Cleaning Up the Front End

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.

The Data Transformation Wizard

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.

How It Works

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.

Step 1: Extract

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.

Step 2: Transform

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.

Step 3: Load

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.

What Makes This Different

• Multi-version HL7 support: Handles v2.3, v2.4, and v2.5.1 in the same pipeline without separate parsers.
• Built-in validation: Post-ETL quality checks produce validation results before anything is considered final.
• Zero dependencies: Python 3.12+ standard library only — no pip packages required.

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. For the eligibility layer that works alongside this ETL foundation, see The Front End Guard.