extract-clinical-entities-to-fhir
Extract clinical entities from synthetic or already de-identified text with OpenMed and map them into deterministic FHIR R4 resources and a Bundle. Use when an agent must turn local clinical NER output into Conditions, MedicationStatements, Observations, or other FHIR resources without inventing terminology codes.
npx skills add maziyarpanahi/openmed --skill extract-clinical-entities-to-fhir --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# Extract clinical entities to FHIR Separate extraction from clinical coding. OpenMed finds spans and supplies the mechanical FHIR builders; the application decides which resource type and status are clinically appropriate. ## Procedure 1. Keep the source synthetic, or de-identify it inside the trusted boundary before extraction. 2. Run `openmed.analyze_text` with the task-appropriate clinical model. 3. Filter predictions by label and confidence; preserve offsets in a PHI-safe audit record. 4. Map each accepted span to the correct FHIR resource type. 5. Add terminology codes only from a user-approved mapping or terminology service. Never invent a code. 6. Assemble resources with `to_bundle` and validate against the target profile. ## Runnable synthetic example Install the model runtime first with `python -m pip install "openmed[hf]"`. ```python import json from openmed import analyze_text from openmed.clinical.exporters.fhir import to_bundle note = "Assessment: type 2 diabetes mellitus is stable on metformin." result = analyze_text( note, model_name="disease_detection_superclinical", confidence_threshold=0.5, ) resources = [{"resourceType": "Patient", "id": "synthetic-patient"}] fo
- Procedure
- Runnable synthetic example
- Safety checks
- Repository example
What does the extract-clinical-entities-to-fhir skill do?
Extract clinical entities from synthetic or already de-identified text with OpenMed and map them into deterministic FHIR R4 resources and a Bundle. Use when an agent must turn local clinical NER output into Conditions, MedicationStatements, Observations, or other FHIR resources without inventing terminology codes.
How do I install it?
Run `npx skills add maziyarpanahi/openmed --skill extract-clinical-entities-to-fhir --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From maziyarpanahi/openmed, a repository with 4,851 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.