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

Linying Yang (), Teng Zhang, Peter Glynn and David Scheinker
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Linying Yang: Stanford University
Teng Zhang: Stanford University
Peter Glynn: Stanford University
David Scheinker: Stanford University

Health Care Management Science, 2021, vol. 24, issue 2, No 11, 375-401

Abstract: Abstract Hospitals commonly project demand for their services by combining their historical share of regional demand with forecasts of total regional demand. Hospital-specific forecasts of demand that provide prediction intervals, rather than point estimates, may facilitate better managerial decisions, especially when demand overage and underage are associated with high, asymmetric costs. Regional point forecasts of patient demand are commonly available, e.g., for the number of people requiring hospitalization due to an epidemic such as COVID-19. However, even in this common setting, no probabilistic, consistent, computationally tractable forecast is available for the fraction of patients in a region that a particular institution should expect. We introduce such a forecast, DICE (Demand Intervals from Consistent Estimators). We describe its development and deployment at an academic medical center in California during the ‘second wave’ of COVID-19 in the Unite States. We show that DICE is consistent under mild assumptions and suitable for use with perfect, biased and unbiased regional forecasts. We evaluate its performance on empirical data from a large academic medical center as well as on synthetic data.

Keywords: COVID-19; Hospital-level forecast; Prediction interval; Parametric bootstrap; Moment method; Prediction bias (search for similar items in EconPapers)
Date: 2021
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Citations: View citations in EconPapers (2)

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DOI: 10.1007/s10729-021-09555-3

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