FHIR Python API Docs | dltHub

FHIR is an HL7 standard for exchanging healthcare information electronically using a RESTful API structure. The REST API base URL is The FHIR service base URL is defined by the server implementation (e.g., 'https://server/path') and is the root address for all FHIR resource interactions. and FHIR uses standard OAuth 2.0 and the SMART App Launch framework for authentication and authorization..

dlt is an open-source Python library that handles authentication, pagination, and schema evolution automatically. dlthub provides AI context files that enable code assistants to generate production-ready pipelines. Install with uv pip install "dlt[workspace]" and start loading FHIR data in under 10 minutes.

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What data can I load from FHIR?

Here are some of the endpoints you can load from FHIR:

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How do I authenticate with the FHIR API?

FHIR servers typically use OAuth 2.0 to authorize requests. The client must present an 'Authorization' header containing a Bearer token: 'Authorization: Bearer <access_token>'.

1. Get your credentials

1. Register your application in the provider's developer console or administrative portal to obtain a unique Client ID and Client Secret. 2. Configure the necessary scopes (permissions) for your application, such as read/write access to specific FHIR resources. 3. Identify the Token Endpoint and Authorization Server URL provided by your healthcare data provider (e.g., Azure Entra ID, SMART on FHIR server). 4. Ensure your server environment is configured to securely store these credentials, as they act as the authentication mechanism for your data pipeline.

2. Add them to .dlt/secrets.toml

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[sources.fhir_source]
client_id = "your_client_id_here"
client_secret = "your_client_secret_here"
token_url = "https://your-auth-provider.com/token"
fhir_base_url = "https://your-fhir-service-url.com"

dlt reads this automatically at runtime — never hardcode tokens in your pipeline script. For production environments, see setting up credentials with dlt for environment variable and vault-based options.

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How do I set up and run the pipeline?

Set up a virtual environment and install dlt:

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uv venv && source .venv/bin/activate
uv pip install "dlt[workspace]"

1. Install the dlt AI harness:

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dlthub ai init --agent <your-agent> # <agent>: claude | cursor | codex

This installs project rules, a secrets management skill, appropriate ignore files, and configures the dlt MCP server for your agent. Learn more →

2. Install the rest-api-pipeline toolkit:

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dlthub ai toolkit rest-api-pipeline install

This loads the skills and context about dlt the agent uses to build the pipeline iteratively, efficiently, and safely. The agent uses MCP tools to inspect credentials — it never needs to read your secrets.toml directly. Learn more →

3. Start LLM-assisted coding:

Use /find-source to load data from the FHIR API into DuckDB.

The rest-api-pipeline toolkit takes over from here — it reads relevant API documentation, presents you with options for which endpoints to load, and follows a structured workflow to scaffold, debug, and validate the pipeline step by step.

4. Run the pipeline:

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python fhir_pipeline.py

If everything is configured correctly, you'll see output like this:

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Pipeline fhir_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset fhir_data
The duckdb destination used duckdb:/fhir.duckdb location to store data
Load package 1749667187.541553 is LOADED and contains no failed jobs

Inspect your pipeline and data:

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dlt pipeline fhir_pipeline show

This opens the Pipeline Dashboard where you can verify pipeline state, load metrics, schema (tables, columns, types), and query the loaded data directly.

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Python pipeline example

This example loads token and authorize from the FHIR API into DuckDB. It mirrors the endpoint and data selector configuration from the table above:

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import dlt
from dlt.sources.rest_api import RESTAPIConfig, rest_api_resources

@dlt.source
def fhir_source(access_token=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "The FHIR service base URL is defined by the server implementation (e.g., 'https://server/path') and is the root address for all FHIR resource interactions.",
            "auth": {"type": "bearer", "token": access_token},
        },
        "resources": [
            {"name": "patient", "endpoint": {"path": "v2/fhir/Patient", "data_selector": "entry"}},
            {"name": "explanation_of_benefit", "endpoint": {"path": "v2/fhir/ExplanationOfBenefit", "data_selector": "entry"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="fhir_pipeline",
        destination="duckdb",
        dataset_name="fhir_data",
    )
    load_info = pipeline.run(fhir_source())
    print(load_info)

To add more endpoints, append entries from the resource table to the "resources" list using the same name, path, and data_selector pattern.

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How do I query the loaded data?

Once the pipeline runs, dlt creates one table per resource. You can query with Python or SQL.

Python (pandas DataFrame):

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import dlt

data = dlt.pipeline("fhir_pipeline").dataset()
sessions_df = data.patient.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM fhir_data.patient LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("fhir_pipeline").dataset()
data.patient.df().head()

See how to explore your data in marimo Notebooks and how to query your data in Python with dataset.

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What destinations can I load FHIR data to?

dlt supports loading into any of these destinations — only the destination parameter changes:

Change the destination in dlt.pipeline(destination="snowflake") and add credentials in .dlt/secrets.toml. See the full destinations list.

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Next steps

Continue your data engineering journey with the other toolkits of the dltHub AI harness:

• data-exploration — Build custom notebooks, charts, and dashboards for deeper analysis with marimo notebooks.
• dlthub-platform — Deploy, schedule, and monitor your pipeline in production.

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dlthub ai toolkit data-exploration install
dlthub ai toolkit dlthub-platform install

• How to deploy a pipeline
• How to explore your data in marimo Notebooks
• How to query your data in Python with dataset
• Join the dlt community on Slack