Table of Contents

• Bulk FHIR validation
• Aggregation and presentation of bulk validation results
• Based on open standards and powerfull and flexible Open Source Software
• Architecture
• Installation and Configuration
• Setup FHIR Packages
• Create config file
• Setup your FHIR server parameters
• Setup initial password for Jupyter Lab UI
• Start validation environment
• Usage
• Web UI
• Login
• Start validation
• Navigate validation results
• User documentation
• Select resources to be validated by FHIR Search parameters
• Python library

Bulk FHIR validation

Dockerized Open Source environment for bulk FHIR validation of FHIR resources.

Aggregation and presentation of bulk validation results

This bulk FHIR validation environment aggregates/groups and presents validation results of bulk FHIR validation of FHIR Search results.

Based on open standards and powerfull and flexible Open Source Software

Therefore this validation environment uses following standards and Open Source Software by the Python Library fhirvalidation.py:

• Loading FHIR resources to be validated by FHIR search (for documentation see section "Select resources to be validated by FHIR Search parameters" below)

• FHIR validation by HAPI FHIR Validator configured by Docker environment variables

• Aggregation by Python Pandas dataframe

• Presentation of validation results in web UI by Jupyter Lab

Architecture

Installation and Configuration

Setup FHIR Packages

Download the FHIR NPM Packages of the German MII Core Dataset modules (Kerndatensatz der Medizininformatik Initiative) to the directory fhir-packages.

E.g. by running download-packages.sh:

bash download-packages.sh

If you want to use other FHIR packages, download the NPM packages to the fhir-packages directory and set them up by the environment variables of the HAPI validation service.

The environment variable names is derived from config section implementationguides in HAPIs application.yaml

Create config file

Copy .env.skeleton to .env (so .env which will contain your credentials will be excluded from the git repo by .gitignore):

cp .env.example .env

Setup your FHIR server parameters

Edit .env and set up custom parameters like the URL for your FHIR Server.

Setup initial password for Jupyter Lab UI

Set a custom initial token in variable JUPYTER_TOKEN in .env

Start validation environment

Start the validation environment by

docker compose up -d

Usage

Web UI

Access the web user interface of Jupyter Lab on the configured (default: 80) port: http://yourserver/

Login

Login with the initial password / token you configured in .env

Start validation

Now you can start the validation and aggregation of validation results.

Therefore run the Jupyter Notebook fhir-validation.ipynb.

Navigate validation results

You can navigate the validation results by "Table of Content" of Jupyter Lab. Therefore switch the left navigation bar from "File browser" to "Table of Contents".

User documentation

The further user documentation is embedded in the Jupyter Notebook:

The different outputs are described in markdown cells and used parameters are described in the code cells.

Select resources to be validated by FHIR Search parameters

You can select/filter the resources to be validated by FHIR search parameters.

For filter options you can set search_parameters, see FHIR search common parameters for all resource types, as well as additional FHIR search parameters for certain resource types like Patient, Condition, Observation, ...

Python library

If you dont want to use Jupyter Lab as a user interface (e.g. if you want to generate markdown for CI/CD reports), you can use the Python library fhirvalidation.py returning a pandas dataframe independent from Jupyter Lab.

In the Jupyter Notebook, you can find documentation on how to use the library, including example with code snippets.