Healthcare data rarely arrives clean or consistent. HL7 messages come in from multiple source systems — each running a different version of the standard, each structuring patient demographics, diagnoses, and procedures slightly differently. The result is fragmented data that's impossible to report on or route reliably until someone normalizes it.
This pipeline extracts HL7 v2.x messages from three simulated source systems (v2.3, v2.4, and v2.5.1), transforms them into a single standardized common format, and loads the results into per-practice CSVs and a consolidated JSON repository. Patient demographics, provider info, ICD-10 codes, CPT codes, and encounter metadata all map to the same schema regardless of where they came from.
The pipeline reads HL7 messages from three source systems — each running a different version of the standard — normalizes everything into a common schema, and loads the result into per-practice CSVs and a consolidated repository.
Here's what the mapper is actually doing at each step. If you're the kind of person who wants to know how the engine works (not just that it does), this is for you.
Reads HL7 v2.x messages from system_a (v2.3), system_b (v2.5.1), and system_c (v2.4). Each directory represents a different source system with its own HL7 version and field conventions. The pipeline handles all three in a single pass.
Disparate HL7 versions get normalized into a single common schema: patient demographics, provider info, ICD-10 diagnosis codes, CPT procedure codes, and encounter metadata — all structured the same way regardless of source.
Transformed data is written to per-practice CSV files, a consolidated_repository.json with the full merged dataset, and an etl_summary_report.txt with record counts and quality metrics across all 16 practice types.
The results_generator.py produces post-ETL analytics and validation summaries. Output includes validation_results.json and validation_results.csv so your team can see exactly what passed, what flagged, and where the data quality gaps are.
There are other ETL tools out there. What makes this one different is that it was designed specifically for healthcare RCM, by someone who understands the data, the compliance requirements, and what happens downstream when something is mapped wrong.
Handles HL7 v2.3, v2.4, and v2.5.1 in the same pipeline. Each version has different segment structures and field positions — the engine accounts for all of them without requiring separate parsers per source.
Post-ETL quality checks produce a validation_results.json and validation_results.csv. Record counts, quality metrics, and any flagged anomalies are surfaced in the etl_summary_report.txt before anything is considered final.
Beyond per-practice CSVs, the pipeline writes a full consolidated_repository.json that merges all practice data into a single queryable dataset. No pip packages required — Python 3.12+ standard library only.
One pipeline. Three HL7 versions. Sixteen practice types. When your source systems speak different dialects of the same standard, this pipeline is the interpreter — outputting clean, consistent data every time without manual reconciliation.
If you need to know what's in the repository and how the pieces connect, here's the breakdown.
etl_engine.py
results_generator.py
ETL_Transformation_Engine.py
generators/generate_hl7.py
{practice_type}_standardized.csv ·
consolidated_repository.json ·
etl_summary_report.txt ·
validation_results.json ·
validation_results.csv
Results/ETL_Engine/