Epic Systems Python API Docs | dltHub

Epic on FHIR provides standards-based HL7 FHIR REST APIs for interoperability with Epic electronic health record systems. The REST API base URL is https://fhir.epic.com/interconnect-fhir-oauth/api/FHIR/R4 and all requests require a Bearer token obtained via OAuth 2.0 flows.

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 add "dlt[hub]" and start loading Epic Systems data in under 10 minutes.

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What data can I load from Epic Systems?

Here are some of the endpoints you can load from Epic Systems:

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How do I authenticate with the Epic Systems API?

Epic APIs use SMART on FHIR / OAuth 2.0. Requests require an Authorization header with a Bearer token, typically obtained via the token endpoint using authorization_code, client_credentials, or JWT bearer flows.

1. Get your credentials

1. Register your organization and application on the Epic on FHIR portal (fhir.epic.com). 2. In the portal, navigate to the Build Apps page and select your application. 3. For Backend Services (server-to-server), upload your RSA public key or provide a JWK Set URL (JKU) under the appropriate app settings to authenticate via signed JWTs. 4. For confidential clients requiring a client secret, navigate to Review & Manage Downloads within the application dashboard and select Get Secrets. 5. Once registered and configured, Epic will provide your Client ID. Note that production access requires a customer health system to authorize your application within their environment.

2. Add them to .dlt/secrets.toml

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[sources.epic_systems_source]
client_id = "your_client_id_here"
private_key = "-----BEGIN RSA PRIVATE KEY-----\n...\n-----END RSA PRIVATE KEY-----"
token_url = "https://fhir.epic.com/interconnect-fhir-oauth/oauth2/token"

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 init
uv add "dlt[hub]"

1. Install the dlt AI harness:

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uv run 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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uv run dlthub ai toolkit install rest-api-pipeline

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 Epic Systems 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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uv run python epic_systems_pipeline.py

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

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Pipeline epic_systems_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset epic_systems_data
The duckdb destination used duckdb:/epic_systems.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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uv run dlthub 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 /oauth2/authorize and /oauth2/token from the Epic Systems 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 epic_systems_source(client_id=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://fhir.epic.com/interconnect-fhir-oauth/api/FHIR/R4",
            "auth": {"type": "bearer", "token": client_id},
        },
        "resources": [
            {"name": "patient", "endpoint": {"path": "Patient", "data_selector": "entry"}},
            {"name": "observation", "endpoint": {"path": "Observation", "data_selector": "entry"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="epic_systems_pipeline",
        destination="duckdb",
        dataset_name="epic_systems_data",
    )
    load_info = pipeline.run(epic_systems_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("epic_systems_pipeline").dataset()
sessions_df = data.observation.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM epic_systems_data.observation LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("epic_systems_pipeline").dataset()
data.observation.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 Epic Systems 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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uv run dlthub ai toolkit install data-exploration
uv run dlthub ai toolkit install dlthub-platform

• 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