120 lines
4.4 KiB
Markdown
120 lines
4.4 KiB
Markdown
---
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gitea: none
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include_toc: true
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---
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# Bulk FHIR validation
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Dockerized Open Source environment for **bulk FHIR validation of FHIR resources**.
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## Aggregation and presentation of bulk validation results
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This bulk FHIR validation environment **aggregates/groups and presents validation results of bulk FHIR validation** of FHIR Search results.
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## Based on open standards and powerfull and flexible Open Source Software
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Therefore this validation environment uses following standards and Open Source Software by the Python Library [fhirvalidation.py](home/fhirvalidation.py):
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- Loading FHIR resources to be validated by [FHIR search](https://www.hl7.org/fhir/search.html) (for documentation see section "Select resources to be validated by FHIR Search parameters" below)
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- [FHIR validation](https://www.hl7.org/fhir/validation.html#op) by [HAPI FHIR Validator](https://hapifhir.io/hapi-fhir/docs/validation/introduction.html) configured by Docker environment variables
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- Aggregation by [Python Pandas](https://pandas.pydata.org/docs/user_guide/index.html) dataframe
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- Presentation of validation results in web UI by [Jupyter Lab](https://jupyterlab.readthedocs.io/en/latest/)
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## Architecture
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## Installation and Configuration
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### Setup FHIR Packages
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Download the FHIR NPM Packages of the [German MII Core Dataset modules](https://www.medizininformatik-initiative.de/de/uebersicht-ueber-versionen-der-kerndatensatz-module) (Kerndatensatz der Medizininformatik Initiative) to the directory `fhir-packages`.
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E.g. by running [download-packages.sh](download-packages.sh):
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``
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bash download-packages.sh
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``
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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.
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The environment variable names is derived from config section `implementationguides` in HAPIs [application.yaml](https://github.com/hapifhir/hapi-fhir-jpaserver-starter/blob/master/src/main/resources/application.yaml)
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### Create config file
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Copy .env.skeleton to .env (so .env which will contain your credentials will be excluded from the git repo by .gitignore):
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`cp .env.example .env`
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### Setup your FHIR server parameters
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Edit [.env](.env.example) and set up custom parameters like the URL for your FHIR Server.
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### Setup initial password for Jupyter Lab UI
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Set a custom initial token in variable `JUPYTER_TOKEN` in `.env`
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### Start validation environment
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Start the validation environment by
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``
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docker compose up -d
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``
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## Usage
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### Web UI
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Access the [web user interface of Jupyter Lab](https://jupyterlab.readthedocs.io/en/latest/) on the configured (default: 80) port:
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http://yourserver/
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#### Login
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Login with the initial password / token you configured in .env
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#### Start validation
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Now you can start the validation and aggregation of validation results.
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Therefore run the Jupyter Notebook [fhir-validation.ipynb](home/fhir-validation.ipynb).
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#### Navigate validation results
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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").
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#### User documentation
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The further user documentation is embedded in the Jupyter Notebook:
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The different outputs are described in markdown cells and used parameters are described in the code cells.
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### Select resources to be validated by FHIR Search parameters
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You can select/filter the resources to be validated by [FHIR search](https://www.hl7.org/fhir/search.html) parameters.
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For filter options you can set `search_parameters`, see [FHIR search common parameters for all resource types](https://www.hl7.org/fhir/search.html#standard), as well as additional FHIR search parameters for certain resource types like [Patient](https://www.hl7.org/fhir/patient.html#search), [Condition](https://www.hl7.org/fhir/condition.html#search), [Observation](https://www.hl7.org/fhir/observation.html#search), ...
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### Python library
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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](home/fhirvalidation.py) returning a [pandas](https://pandas.pydata.org/docs/user_guide/index.html) dataframe independent from Jupyter Lab.
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In the Jupyter Notebook, you can find documentation on how to use the library, including example with code snippets. |