Forecasting Emergency Department Capacity Constraints for COVID Isolation Beds

TL;DR

This paper presents an hourly COVID-19 emergency department capacity forecasting tool using Gaussian Process Regressions, enabling hospitals to proactively manage resources during capacity constraints.

Contribution

The study introduces a novel hourly forecasting model with GPRs for hospital capacity, demonstrating superior performance and robustness over traditional methods.

Findings

GPRs achieved an average R-squared of 82% for point predictions.

Classification of capacity tiers had an average precision of 82% and recall of 74%.

The model is robust to dataset shifts during 2020.

Abstract

Predicting patient volumes in a hospital setting is a well-studied application of time series forecasting. Existing tools usually make forecasts at the daily or weekly level to assist in planning for staffing requirements. Prompted by new COVID-related capacity constraints placed on our pediatric hospital's emergency department, we developed an hourly forecasting tool to make predictions over a 24 hour window. These forecasts would give our hospital sufficient time to be able to martial resources towards expanding capacity and augmenting staff (e.g. transforming wards or bringing in physicians on call). Using Gaussian Process Regressions (GPRs), we obtain strong performance for both point predictions (average R-squared: 82%) as well as classification accuracy when predicting the ordinal tiers of our hospital's capacity (average precision/recall: 82%/74%). Compared to traditional regression approaches, GPRs not only obtain consistently higher performance, but are also robust to the dataset shifts that have occurred throughout 2020. Hospital stakeholders are encouraged by the strength of our results, and we are currently working on moving our tool to a real-time setting with the goal of augmenting the capabilities of our healthcare workers.