Background

Regular patient screenings in public healthcare are, in principle, one of the more predictable parts of the system: people of a certain age are invited on a known schedule, and population data for a wellbeing services county is generally available. In practice, however, workforce planning for the units running these screenings tends to be reactive rather than forecast-driven, and the flow of patients into and through the screening process over time is rarely modeled explicitly. At the same time, the demographics of wellbeing services counties are shifting, which changes both how many people will be eligible for screenings and how that number will develop in the coming years.

Challenge

How can we forecast how many people will take part in regular patient screenings, and translate that into the workforce needed for the specific unit running them? The project will look at how to model the flow of patients into screenings over time, using the fact that screening demand is largely predictable: eligible age groups are known, county population and demographic data is available, and historical no-show rates can be factored in. It will also explore how changing demographics — population growth, aging, or migration within a county — should be built into the forecast rather than treated as a fixed baseline, and what a useful forecast should show, including scenario views (e.g. tied to regional population growth projections), the workforce implications of different scenarios, a simple way to weigh the cost/benefit of adding staff against the cost of a growing queue, and how the system should behave once demand or a queue approaches zero.

Goal
1. Build a forecasting model that estimates screening participation from population age structure, invitation schedules, and historical no-show rates.

2. Model the flow of patients into and through screenings over time to estimate the workforce needed for a specific unit.

3. Incorporate demographic change and population growth scenarios into the forecast, rather than assuming a static population.

4. Explore a simple recruitment ROI view that weighs the cost of adding workforce against the cost of a growing patient queue, and consider what happens once a queue approaches zero.

5. Explore how forecasted queue status could be communicated back to patients, so they have a better sense of what to expect.

Scope

The project is not aimed at a production forecasting or workforce-planning system, but at validating whether screening participation and the resulting workforce needs can be usefully forecast from available population and historical data. The scope is limited to one or two representative screening programs with simplified demographic and queue data, so most effort goes into testing the forecasting and scenario-modeling approach rather than building a fully integrated planning tool.