Patients couldn't find relevant clinical trials without physician involvement.
Legacy search relied on outdated text matching that missed medical synonyms, and the system had to rank 65,000+ active trials within sub-5-second response times. The team had originally tried to maintain a condition mapping by hand. That approach covered less than 5% of their target domains, and doing it properly would have required an estimated 5-10 full-time people.
PressW built ClinicalNet a custom NLP search engine for patient-to-trial matching, with automated medical-condition hierarchy mapping in place of a hand-built taxonomy, a multi-level search architecture that returns instant results while deeper matching runs in the background, and models fine-tuned to clinical-trial eligibility criteria.
Automated matching removed 1,000+ hours of manual trial-search work.
The engine indexes and ranks 65,000+ active trials across more than a million conditions.
Patients get relevant matches in under five seconds, with core results in under one.
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