Evidence as terminal output.

At the end of every precision medicine workflow, someone has to prove it worked.

A trial needs a regulatory submission. A diagnostic needs an FDA filing. A therapy needs compliance documentation. The science produced the result. The evidence proves it's real.

Consider an FDA submission for a blood-based cancer screening test. The evidence package includes not just the clinical trial results, but the complete analytical validation: specimen handling protocols, assay performance characteristics, variant calling accuracy, and the statistical framework linking ctDNA signal to cancer detection. Every piece of that evidence must be traceable back to the original execution. Any gap in the chain, a missing specimen log, an unversioned algorithm change, an undocumented data transformation, could delay or block approval.

Evidence is the end product of precision medicine. Not the assay. Not the algorithm. The proof that everything was done correctly, that it can be reproduced, and that it holds up to scrutiny across organizations.

Evidence by construction

Today, evidence is assembled after the fact. Teams reconstruct lineage, audit trails, and provenance from execution that already happened. They pull data from logs, chase down approvals, and stitch together documentation that should have been generated automatically.

Evidence is expensive because it's almost always assembled after the fact.

That reconstruction is where the cost lives. It's slow, error-prone, and it happens on every submission.

Evidence must be reproducible, auditable, and defensible across organizational boundaries. It has the strictest requirements and the longest memory. When it's built properly the first time, the ecosystem compounds. When it's reconstructed, the ecosystem stalls.