What if you could predict patient enrollment before spending the budget?

Clinical trial recruitment is often treated as a forecasting problem after the campaign has already started.

CT SCAN is trying to change that.

The clinical trial enrollment company has developed Predictive Enrollment Engineering™, an approach designed to estimate what it will actually cost to enroll a patient before recruitment begins.

At the center of the model is DYNO Ai™.

Using historical advertising and enrollment data, the platform works backward from the study’s enrollment goal to estimate:

• the advertising budget likely to be required
• expected funnel performance
• projected cost per enrolled patient
• how many patients may need to enter the funnel to reach the final target

Built on real enrollment data

CT SCAN says DYNO Ai™ is built on nearly $10 million in historical advertising and enrollment data and has demonstrated approximately 80% predictive accuracy across trials in multiple therapeutic areas and indications.

But the model does not stop at prediction.

Once the enrollment plan is established, CT SCAN manages the recruitment process – from advertising and initial patient contact to prequalification, follow-up, and appointment scheduling.

The idea is simple: instead of launching a recruitment campaign and discovering the economics along the way, sponsors and research sites can start with a clearer picture of what enrollment may require.

In an industry where recruitment delays can quickly affect timelines and budgets, that shift from estimation to prediction could make enrollment planning far more measurable.

Learn more about CT SCAN and DYNO Ai™:
https://dyno.clinicaltrialscan.ai