Hospital Capacity Forecasting is designed to help operations teams examine expected demand alongside staffed capacity. It combines operational data, forecasting models, and scenario tools in a dashboard that shows prediction ranges and the freshness of its inputs.
Planners can explore where pressure may develop, compare assumptions, and review model performance over time. The project supports operational decisions with visible evidence while leaving staffing and service decisions with accountable teams.
Planning teams need to look beyond current occupancy when preparing for the next shift or week. A forecast is difficult to interpret if its uncertainty, data delays, or recent performance are hidden behind a single number.
Showing these details together helps planners judge how much weight to give a forecast. Comparing scenarios against staffed capacity also makes the assumptions behind a plan easier to discuss and record.
The platform ingests approved operational data, checks completeness and freshness, and produces forecasts for agreed time horizons. Results are shown with prediction intervals and compared against observed demand and documented benchmark models.
Operations leads can adjust capacity assumptions to explore different scenarios and save the assumptions behind their plans. Monitoring highlights changes in data or model performance, with a route back to a benchmark during review. Forecasts inform planning; they do not automatically assign treatment priority or change staffing.
Connect agreed operational sources and surface missing or delayed feeds so planners can see whether the information supporting a forecast is current.
Present expected demand alongside uncertainty intervals and observed outcomes, with views tailored to the selected planning horizon and participating service.
Compare forecasts with staffed capacity and test user-entered assumptions. Retain those assumptions with the scenario so the plan can be reviewed.
Compare performance with documented benchmarks, monitor deterioration across sites, and support a controlled return to the benchmark while issues are investigated.
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