The right trial should find the patient.

Somewhere right now there is a patient whose disease matches a trial perfectly — the exact mutation, the right stage, an open slot at a site twenty minutes away. They will never hear about it. Their record sits in a system no sponsor can see, behind a privacy wall that — rightly — never comes down. The trial, meanwhile, slips another quarter behind and burns millions while it waits.

Often the patient exists but the protocol shut them out — a threshold set a point too tight, a washout a week too long, an exclusion no data supports. So the work starts before enrollment does: GhostTrials lets you author the protocol and de-risk its eligibility against real de-identified counts — so the criteria fit the patients who are actually out there, not an idealized cohort that isn't.

And it is not a data problem you solve by moving data. The data must stay where it is. So GhostTrials brings the question to the data instead of the data to the question: both the de-risking counts and the patient match run inside each hospital, and only de-identified signals ever leave. Privacy isn't a constraint we tolerate — it's the architecture.

And because eligibility is genuinely hard — biomarkers conflict, staging is missing, an assessment is a week stale — we refuse to let a model guess. Every ambiguous case goes to a person, with the protocol citation and the evidence in front of them. The machine does the search at scale; the human makes the call that matters. That is what makes this safe to put near real patients.

Get this right and the second-order effects are enormous: protocols are designed to enroll, trials finish on time, therapies reach market years sooner, sites stay solvent, and — the only number that ultimately matters — more patients get the treatment that was always meant for them.