TELUS Collaborative Health Record Python API Docs | dltHub

Build a TELUS Collaborative Health Record-to-database pipeline in Python using dlt with AI Workbench support for Claude Code, Cursor, and Codex.

Last updated: Mar 13, 2026

The TELUS Collaborative Health Record API uses GraphQL for data fetching and updating. It requires enabling from TELUS Health and provides secure access to CHR data. The API supports real-time data operations. The REST API base URL is https://<your-chr-domain>/enterprise/graphql (copy from CHR Settings > Enterprise API > API Endpoint) and All requests require a short‑lived RS512‑signed JWT provided in the Authorization header as a Bearer token..

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 TELUS Collaborative Health Record data in under 10 minutes.

What data can I load from TELUS Collaborative Health Record?

Here are some of the endpoints you can load from TELUS Collaborative Health Record:

Resource	Endpoint	Method	Data selector	Description
appointments	(POST) GraphQL query to base_url with query `appointments { ... }`	POST	data.appointments	Fetch list of appointments.
appointment	(POST) GraphQL query `appointment(id: "...") { ... }`	POST	data.appointment	Fetch a single appointment.
patients	(POST) GraphQL query `patients { ... }`	POST	data.patients	Fetch list of patients.
patient	(POST) GraphQL query `patient(id: "...") { ... }`	POST	data.patient	Fetch a single patient.
users	(POST) GraphQL query `users { ... }`	POST	data.users	Fetch list of users/clinicians.
clinics	(POST) GraphQL query `clinics { ... }`	POST	data.clinics	Fetch list of clinics/locations.
encounters	(POST) GraphQL query `encounters { ... }`	POST	data.encounters	Fetch list of encounters/visits.

How do I authenticate with the TELUS Collaborative Health Record API?

Each request must include header 'Authorization: Bearer {json_web_token}'. The JWT must be RS512‑signed, contain the configured issuer (iss) claim and expire within 15 minutes.

1. Get your credentials

In the CHR web app go to Settings > Enterprise API. 2) Create an API Consumer (see "Creating API consumers") and configure its public key and issuer. 3) Generate a JWT signed with RS512 using the private key and the configured issuer (iss) claim; set expiration to 900 seconds or less. 4) Use the JWT as the value for the Authorization: Bearer header in all requests.

2. Add them to .dlt/secrets.toml


[sources.telus_collaborative_health_record_source]
token = "your_jwt_here"

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.

How do I set up and run the pipeline?

Set up a virtual environment and install dlt:


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

1. Install the dlt AI Workbench:


dlt 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:


dlt 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 TELUS Collaborative Health Record 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:


python telus_collaborative_health_record_pipeline.py

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


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

Inspect your pipeline and data:


dlt pipeline telus_collaborative_health_record_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.

Python pipeline example

This example loads appointments and patients from the TELUS Collaborative Health Record API into DuckDB. It mirrors the endpoint and data selector configuration from the table above:


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

@dlt.source
def telus_collaborative_health_record_source(token=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://<your-chr-domain>/enterprise/graphql (copy from CHR Settings > Enterprise API > API Endpoint)",
            "auth": {
                "type": "bearer",
                "token": token,
            },
        },
        "resources": [
            {"name": "appointments", "endpoint": {"path": "(POST to the tenant GraphQL endpoint)", "data_selector": "data.appointments"}},
            {"name": "patients", "endpoint": {"path": "(POST to the tenant GraphQL endpoint)", "data_selector": "data.patients"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="telus_collaborative_health_record_pipeline",
        destination="duckdb",
        dataset_name="telus_collaborative_health_record_data",
    )
    load_info = pipeline.run(telus_collaborative_health_record_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.

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):


import dlt

data = dlt.pipeline("telus_collaborative_health_record_pipeline").dataset()
sessions_df = data.appointments.df()
print(sessions_df.head())

SQL (DuckDB example):


SELECT * FROM telus_collaborative_health_record_data.appointments LIMIT 10;

In a marimo or Jupyter notebook:


import dlt

data = dlt.pipeline("telus_collaborative_health_record_pipeline").dataset()
data.appointments.df().head()

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

What destinations can I load TELUS Collaborative Health Record data to?

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

Destination	Example value
DuckDB (local, default)	`"duckdb"`
PostgreSQL	`"postgres"`
BigQuery	`"bigquery"`
Snowflake	`"snowflake"`
Redshift	`"redshift"`
Databricks	`"databricks"`
Filesystem (S3, GCS, Azure)	`"filesystem"`

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

Next steps

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

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


dlt ai toolkit data-exploration install
dlt ai toolkit dlthub-runtime install

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