How to Visualize FHIR Data in Python with pandas

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, the pandas & Matplotlib modules, and the SQLAlchemy toolkit, you can build FHIR-connected Python applications and scripts for visualizing FHIR data. This article shows how to use the pandas, SQLAlchemy, and Matplotlib built-in functions to connect to FHIR data, execute queries, and visualize the results.

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.

Follow the procedure below to install the required modules and start accessing FHIR through Python objects.

Install Required Modules

Use the pip utility to install the pandas & Matplotlib modules and the SQLAlchemy toolkit:

pip install pandas
pip install matplotlib
pip install sqlalchemy

Be sure to import the module with the following:

import pandas
import matplotlib.pyplot as plt
from sqlalchemy import create_engine

Visualize FHIR Data in Python

You can now connect with a connection string. Use the create_engine function to create an Engine for working with FHIR data.

engine = create_engine("fhir:///?URL=http://test.fhir.org/r4b/&ConnectionType=Generic&ContentType=JSON&AuthScheme=None")

Execute SQL to FHIR

Use the read_sql function from pandas to execute any SQL statement and store the resultset in a DataFrame.

df = pandas.read_sql("SELECT Id, [name-use] FROM Patient WHERE [address-city] = 'New York'", engine)

Visualize FHIR Data

With the query results stored in a DataFrame, use the plot function to build a chart to display the FHIR data. The show method displays the chart in a new window.

df.plot(kind="bar", x="Id", y="[name-use]")
plt.show()

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 pandas
import matplotlib.pyplot as plt
from sqlalchemy import create_engin

engine = create_engine("fhir:///?URL=http://test.fhir.org/r4b/&ConnectionType=Generic&ContentType=JSON&AuthScheme=None")
df = pandas.read_sql("SELECT Id, [name-use] FROM Patient WHERE [address-city] = 'New York'", engine)

df.plot(kind="bar", x="Id", y="[name-use]")
plt.show()