How Synthetic Cases Support Product Testing

Fabricated on purpose

Every case in our demonstration library — a new patient review, a case of inadequate control, a suspected intolerance, a polypharmacy scenario, and one built around incomplete intake data — is written from scratch as a synthetic scenario. No real patient information is used anywhere in the product, and none of the outputs are intended to represent a validated clinical judgment about any real situation.

That's a constraint we chose deliberately, not one imposed on us by circumstance. Working with synthetic cases early lets the interaction design, the explainability layer, and the review workflow all get built and tested without any of the handling, governance, and access-control complexity that real patient data would require at this stage.

Designing for coverage, not just realism

Because the cases are authored rather than sampled from real records, we can deliberately construct scenarios that stress specific parts of the workflow: one case is built to be data-complete and straightforward, precisely so it can act as a baseline; another is built to be data-incomplete, precisely so the abstention path gets a genuine test. That kind of intentional coverage is harder to guarantee when working only with whatever real cases happen to arrive.

This mirrors a common practice in software testing more broadly — engineered test fixtures alongside real-world data — applied here to a clinical-adjacent workflow where the fixtures also have to read as plausible and professionally coherent, not just structurally valid.

What synthetic testing does not establish

It's worth being direct about the limits of this approach. Testing a workflow against synthetic cases demonstrates that the interface, the drafting layer and the review gate behave as intended for the scenarios we've authored. It does not, on its own, establish clinical accuracy, Canadian regulatory alignment, or real-world performance, because none of those can be assessed on fabricated data.

We treat synthetic-case testing as one input among several needed before any broader validation claim would be appropriate, and we're careful not to let a well-behaved demo be mistaken for that broader validation.

How the library keeps growing

As the product evolves, we expect the synthetic case library to grow alongside it — new scenario types as new workflow features are added, and revisions to existing cases as the review screens change. The library is treated as living test infrastructure and a teaching tool for the demonstration, not a fixed showcase.