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Forecasting ward-level bed requirements to aid pandemic resource planning: Lessons learned and future directions.
Johnson, Michael R; Naik, Hiten; Chan, Wei Siang; Greiner, Jesse; Michaleski, Matt; Liu, Dong; Silvestre, Bruno; McCarthy, Ian P.
  • Johnson MR; Beedie School of Business, Simon Fraser University, Vancouver, Canada. michael_johnson@sfu.ca.
  • Naik H; Department of Medicine, University of British Columbia, Vancouver, Canada.
  • Chan WS; Land and Food Systems, University of British Columbia, Vancouver, Canada.
  • Greiner J; Department of Medicine, Providence Health Care, Vancouver, Canada.
  • Michaleski M; Department of Medicine, Vancouver General Hospital, Vancouver, Canada.
  • Liu D; Land and Food Systems, University of British Columbia, Vancouver, Canada.
  • Silvestre B; Asper School of Business, University of Manitoba, Winnipeg, Canada.
  • McCarthy IP; Beedie School of Business, Simon Fraser University, Vancouver, Canada.
Health Care Manag Sci ; 2023 May 18.
Article in English | MEDLINE | ID: covidwho-2323892
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.
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Full text: Available Collection: International databases Database: MEDLINE Type of study: Prognostic study Language: English Journal subject: Health Services Year: 2023 Document Type: Article Affiliation country: S10729-023-09639-2

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Full text: Available Collection: International databases Database: MEDLINE Type of study: Prognostic study Language: English Journal subject: Health Services Year: 2023 Document Type: Article Affiliation country: S10729-023-09639-2