How to Build an ETL App for FHIR Data in Python with CData

The rich ecosystem of Python modules lets you get to work quickly and integrate your systems more effectively. With the CData Python Connector for FHIR and the petl framework, you can build FHIR-connected applications and pipelines for extracting, transforming, and loading FHIR data. This article shows how to connect to FHIR with the CData Python Connector and use petl and pandas to extract, transform, and load FHIR data.

With built-in, optimized data processing, the CData Python Connector offers unmatched performance for interacting with live FHIR data in Python. When you issue complex SQL queries from FHIR, the driver pushes supported SQL operations, like filters and aggregations, directly to FHIR and utilizes the embedded SQL engine to process unsupported operations client-side (often SQL functions and JOIN operations).

Connecting to FHIR Data

Connecting to FHIR data looks just like connecting to any relational data source. Create a connection string using the required connection properties. For this article, you will pass the connection string as a parameter to the create_engine function.

Set URL to the Service Base URL of the FHIR server. This is the address where the resources are defined in the FHIR server you would like to connect to. Set ConnectionType to a supported connection type. Set ContentType to the format of your documents. Set AuthScheme based on the authentication requirements for your FHIR server.

Generic, Azure-based, AWS-based, and Google-based FHIR server implementations are supported.

Sample Service Base URLs

• Generic: http://my_fhir_server/r4b/
• Azure: https://MY_AZURE_FHIR.azurehealthcareapis.com/
• AWS: https://healthlake.REGION.amazonaws.com/datastore/DATASTORE_ID/r4/
• Google: https://healthcare.googleapis.com/v1/projects/PROJECT_ID/locations/LOCATION/datasets/DATASET_ID/fhirStores/FHIR_STORE_ID/fhir/

Generic FHIR Instances

The product supports connections to custom instances of FHIR. Authentication to custom FHIR servers is handled via OAuth (read more about OAuth in the Help documentation. Before you can connect to custom FHIR instances, you must set ConnectionType to Generic.

After installing the CData FHIR Connector, follow the procedure below to install the other required modules and start accessing FHIR through Python objects.

Install Required Modules

Use the pip utility to install the required modules and frameworks:

pip install petl
pip install pandas

Build an ETL App for FHIR Data in Python

Once the required modules and frameworks are installed, we are ready to build our ETL app. Code snippets follow, but the full source code is available at the end of the article.

First, be sure to import the modules (including the CData Connector) with the following:

import petl as etl
import pandas as pd
import cdata.fhir as mod

You can now connect with a connection string. Use the connect function for the CData FHIR Connector to create a connection for working with FHIR data.

cnxn = mod.connect("URL=http://test.fhir.org/r4b/;ConnectionType=Generic;ContentType=JSON;AuthScheme=None;")

Create a SQL Statement to Query FHIR

Use SQL to create a statement for querying FHIR. In this article, we read data from the Patient entity.

sql = "SELECT Id, [name-use] FROM Patient WHERE [address-city] = 'New York'"

Extract, Transform, and Load the FHIR Data

With the query results stored in a DataFrame, we can use petl to extract, transform, and load the FHIR data. In this example, we extract FHIR data, sort the data by the [name-use] column, and load the data into a CSV file.

Loading FHIR Data into a CSV File

table1 = etl.fromdb(cnxn,sql)

table2 = etl.sort(table1,'[name-use]')

etl.tocsv(table2,'patient_data.csv')

With the CData Python Connector for FHIR, you can work with FHIR data just like you would with any database, including direct access to data in ETL packages like petl.

Free Trial & More Information

Download a free, 30-day trial of the CData Python Connector for FHIR to start building Python apps and scripts with connectivity to FHIR data. Reach out to our Support Team if you have any questions.

Full Source Code

import petl as etl
import pandas as pd
import cdata.fhir as mod

cnxn = mod.connect("URL=http://test.fhir.org/r4b/;ConnectionType=Generic;ContentType=JSON;AuthScheme=None;")

sql = "SELECT Id, [name-use] FROM Patient WHERE [address-city] = 'New York'"

table1 = etl.fromdb(cnxn,sql)

table2 = etl.sort(table1,'[name-use]')

etl.tocsv(table2,'patient_data.csv')