The document discusses predicting patient discharge from hospital wards with no real-time clinical data. It presents a random forest model using features like ward admissions/discharges, patient characteristics, and time series patterns. The random forest model improved discharge predictions over baseline ARIMA and naive models, with a 25% reduction in error versus naive forecasts. Seasonality like day-of-week was found to influence predictions, suggesting administrative factors impact discharges. The proposed commonly available data-driven approach could integrate into hospital systems to help manage bed occupancy.