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Hospital capacity depends on how patients enter, move through and leave the service, as well as the resources available to support them. This platform design brings operational feeds, demand forecasts and scenario tools into a shared planning environment. The programme includes data alignment, historical evaluation and a shadow-running period before use in daily operations.
Approved admission, transfer, discharge and staffing feeds are reconciled into an operational view. Forecasts show ranges, input freshness and comparison with documented baselines. Planners explore scenarios involving staffed beds, service demand and discharge dependencies, retaining the assumptions behind their choices. Model monitoring supports review and fallback when performance changes. The platform informs operational planning; staff authorise resource changes and clinical prioritisation.
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.
Aligned event definitions and quality checks connect hospital flow with resource availability.
Prediction ranges and baseline comparisons make uncertainty visible to planning teams.
User-defined assumptions explore capacity constraints and dependencies across the care journey.
Past periods and observed operations provide evidence before forecasts influence daily planning.
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