The Development and Deployment of a Model for Hospital-level COVID-19 Associated Patient Demand Intervals from Consistent Estimators (DICE)

TL;DR

This paper introduces DICE, a new probabilistic model for hospital demand forecasting during COVID-19, providing consistent prediction intervals to improve decision-making, validated with real and synthetic data.

Contribution

The paper develops and deploys DICE, a novel demand interval forecasting method that is consistent and applicable to various regional forecast types, addressing a key gap in hospital demand prediction.

Findings

DICE provides reliable prediction intervals for hospital demand.

DICE performs well with both perfect and biased regional forecasts.

Empirical and synthetic data evaluations confirm DICE's effectiveness.

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 forecasts of patient demand are commonly available as a Poisson random variable, 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, unbiased regional forecasts. We evaluate its performance on empirical data from a large academic medical center as well as on synthetic data.