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Exporting To Fhir

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maziyarpanahi
exporting-to-fhir

Convert OpenMed NER output (entities from openmed.analyze_text) into FHIR R4 resources — Condition, MedicationStatement, Observation — using OpenMed's built-in FHIR R4 export helpers in openmed.clinical.exporters. Covers the verified CodeableConcept builder (coding, codeable_concept, system_uri), deterministic fullUrl references, and OperationOutcome reporting. Use after running OpenMed NER when the user wants standards-conformant FHIR JSON, mentions FHIR, Condition/Observation/MedicationStatement, CodeableConcept, RxNorm/LOINC/ICD-10/SNOMED coding, or interoperability with an EHR. Pairs after extracting-clinical-entities; feeds assembling-fhir-bundles and validating-us-core.

Overview

Publishermaziyarpanahi
Repositoryopenmed
Skill nameexporting-to-fhir
Stars
5.3K
Forks
677
Bundled files
Instructions only
LicenseApache-2.0
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by maziyarpanahi on GitHub. Read the source before you install it.

Installation

Install the Exporting To Fhir AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/maziyarpanahi/openmed.git /tmp/openmed
mkdir -p .claude/skills
cp -r /tmp/openmed/skills/exporting-to-fhir .claude/skills/exporting-to-fhir
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Exporting To Fhir in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Exporting To Fhir on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Exporting To Fhir is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Exporting to FHIR

OpenMed's NER (openmed.analyze_text) returns spans — text, label, offsets, confidence. To make those spans interoperable you wrap each clinically relevant span in a FHIR R4 resource (Condition, MedicationStatement, Observation, ...) carrying a coded CodeableConcept. OpenMed ships the mechanical R4 export helpers for this in openmed.clinical.exporters; you own the small amount of clinical mapping (which span becomes which resource).

When to use

Use this after NER, when the consumer is a FHIR system (an EHR, a registry, a data lake on FHIR). Reach for it when the user says "export to FHIR", "make a Condition/Observation", "build a CodeableConcept", or needs RxNorm/LOINC/ICD-10/ SNOMED-coded resources. For packaging many resources into one transaction Bundle, hand off to assembling-fhir-bundles. To check the result against US Core, hand off to validating-us-core.

What OpenMed gives you (verified API)

OpenMed deliberately ships the purely mechanical pieces and leaves clinical judgement to you. The verified entry points:

python
# CodeableConcept builder — openmed/clinical/exporters/codeable_concept_simple.py
from openmed.clinical.exporters.codeable_concept_simple import (
    system_uri,        # vocab id -> canonical HL7 system URI
    coding,            # (system, code, display) -> Coding dict
    codeable_concept,  # [Coding, ...] -> CodeableConcept dict (deterministic order)
)

# Bundle + reference + OperationOutcome — openmed/clinical/exporters/fhir/
from openmed.clinical.exporters.fhir import (
    to_bundle,                 # [resource, ...] -> R4 transaction Bundle
    deterministic_fullurl,     # (doc_id, index) -> stable urn:uuid
    OperationOutcomeIssue,     # issue dataclass
    to_operation_outcome,      # [issue, ...] -> OperationOutcome
    from_validation_result,    # validator result -> OperationOutcome
)

system_uri knows these vocabularies out of the box: rxnorm, icd-10-cm, loinc, snomed, hpo, mesh (and passes through any http(s):// URI unchanged). It is the single source of truth for vocab-id → system-URI mapping.

There is no to_condition() / to_observation() magic function. You build the resource shell (a small dict) and drop a codeable_concept(...) into its coded slot. That is by design: the resource type and clinical status are decisions OpenMed will not make for you.

Quick start: entity → Condition

python
import openmed
from openmed.clinical.exporters.codeable_concept_simple import coding, codeable_concept

# 1) NER (synthetic note — no real PHI)
result = openmed.analyze_text(
    "Assessment: type 2 diabetes mellitus, stable.",
    model_name="disease_detection_superclinical",
)
# result.entities -> EntityPrediction(text, label, confidence, start, end)
span = result.entities[0]            # e.g. text="type 2 diabetes mellitus"

# 2) Ground to a code OUT OF PROCESS (your terminology server / mapping table).
#    OpenMed bundles no restricted vocab — see querying-terminology-service.
icd_code, snomed_code = "E11.9", "44054006"

# 3) Build the CodeableConcept with OpenMed's builder
cc = codeable_concept(
    [
        coding("snomed", snomed_code, "Diabetes mellitus type 2"),
        coding("icd-10-cm", icd_code, "Type 2 diabetes mellitus without complications"),
    ],
    text=span.text,
)

# 4) Assemble the resource shell yourself
condition = {
    "resourceType": "Condition",
    "id": "cond-1",
    "clinicalStatus": {
        "coding": [{
            "system": "http://terminology.hl7.org/CodeSystem/condition-clinical",
            "code": "active",
        }]
    },
    "verificationStatus": {
        "coding": [{
            "system": "http://terminology.hl7.org/CodeSystem/condition-ver-status",
            "code": "confirmed",
        }]
    },
    "category": [{
        "coding": [{
            "system": "http://terminology.hl7.org/CodeSystem/condition-category",
            "code": "encounter-diagnosis",
        }]
    }],
    "code": cc,                                  # OpenMed-built CodeableConcept
    "subject": {"reference": "Patient/patient-1"},
    "recordedDate": "2024-03-02",
}

codeable_concept sorts codings deterministically (SNOMED, LOINC, RxNorm, ICD-10-CM, HPO, MeSH first; everything else alphabetical), so the JSON is byte-stable across runs — important for diffable pipelines and golden tests.

