Python FHIR converter, fastish, most nuts and bolts included, ready for production
Provides a python implementation of FHIR-Converter written in C#. This allows the data transformation to live and breath as any other python module in your favorite python based data pipeline framework
Whats supported:
- CDA->FHIR R4
- STU3->FHIR R4
Key features:
- Fast. Speed is relative. Minimizes overhead outside the rendering engine
- Designed to be extensibile. Use the thin rendering API or leverage the builtin parts
- Designed to be easy to use, extend and deploy. Use what's bundled or manage the environment your way
- Multiple enhancement / bug corrections included with the packaged CDA->R4 templates.
Limitations:
- Additional work is needed to support JSON->FHIR, HL7v2->FHIR and FHIR->HL7v2.
- Comma between parameters. Python-liquid requires a comma between parameters. Templates brought to this environment may need commas added.
- Variable names when passing variables to a snippet. Python-liquid requires the identifier / variable name. Templates brought to this environment may need changes. See Resource.liquid as an example of a template that has been updated.
- C# date format strings are supported to an extent to mimimize the impact of migrating templates. See filters for more information.
Built on the back of:
Table of Contents
Install Python FHIR Converter using Pipenv:
$ pipenv install -u python-fhir-converterOr pip:
$ pip install python-fhir-converter- Documentation: https://chaseastewart.github.io/fhir-converter/
- PyPi: https://pypi.org/project/python-fhir-converter/
- Source: https://github.com/chaseastewart/fhir-converter
- Issues: https://github.com/chaseastewart/fhir-converter/issues
See examples for more indepth usage / usecases.
from fhir_converter.renderers import CcdaRenderer
with open("data/sample/ccda/ccd.ccda") as xml_in:
print(CcdaRenderer().render_fhir_string("CCD", xml_in))The package comes with a CLI interface that can be invoked either by the script name
fhir_converter_cli or as python module python -m fhir_converter. The CLI allows you to transform a single file or an entire directory.
fhir_converter_cli --from-file ./data/sample/ccda/CCD.ccda --to-dir ./data/out --template-name CCD
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RENDER SUCCESS
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Total time: 0.14s
Finished at: 2024-01-11 10:49:44.182033
Final Memory: 32M
---------------------------------------------------------------Templates can be loaded from any python-liquid supported mechanism. When a rendering environment is not provided, templates will be loaded from the module templates. To ease the creation / reuse of templates a TemplateSystemLoader is provided that handles the template name conventions establised by FHIR-Converter. This allows user defined templates to reference existing templates without change. The example user defined templates reuse the default section / header templates.
You can run the benchmark from the root of the source tree. Test rig is a 14-inch, 2021 Macbook Pro with the M1 Pro. The benchmark performs the conversion for each template showing the min, max and mean times for the sample data used.
Python Version=3.12.1
Iterations=20
Sample=data/sample/ccda/Discharge_Summary.ccda
CCD max=0.042 min=0.013 avg=0.015
ConsultationNote max=0.015 min=0.013 avg=0.014
DischargeSummary max=0.020 min=0.014 avg=0.014
HistoryandPhysical max=0.015 min=0.013 avg=0.014
OperativeNote max=0.014 min=0.010 avg=0.010
ProcedureNote max=0.013 min=0.011 avg=0.012
ProgressNote max=0.013 min=0.012 avg=0.012
ReferralNote max=0.015 min=0.014 avg=0.014
TransferSummary max=0.016 min=0.014 avg=0.014
LabsandVitals max=0.009 min=0.008 avg=0.008
Pampi max=0.010 min=0.009 avg=0.009
Sample=data/sample/ccda/History_and_Physical.ccda
CCD max=0.053 min=0.018 avg=0.020
ConsultationNote max=0.021 min=0.018 avg=0.019
DischargeSummary max=0.018 min=0.016 avg=0.017
HistoryandPhysical max=0.020 min=0.018 avg=0.019
OperativeNote max=0.013 min=0.011 avg=0.012
ProcedureNote max=0.015 min=0.014 avg=0.014
ProgressNote max=0.017 min=0.015 avg=0.016
ReferralNote max=0.022 min=0.018 avg=0.019
TransferSummary max=0.021 min=0.019 avg=0.020
LabsandVitals max=0.012 min=0.010 avg=0.011
Pampi max=0.013 min=0.012 avg=0.012