With data breaches regularly making headlines, protecting sensitive information is more critical than ever. For healthcare and research professionals, patient data privacy isn’t just a legal requirement—it’s foundational for patient trust and ethical responsibility. However, every healthcare provider’s data needs and infrastructures are different, and one-size-fits-all solutions cannot provide the precise customization required.

The ideal de-identification platform offers a fast, scalable, secure, compliant, and user-friendly solution that can seamlessly integrate into a customer’s existing data infrastructure.

Demand a Swiss Army Knife of De-Identification Tools

The strongest and most versatile de-identification solutions should support a variety of data types, ensuring versatility and thoroughness, and come as an all-in-one, off-the-shelf technology tool that can be confidently deployed.

Capabilities Checklist:

  • Speed and Scalability: Can the solution effortlessly handle large volumes of data at the required speeds?
  • Top-Notch Security: Are privacy protection algorithms modern and state of the art?
  • Regulatory Compliance: Does the solution meet all local legal and regulatory requirements?
  • User-Friendly Interface: Does the solution seamlessly integrate with secure APIs, making it easy for IT, business, and clinical users to use?

Data Types Checklist:

  • Databases: Does the solution include built-in machine-learning recognition modules that detect sensitive data across tables? Test databases should verify that a new system will perform as expected.
  • Datasets: Does that solution have comprehensive configuration and data profiling screens to make managing datasets easier? This will enable the sharing of anonymized data confidently with vendors, researchers, and data brokers by removing sensitive PHI information while keeping most of the business transaction data intact.
  • HL7 Messages: Does the solution support HL7 v2.x with automatic schema extraction and configuration, while adapting to different data formats?
  • Clinical Free Text De-Identification: Does the solution include built-in machine learning and rule-based PHI detection modules to easily handle unstructured clinical text?

Ensure your chosen solution uses AI and ML models that have been trained using clinical notes. This ensures pseudonymized surrogate clinical texts are produced, protecting patient privacy. This looks like:

Insightful Reporting and Risk Assessment

Transparency and accountability should be at the heart of any de-identify platform through detailed reporting and comprehensive risk assessments.

Before De-identification:

To prepare for a secure process, Re-identification risks should be analyzed thoroughly, including de-identification levels, attribute types, and hierarchies.

After De-identification

A de-identify solution should assess residual re-identification risk and compliance levels, ensuring an understanding of your data’s privacy status and potential risks and assisting with compliance of regulations and data security standards. Reports should be downloadable to support data governance efforts and enable effective communication of data processes across an organization.

To get an idea of what a healthcare specific data De-Identification workflow looks like, read more in chapter three of The Buyer’s guide to De-Identification Solutions

Read the eBook

Want more insights? Check out key excerpts from Chapter Two in our previous blog: Building your data strategy: Types of De-Identification methods.