PHIdelity 0.1.1

The tool extracts PHI into a structured CSV file and saves the anonymized note as a text file, making it ideal for healthcare professionals, researchers, and developers handling sensitive medical data.

PHIdelity

Overview

The PHIdelity is a Python tool that intelligently anonymizes Protected Health Information (PHI) in clinical notes while preserving their contextual meaning. Unlike basic redaction methods that obscure data, this tool uses a local Large Language Model (LLM) via Ollama to identify PHI (e.g., names, dates, medical record numbers) and replace it with meaningful, generalized descriptions (e.g., [Patient Name], [Date of Visit]). This contextualized anonymization ensures the anonymized notes remain useful for research, analysis, or sharing while complying with privacy regulations like HIPAA.

The tool extracts PHI into a structured CSV file and saves the anonymized note as a text file, making it ideal for healthcare professionals, researchers, and developers handling sensitive medical data.

Key Features

• Contextualized Anonymization: Replaces PHI with descriptive placeholders that retain the note's meaning (e.g., "John Doe" becomes [Patient Name]), enhancing usability for downstream applications.
• Advanced PHI Detection: Leverages a local LLM (default: qwen3:4B) to identify a wide range of PHI, including names, dates, medical record numbers, and more.
• Structured Output: Saves PHI to a CSV file with unique IDs, types, values, and descriptions for easy tracking and auditing.
• Anonymized Note Export: Generates a text file with the anonymized clinical note, ready for secure sharing or analysis.
• Configurable and Local: Runs on a local Ollama server, ensuring data privacy and allowing customization of the LLM model and output paths.
• Open Source: Licensed under the MIT License, inviting community contributions and adoption.

Why Contextualized Anonymization?

Traditional anonymization methods often replace PHI with generic markers (e.g., [REDACTED]) or random strings, which can obscure the note's meaning and reduce its value for research or clinical review. The Clinical Note Anonymizer addresses this by:

• Preserving Semantics: Descriptive placeholders like [Attending Physician Name] or [Medical Record Number] maintain the note's context, making it interpretable for humans and machines.
• Supporting Use Cases: Anonymized notes remain suitable for medical research, machine learning training, or educational purposes without compromising privacy.
• Ensuring Compliance: By removing identifiable information while retaining structure, the tool helps meet strict privacy standards like HIPAA.

Prerequisites

• Python: Version 3.8 or higher.
• Ollama: A running Ollama server (default: http://localhost:11434/) with the qwen3:4B model installed. See Ollama's documentation for setup.
• Dependencies: Python packages listed in requirements.txt.

Installation

1. Clone the Repository:

   git clone https://github.com/your-username/clinical-note-anonymizer.git
   cd clinical-note-anonymizer
   

2. Set Up a Virtual Environment (recommended):

   python -m venv venv
   source venv/bin/activate  # On Windows: venv\Scripts\activate
   

3. Install Dependencies:

   pip install -r requirements.txt
   

4. Install and Configure Ollama:

   • Install Ollama from ollama.ai.
   • Start the Ollama server:

     ollama serve
     

   • Pull the required model:

     ollama pull qwen3:4B
     

5. Verify Setup: Confirm the Ollama server is running at http://localhost:11434/:

   curl http://localhost:11434/api/generate -d '{"model": "qwen3:4B", "prompt": "Test"}'
   

Usage

1. Prepare a Clinical Note: The script includes a sample clinical note in anonymizer.py. Modify the clinical_note variable or provide your own note as a string.

2. Run the Script: Process the clinical note to detect PHI and generate outputs:

   python anonymizer.py
   

3. Outputs:

   • PHI Data (phi_data.csv): A CSV file with columns: ID, type, value, description.
   • Anonymized Note (anonymized_note.txt): A text file with PHI replaced by contextual placeholders.
   • Console Output: Shows the LLM's JSON output, the anonymized note, and status messages.

4. Example Output:

   • phi_data.csv:

     ID,type,value,description
     redacted_name_001,Name,John Doe,Patient Name
     redacted_date_001,Date,June 11, 2025,Date of Visit
     redacted_medical_record_number_001,Medical Record Number,123456,Medical Record Number
     redacted_name_002,Name,Dr. Jane Smith,Attending Physician Name
     ...
     

   • anonymized_note.txt:

     Radiation Oncology Clinical Note
     Date of Visit: [Date of Visit]
     Patient Information
     
     Name: [Patient Name]
     Age: 65 years old
     Medical Record Number: [Medical Record Number]
     ...
     Physician: [Attending Physician Name]
     

5. Customize Configuration: Edit anonymizer.py to adjust:

   • OLLAMA_ENDPOINT: Ollama server URL (default: http://localhost:11434/).
   • OLLAMA_MODEL: LLM model (default: qwen3:4B).
   • Output file paths in generate_phi_csv and anonymize_clinical_note.

File Structure

clinical-note-anonymizer/
├── anonymizer.py          # Main script for PHI detection and anonymization
├── requirements.txt       # Python dependencies
├── README.md              # Project documentation (this file)
├── phi_data.csv           # Output CSV file (generated)
├── anonymized_note.txt    # Output anonymized note (generated)

Contributing

We welcome contributions to enhance the Clinical Note Anonymizer, especially improvements to contextualization, LLM integration, or output formats. To contribute:

1. Fork the Repository: Create a fork on GitHub.
2. Create a Branch: Use a descriptive name (e.g., feature/improve-phi-detection).
3. Make Changes: Implement and test your changes.
4. Submit a Pull Request: Include a clear description and reference related issues.

Review the Contributing Guidelines and Code of Conduct before submitting.

Issues and Support

Encounter a problem? Please:

• Check the Issues page for similar reports.
• Open a new issue with details, including error messages and reproduction steps.

License

This project is licensed under the MIT License. See the LICENSE file for details.

Acknowledgments

• Powered by Ollama for secure, local LLM inference.
• Motivated by the need for privacy-preserving tools in healthcare that balance compliance and data utility.

Contact

For inquiries or collaboration, use GitHub Issues or contact (add your email if desired).

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Last updated: June 11, 2025