trial-eligibility-agent

name	trial-eligibility-agent
description	Parse trial protocols and patient data to produce criterion-level MET/NOT/UNKNOWN determinations with evidence and gaps for clinical trial screening tasks.

At-a-Glance

• description (10-20 chars): Trial triage hub
• keywords: eligibility, ClinicalTrials, FHIR, evidence, gaps
• measurable_outcome: Produce a MET/NOT/UNKNOWN matrix with supporting citations for ≥90% of inclusion/exclusion criteria within 5 minutes per trial request.

Inputs

• trial_id (NCT or sponsor ID) plus protocol text if not public.
• patient_summary narrative and optional patient_structured FHIR bundle.
• Declare data sources used (notes, labs, imaging, meds) to show provenance.

Outputs

1. Structured table (JSON recommended) listing each criterion id/text with status, evidence snippet, and confidence.
2. Overall recommendation (potentially_eligible, not_eligible, needs_more_information).
3. Data gap checklist covering missing labs/imaging/biomarkers.

Workflow

1. Acquire protocol: Pull eligibility text from ClinicalTrials.gov or sponsor PDF.
2. Normalize criteria: Break into atomic checks with AND/OR logic and thresholds.
3. Extract patient facts: Map narrative + FHIR data into canonical features (age, labs, ECOG, biomarkers).
4. Evaluate: Assign MET/NOT/UNKNOWN with cited evidence for each criterion, flag missing context explicitly.
5. Summarize: Present recommendation and highlight gating unknowns plus next-best actions.

Guardrails

• Never claim enrollment decisions; mark outputs as advisory.
• Cite direct patient evidence for every MET/NOT call; default to UNKNOWN rather than guessing.
• Respect PHI handling expectations—avoid storing raw notes outside secure paths.

Tooling & References

• Use README.md for API snippets (FHIR parsing, JSON schema) and dependency versions.
• Pair with Clinical/Trial_Matching/TrialGPT when retrieval/ranking is also needed.