De-identification review highlighting for clinical text
Highlighting possible identifiers can make privacy review easier, but it should be treated as a visual aid rather than a final determination.
Important: Automated de-identification is not perfect. Review the full text manually and follow the policy that applies to your setting.
Why highlighting helps
Token replacement tools can remove common patterns, but sometimes a reviewer wants to see where sensitive details may be located before changing text. Highlighting possible email addresses, phone numbers, dates, ID labels, and basic address patterns gives the reviewer a first-pass map of areas that need attention.
What highlighting can miss
Not every identifier follows a simple pattern. A patient name written without a label, a rare clinical story, a small community location, a detailed timeline, or a workplace reference may still identify someone. That is why highlighted review should be paired with careful reading, policy review, and conservative judgment.
Use highlighting before export
A helpful workflow is to highlight possible identifiers first, create a tokenized version only when appropriate, then export the reviewed text as plain text, Markdown, or a printable document. This keeps the formatting workflow separate from clinical interpretation and makes each step easier to audit.
Review checklist
- Check visible highlights, then read the unhighlighted text too.
- Look for names, dates, locations, contact details, and record numbers.
- Watch for rare contextual clues or unusually specific timelines.
- Use fictional examples for public guides, teaching samples, and screenshots whenever possible.
Use the tools
Try the De-Identification Review Highlighter for visual review and the Clinical Text De-Identifier for token replacement. For final cleanup, use the Plain Text Exporter.