Forecasting Emergency Department Capacity Constraints for COVID Isolation Beds
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.