HL7v2.5 Parser

This repository contains a Python-based parser for HL7v2.5 medical record files, capable of converting these files into JSON format. This tool has been tested against the Redox engine, demonstrating superior performance in various scenarios.

Features

• Parse HL7v2.5 files to JSON: Converts HL7v2.5 medical records into a structured JSON format for easier processing and integration.
• Detailed breakdown: Breaks down HL7 messages into segments, data fields, components, and sub-components for detailed analysis.
• Validation: Tested extensively to ensure accuracy and reliability.

Requirements

• Python 3.11

Getting Started

To use this parser, simply clone this repository and follow the steps below:

git clone https://github.com/dev-vivekkumarverma/hl7v2.5-Parser.git

Note: Do not add the extension of the input file (defaults to .txt).

Open the .ipynb file in Jupyter Notebook and follow the instructions provided in the markdown cells to parse your HL7v2.5 files.

Important Points

Structure:

• Trigger Event: The event that takes place which generates the message.
• Segments: Data-field delimiter separated (e.g., <cr>), identified by a three-letter code (e.g., MHS, PID).
  • Data-fields: Data between the data-field separator (|). For example: |A01|20150601135823+0100|. Datafields can also repeat with the data field seperator ("|") using the repetition seperator character ('~').
    • Components: Data inside a data-field separated by component separator delimiter (^). For example: |23432^Smith^Gordon^Denny^Jr^MD^^^AssignAuth&1.2.3.4.5.6&ISO^L^9^1000^DN^ AssignFac&1.2.3.4.5.7&ISO^^G^20100101000000+0100^20330101000000+0100^doctor|.
      • Sub-components: Parts of the components separated by sub-component separator delimiter (&) if present. For example: ^AssignFac&1.2.3.4.5.7&ISO^.

Example Usage

Here's a brief example of how to use the parser:

• After cloning the repository change the directory with following command

      cd hl7v2.5-Parser

  then change the input_file_name in the Hl7v2.ipynb file:

input_file_name='hl7v2_test'  # replace with your hl7v2.5 file path

then run the entire notebook. Your output files will be stored in output_folder with format {input_file_name}_output_{date}.json

Advantages

• Transparency: The parser provides detailed keys in the output JSON, allowing for easy cross-referencing with HL7 definitions.
• Customizable: Easy to modify and extend for specific use cases.

For reference, use this recommended site to look up HL7 data fields and structures.