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Aurenex

Clinical AI

Forecasting enrolment without fooling yourself

Most enrolment forecasts are optimistic by construction. A few structural fixes help.

Marcus Lindqvist · Head of Clinical Data Science · 6 min read

Enrolment forecasts inherit the optimism of the assumptions used to build the study plan, then compound it.

Calibrating against historical realised curves — including the studies nobody likes to cite — is the single highest-value correction available.

We publish forecast intervals rather than points, and score our own past forecasts publicly within the programme. Accountability improves calibration faster than any model change.

  • forecasting
  • clinical
  • statistics

Next step

Bring a real problem. We will bring the architecture.

Thirty minutes with our architects is usually enough to tell whether a programme is ready for agents, or whether the data foundation needs work first.