Worked: the resulting Condition.code

json
{
  "code": {
    "coding": [
      { "system": "http://snomed.info/sct", "code": "44054006",
        "display": "Diabetes mellitus type 2" },
      { "system": "http://hl7.org/fhir/sid/icd-10-cm", "code": "E11.9",
        "display": "Type 2 diabetes mellitus without complications" }
    ],
    "text": "type 2 diabetes mellitus"
  }
}

Workflow

  1. NERopenmed.analyze_text(note, model_name=...)result.entities.
  2. Classify each span: a disease label → Condition; a drug → MedicationStatement; a lab/vital/measurement → Observation.
  3. Ground the surface text to a code out of process (terminology server or your own map). Never invent codes; if you cannot ground a span, emit a CodeableConcept with only text and no coding.
  4. Build the CodeableConcept with coding(...) + codeable_concept(...).
  5. Wrap it in the resource shell (set clinicalStatus/status, subject, dates). Use the cheat-sheet below.
  6. Reference the Patient/Encounter via {"reference": "Patient/<id>"}.
  7. Pass the list to to_bundle(...) (assembling-fhir-bundles) and validate (validating-us-core).

Resource cheat-sheet (where the CodeableConcept goes)

OpenMed entity kindFHIR resourceCoded slotRequired status field
Disease / diagnosisConditioncodeclinicalStatus, verificationStatus
Drug / medicationMedicationStatementmedicationCodeableConceptstatus (e.g. active)
Lab / vital / findingObservationcode (+ valueQuantity/valueCodeableConcept)status (e.g. final)
ProcedureProcedurecodestatus
AllergyAllergyIntolerancecodeclinicalStatus

MedicationStatement (drug span)

python
med = {
    "resourceType": "MedicationStatement",
    "id": "med-1",
    "status": "active",
    "medicationCodeableConcept": codeable_concept(
        [coding("rxnorm", "860975", "metformin hydrochloride 500 MG Oral Tablet")],
        text="metformin 500 mg",
    ),
    "subject": {"reference": "Patient/patient-1"},
}

Observation (lab/vital span)

python
obs = {
    "resourceType": "Observation",
    "id": "obs-1",
    "status": "final",
    "category": [{"coding": [{
        "system": "http://terminology.hl7.org/CodeSystem/observation-category",
        "code": "laboratory",
    }]}],
    "code": codeable_concept(
        [coding("loinc", "4548-4", "Hemoglobin A1c/Hemoglobin.total in Blood")],
        text="HbA1c",
    ),
    "valueQuantity": {
        "value": 7.4, "unit": "%",
        "system": "http://unitsofmeasure.org", "code": "%",
    },
    "subject": {"reference": "Patient/patient-1"},
}

Hand-off to / from OpenMed

  • From OpenMed: result.entities (EntityPrediction.text/.label/.confidence /.start/.end) is the input. Keep confidence and the offsets in an extension or a side log so the resource is auditable back to the source span.
  • To OpenMed: before exporting a note that still contains PHI, run openmed.deidentify(...); or de-identify a built resource/Bundle with openmed.interop.fhir_operations.de_identify_resource / de_identify_bundle (see that module — it walks free-text + narrative and never touches codes, references, systems, or temporal values).
  • OperationOutcome: report any spans you could not map as OperationOutcomeIssue(severity="warning", code="incomplete", diagnostics=..., expression="Condition.code")to_operation_outcome([...]). Keep diagnostics PHI-free (offsets/labels, never raw identifiers).

Edge cases & gotchas

  • No code? Still valid. A CodeableConcept with only text and no coding is legal R4. Emit it rather than inventing a code, and flag it via OperationOutcome. US Core may still require a code — see validating-us-core.
  • system_uri raises on an unknown short id that is not a URL. Pass a known short id (rxnorm/loinc/snomed/icd-10-cm/hpo/mesh) or a full http(s):// system URI.
  • Negation / temporality. OpenMed NER finds the mention, not its assertion. A negated ("no diabetes") or historical span should change verificationStatus/clinicalStatus or be dropped. Resolve assertion first (resolving-clinical-context, openmed.clinical).
  • Stable ids. Give each resource a unique id; to_bundle rejects duplicate ResourceType/id pairs because they corrupt cross-references.
  • Local-first. Grounding to RxNorm/LOINC/SNOMED is out-of-process with the user's own credentials. OpenMed bundles no restricted terminology.

Standards & references

Frequently asked questions

What does the Exporting To Fhir AI skill do?

Convert OpenMed NER output (entities from openmed.analyze_text) into FHIR R4 resources — Condition, MedicationStatement, Observation — using OpenMed's built-in FHIR R4 export helpers in openmed.clinical.exporters. Covers the verified CodeableConcept builder (coding, codeable_concept, system_uri), deterministic fullUrl references, and OperationOutcome reporting. Use after running OpenMed NER when the user wants standards-conformant FHIR JSON, mentions FHIR, Condition/Observation/MedicationStatement, CodeableConcept, RxNorm/LOINC/ICD-10/SNOMED coding, or interoperability with an EHR. Pairs af...

Why use Exporting To Fhir on TypingMind?

Because you install it once and use it with any model. Exporting To Fhir is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Exporting To Fhir in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/maziyarpanahi/openmed/tree/master/skills/exporting-to-fhir. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Exporting To Fhir?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Exporting To Fhir?

As many as you like. As long as a model supports skills, you can use Exporting To Fhir with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Exporting To Fhir AI skill free?

Yes. It is published on GitHub by maziyarpanahi under the Apache-2.0 license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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