Scaffold a SMART-on-FHIR app (SMART App Launch v2 — EHR launch and standalone launch, OAuth2 PKCE, scopes, token handling, fhirContext) so an OpenMed-powered tool can run inside Epic or Cerner/Oracle Health. Covers the .well-known/smart-configuration discovery, authorize/token sequence, scopes like patient/DocumentReference.rs and launch/patient, and fetching clinical notes the app then de-identifies and runs NER on locally with OpenMed. Use when the user wants to embed OpenMed inside an EHR, mentions SMART on FHIR, OAuth2 launch, scopes, Epic/Cerner app, or clinician-facing FHIR app. Pairs adjacent.
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To put an OpenMed-powered tool inside a clinician's EHR (Epic, Cerner/Oracle Health), you build a SMART on FHIR app: a web app the EHR launches with an OAuth2 flow, granting scoped, time-limited access to the patient's FHIR data. The app fetches the clinical notes, then runs OpenMed on-device (de-id + NER) — so PHI is processed locally and only de-identified output, if anything, leaves the browser/host.
Reach for this when the deliverable is a clinician-facing app embedded in an
EHR, or a standalone app authorizing against an EHR's FHIR endpoint. Triggers:
"SMART on FHIR", "EHR launch", "OAuth2 scopes", "Epic/Cerner app",
"embed OpenMed in the chart". For pulling notes at cohort scale (no UI), use
exporting-bulk-fhir instead.
launch_uri?iss=<fhir-base>&launch=<opaque>; you complete OAuth2 and inherit
the current patient/encounter context.Both use SMART App Launch v2: OAuth2 authorization code flow with PKCE
(required in v2), discovered via .well-known/smart-configuration.
1. EHR launch URL:
GET https://app.example/launch?iss=https://ehr.example/fhir&launch=abc123
2. Discover endpoints:
GET https://ehr.example/fhir/.well-known/smart-configuration
-> { "authorization_endpoint": ".../authorize",
"token_endpoint": ".../token",
"code_challenge_methods_supported": ["S256"],
"capabilities": ["launch-ehr","client-public","context-ehr-patient", ...] }
3. Redirect the browser to authorize (PKCE + the launch token):
GET .../authorize?
response_type=code&
client_id=YOUR_CLIENT_ID&
redirect_uri=https://app.example/callback&
scope=launch openid fhirUser patient/DocumentReference.rs patient/Patient.r&
state=RANDOM&
aud=https://ehr.example/fhir&
launch=abc123&
code_challenge=BASE64URL(SHA256(verifier))&
code_challenge_method=S256
4. Callback -> exchange code for token:
POST .../token
grant_type=authorization_code&code=...&redirect_uri=...&
client_id=...&code_verifier=ORIGINAL_VERIFIER
-> { "access_token": "...", "token_type": "Bearer", "expires_in": 3600,
"scope": "patient/DocumentReference.rs ...",
"patient": "Patient-123", "encounter": "Encounter-9",
"id_token": "..." }
5. Call FHIR with the token:
GET https://ehr.example/fhir/DocumentReference?patient=Patient-123&type=clinical-note
Authorization: Bearer <access_token>The token response carries the launch context (patient, sometimes
encounter, and in v2 a fhirContext array). Use patient to scope every
subsequent query.
SMART v2 scopes are <level>/<Resource>.<permissions> where permissions are a
subset of c r u d s (create/read/update/delete/search) — .rs = read +
search. Request the minimum:
| Scope | Why |
|---|---|
launch | EHR launch context (omit for standalone; use launch/patient) |
openid fhirUser | Identify the launching user |
patient/Patient.r | The in-context patient demographics |
patient/DocumentReference.rs | Read + search the patient's clinical notes |
patient/Condition.rs | (optional) reconcile against existing problems |
offline_access | (optional) refresh token for background work |
Prefer patient/… (current-patient) over user/… (everything the user can see)
to keep the blast radius small. Granular v2 scopes (.rs) are stricter than the
v1 .read/.write forms — use them.
Notes arrive as DocumentReference → content.attachment (often base64 or a
url to a Binary). Decode, then process locally:
import base64, openmed
note_b64 = document_reference["content"][0]["attachment"]["data"]
note = base64.b64decode(note_b64).decode("utf-8")
# De-identify on-device before anything else touches it
deid = openmed.deidentify(note, method="replace", policy="hipaa_safe_harbor")
# Clinical NER on the (de-identified or raw, per your IRB) text
entities = openmed.analyze_text(deid.text, model_name="disease_detection_superclinical")
# -> render highlights in the SMART app UI, or export FHIR (exporting-to-fhir)OpenMed models run on-device after a one-time download — no note text is sent to a third party by OpenMed. Keep the access token and any PHI in memory only; do not log them.
DocumentReference notes →
openmed.deidentify → openmed.analyze_text.exporting-to-fhir) → to_bundle (assembling-fhir-bundles) → write back
with a write scope (e.g. patient/Condition.c) if your use case persists
findings. Validate first (validating-us-core).openmed_analyze_text and openmed_deidentify — same on-device guarantees.code_verifier per launch; never reuse.state and aud. Reject the callback if state does not match;
set aud to the FHIR base or the EHR will reject the authorize request.expires_in; use offline_access +
refresh tokens only if you genuinely need background access, and store them
securely (never client-side for confidential clients).scope and degrade gracefully..well-known/smart-configuration: https://hl7.org/fhir/smart-app-launch/conformance.html80da98c
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