemic resource planning (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1007/s10729-023-09639-2

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