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]".

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,
)