Human-verified automation for cleaning sensitive healthcare information

Convirzon provides an custom workflow that removes and anonymizes protected health information (PHI) from medical data. It uses pretrained natural-language-processing models to recognize patient-identifiable details and prepare datasets for downstream use; an optional human-review layer is available if you require added oversight and alignment with regulatory expectations.

Unlocking usable healthcare data

With this solution, you can:

• Let automated models detect, blur or mask PHI in text and imagery.
• Add a quality review step to verify compliance.
• Share de-identified assets easily across projects and workflows.
• Ensure compliance with relevant healthcare protections and standards, for example, the 18 identifiers defined under HIPAA.

How a recent use case played out

A primary healthcare provider had thousands of ultrasound videos that contained burned-in patient identifiers. The provider used Convirzon’s de-identification solution to clean and anonymize the data, enabling its use in model training, AI/ML projects, and disease-diagnosis research.

Key features at a glance

• Automated Workflow: Raw files pass through a pipeline that de-identifies them and makes them ready for use.
• Customizable & Scalable: The solution adapts to evolving project needs and scales to varying volumes.
• Quality Control Options: Choose between fully automated processing or include a human-in-the-loop (HiTL) layer for verification.
• Analytics & Reporting: Track quality metrics and monitor progress across your data pipeline.

Hybrid Approach: Automation + Expert verification

There are two paths available:

1. A fully automated path where the model handles detection and masking end-to-end.
2. A hybrid path where automation handles the bulk and human teams intervene to correct any remaining PHI burn-in or metadata exposure.

Human-in-the-Loop Teams

With Convirzon, you gain access to US board-certified physicians working alongside global medical annotation teams. This combination ensures that de-identified data meets rigorous compliance and quality requirements, while allowing you to scale through our system.

De-Identification & Cleaning Process

The workflow typically includes:

1. Sourcing & Curation: Identifying and preparing data for de-identification.
2. De-Identification & Cleaning: Using the platform to identify PHI and cleanse the data.
3. Structuring & Annotation: Organizing data post-cleansing for the intended model application.
4. Model Validation: Evaluating model outputs against defined criteria to assure quality.

Regulatory Compliance & Data Security

Handling medical AI data requires awareness of evolving regulatory standards. Convirzon aligns its processes, personnel, and technology with healthcare industry guidelines and regulatory frameworks, enabling data workflows intended for downstream use (including potential filings) to proceed with Confidence.

Next Steps

If you are looking to move from raw medical data to model-ready, de-identified assets, Convirzon’s platform gives you workflow flexibility to do so while aligning with compliance demands.