Fetching Fhir Resources

What it does

When your agent uses it

• DocumentReference
• DiagnosticReport

Example prompts

• “Use the fetching-fhir-resources skill to fetch and pages FHIR R4 resources (Patient, DocumentReference, DiagnosticReport, Observation, Condition)…”
• “/fetching-fhir-resources”

Requirements

• Python 3

Workflow steps

6 steps, taken from the first numbered list in SKILL.md.

1. 1Authenticate. Most production FHIR endpoints use SMART-on-FHIR OAuth2
2. 2Search narrowly. Filter by patient, category, type (LOINC),
3. 3Page via Bundle.link[next] until exhausted. Never assume one page.
4. 4Extract narrative: DocumentReference.content.attachment and
5. 5De-identify → analyze each narrative with OpenMed.
6. 6Rejoin results to subject.reference (patient) and context.encounter

What it can do on your machine

Read from SKILL.md and the folder at commit 34d7b8c. It shows what the files ask for, not the result of running them.

• Tool permissions

  Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

  From allowed-tools in the SKILL.md frontmatter.

• Runs code

  No scripts in the folder and no shell commands in SKILL.md (its code samples are python).

  From the folder's file list and the shell code blocks in SKILL.md.

• Network

  Hosts in commands or code, which the agent is likely to contact:

  • hospital.org

  Also links to:

  • hl7.org

  From URLs in SKILL.md, links to its own repository left out.

• Credentials

  Names no API keys, tokens, secrets or passwords.

  From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Fetching Fhir Resources loads about 1.9k tokens when it runs. Until then it costs about 178 tokens; SKILL.md has 513 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it

~178

When it runs · the whole SKILL.md, loaded when a task matches

~1.9k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from maziyarpanahi/openmed at commit 34d7b8c, republished under its Apache-2.0 licence (© maziyarpanahi). 513 words, ~1,900 tokens.

name

fetching-fhir-resources

description

Fetches and pages FHIR R4 resources (Patient, DocumentReference, DiagnosticReport, Observation, Condition) from a FHIR REST server, decodes base64 attachments, and extracts clinical narrative for OpenMed. Use before OpenMed processing when pulling charts from an EHR FHIR API (Epic, Cerner/Oracle, HAPI, or any US Core server) and you need the note text de-identified and analyzed, then results rejoined by patient. Hand narrative to openmed.deidentify and openmed.analyze_text; openmed.interop.fhir_operations implements a $de-identify operation over Bundles. Trigger keywords: FHIR, R4, US Core, DocumentReference, DiagnosticReport, Bundle, _revinclude, presentedForm, base64, EHR API.

license

Apache-2.0

metadata.project

OpenMed

metadata.category

data-ingestion

metadata.pairs

before

metadata.version

1.0

GET /Patient?identifier=http://hospital.org/mrn|12345
GET /DocumentReference?patient=Patient/abc&category=clinical-note&_count=50
GET /DiagnosticReport?patient=Patient/abc&_revinclude=Observation:related

import base64
import requests
import openmed

BASE = "https://fhir.example.org/r4"
HEADERS = {"Accept": "application/fhir+json", "Authorization": "Bearer <token>"}

def iter_bundle(url, params=None):
    """Yield resources across all pages following Bundle.link[next]."""
    while url:
        bundle = requests.get(url, params=params, headers=HEADERS, timeout=30).json()
        for entry in bundle.get("entry", []):
            yield entry.get("resource", {})
        params = None  # next links are fully-qualified
        url = next(
            (l["url"] for l in bundle.get("link", []) if l.get("relation") == "next"),
            None,
        )

def attachment_text(att):
    """Decode a FHIR Attachment to text (handles base64 and inline text/plain)."""
    if att.get("data"):
        raw = base64.b64decode(att["data"])
        if att.get("contentType", "").startswith("text/"):
            return raw.decode("utf-8", "replace")
        return ""  # PDF/RTF: route to OpenMed multimodal/OCR intake instead
    return ""

# Pull a patient's clinical notes and analyze each.
for doc in iter_bundle(f"{BASE}/DocumentReference",
                       {"patient": "Patient/abc",
                        "category": "clinical-note", "_count": 50}):
    for content in doc.get("content", []):
        text = attachment_text(content.get("attachment", {}))
        if not text.strip():
            continue
        deid = openmed.deidentify(text, method="replace", policy="hipaa_safe_harbor")
        result = openmed.analyze_text(deid.text, output_format="dict")
        patient_ref = doc.get("subject", {}).get("reference")  # rejoin key

© maziyarpanahi, Apache-2.0. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

Just SKILL.md in skills/fetching-fhir-resources of maziyarpanahi/openmed.

Open the folder on GitHubat commit 34d7b8c