The development and deployment of a model for hospital-level COVID-19 associated patient demand intervals from consistent estimators (DICE)

Yang, Linying; Zhang, Teng; Scheinker, David

doi:10.1007/s10729-021-09555-3

Cited by 3 publications

(3 citation statements)

References 38 publications

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“…In order to ensure timely turn-around, we have only taken papers which we could accept with minor revisions; as a result, we have had to reject many promising manuscripts. We are pleased to have been able to accept 12 papers [1][2][3][4][5][6][7][8][9][10][11][12]. We are particularly proud to be able to highlight that "From predictions to prescriptions: A data-driven response to COVID-19" by Dimitris J Bertsimas and colleagues [1] has been awarded the Pierskalla Best Paper Award for 2020 by the INFORMS Health Applications Society.…”

Section: Special Issuementioning

confidence: 99%

Introduction to the special issue: Management Science in the Fight Against Covid-19

Morton

Bish

Megiddo

et al. 2021

Health Care Manag Sci

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Section: Special Issuementioning

confidence: 99%

Introduction to the special issue: Management Science in the Fight Against Covid-19

Morton

Bish

Megiddo

et al. 2021

Health Care Manag Sci

View full text Add to dashboard Cite

“…However, very few methods for epidemic transmission forecasting can provide a probabilistic forecast (Soliman et al. , 2019; Yang et al. , 2021).…”

Section: Introductionmentioning

confidence: 99%

“…However, very few methods for epidemic transmission forecasting can provide a probabilistic forecast (Soliman et al, 2019;Yang et al, 2021). It is necessary to compare stochastic epidemic models with probabilistic forecasting.…”

Section: Introductionmentioning

confidence: 99%

The value of probabilistic forecasting in emergency medical resource planning under uncertainty

Chen

2022

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PurposeMost epidemic transmission forecasting methods can only provide deterministic outputs. This study aims to show that probabilistic forecasting, in contrast, is suitable for stochastic demand modeling and emergency medical resource planning under uncertainty.Design/methodology/approachTwo probabilistic forecasting methods, i.e. quantile regression convolutional neural network and kernel density estimation, are combined to provide the conditional quantiles and conditional densities of infected populations. The value of probabilistic forecasting in improving decision performances and controlling decision risks is investigated by an empirical study on the emergency medical resource planning for the COVID-19 pandemic.FindingsThe managerial implications obtained from the empirical results include (1) the optimization models using the conditional quantile or the point forecasting result obtain better results than those using the conditional density; (2) for sufficient resources, decision-makers' risk preferences can be incorporated to make tradeoffs between the possible surpluses and shortages of resources in the emergency medical resource planning at different quantile levels; and (3) for scarce resources, the differences in emergency medical resource planning at different quantile levels greatly decrease or disappear because of the existing of forecasting errors and supply quantity constraints.Originality/valueVery few studies concern probabilistic epidemic transmission forecasting methods, and this is the first attempt to incorporate deep learning methods into a two-phase framework for data-driven emergency medical resource planning under uncertainty. Moreover, the findings from the empirical results are valuable to select a suitable forecasting method and design an efficient emergency medical resource plan.

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

Johnson,

Naik,

Chan

et al. 2023

Health Care Manag Sci

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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.

show abstract

The development and deployment of a model for hospital-level COVID-19 associated patient demand intervals from consistent estimators (DICE)

Cited by 3 publications

References 38 publications

Introduction to the special issue: Management Science in the Fight Against Covid-19

Introduction to the special issue: Management Science in the Fight Against Covid-19

The value of probabilistic forecasting in emergency medical resource planning under uncertainty

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

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