Prospective comparison of econometric, machine learning, and foundation models for forecasting emergency department boarding patients.

Accurate short-term forecasts of boarding volumes can enable proactive resource management.

Using data from two UC San Diego Health EDs (October 2022-October 2024), models incorporated prior boarding volumes, scheduled surgeries, hospital census, and expert-selected covariates.

Vector autoregression alone performed robustly and outperformed baseline at both sites.