Most recruitment delays aren't about coordination, they're about information. Roughly 80% of trials miss their enrollment targets, and 11% of sites enroll zero patients. The problem is structural: eligibility criteria are written in natural language, patient data sits in fragmented EHR systems, and matching the two manually doesn't scale.
Two technical shifts are converging to change this. First, FHIR (Fast Healthcare Interoperability Resources) is now a mandatory interoperability standard for certified U.S. EHRs, exposing patient data through standardized APIs. Second, large language models can now parse complex eligibility criteria and reason over unstructured clinical notes with accuracy approaching human reviewers.
Recent peer-reviewed studies show GPT-4 achieving 87% accuracy matching patients to trials, processing records in under three minutes per patient at a cost below one dollar. The architecture is two-layered: FHIR APIs first narrow the candidate pool using structured data filters, then LLMs evaluate clinical notes against full eligibility criteria.
This isn't speculative. FHIR adoption in outpatient settings jumped from 49% in 2021 to 64% in 2024, and tested Bulk FHIR exports now handle over 12,000 resources per minute at some sites. The infrastructure is maturing. What matters now is how sites and sponsors build on top of it.