Unstructured Text De-Identification

Enterprise-grade de-identification for clinical notes at scale.

NoteGuard protects sensitive information with high-recall entity detection, configurable de-identification policies, human-in-the-loop review, and audit-ready governance. Built for clinical, research, and AI workflows.

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>99.3%
entity masking precision on note PHI
20+
HIPAA identifier classes detected
<80ms
per-note throughput
1-click
audit packet, evidence attached

Sensitive information hides in every clinical note.

The most valuable clinical data lives in unstructured text. So do the identifiers that make it impossible to share. Names, dates, MRNs, and contact details are scattered across free text where structured tools can't reach them.

01

Identifiers live in free text

PHI can appear anywhere in free text, making them difficult for traditional structured-data tools to find.

02

Manual redaction doesn't scale

Hand-reviewing notes is slow, inconsistent, and error-prone. One missed identifier is one breach too many.

03

When privacy slows progress

Workflows stalls when safe data isn't readily available. Valuable information remains locked away, turning privacy requirements into an operational barrier.

“

NoteGuard makes clinical text usable at scale. Reliable detection, automated de-identification, and enterprise governance help teams access the unstructured data they need with confidence.
– Dr. Doug Johnston, Chief of Cardiac Surgery

The platform

One console, from raw note to safe release.

The complete de-identification pipeline, in one place. Configure policies, automate detection and masking, strip sensitive identifiers, validate outputs, and maintain a complete audit trail at any scale.

01 · Deployment

Keep control of where your data lives.

Whether your data lives in the cloud or behind your own firewall, NoteGuard gives you the flexibility to deploy on your terms. Connect your existing data sources, configure your privacy policies, and start processing without exposing raw text beyond your control.

  • Cloud, VPC, or air-gapped on-prem deployment
  • Connectors for EHRs, data lakes, object stores, and streams
  • Ready-to-use HIPAA/GDPR compliant policy templates
DEPLOY
NoteGuard login dashboard preview
02 · Configuration

Define exactly how sensitive data is transformed.

NoteGuard gives teams explicit control over how sensitive information is handled: from the policy applied to the transformation method used. Preview outputs before processing to ensure the results match your expectations.

  • Suppression, pseudonymization, date shifting, and token masking
  • HIPAA & GDPR Compliance Templates
  • Deterministic date-shift and ID crosswalks
CONFIGURE
03 · Detection

AI-powered detection built for clinical text.

NoteGuard combines high-recall clinical NLP with confidence-based escalation to identify sensitive entities and route uncertain cases for human review.

  • Transformer-based NER + rules-based detection
  • Token-level confidence scoring
  • Automated routing for human verification
DETECT
REVIEW
05 · Governance

Trust and reliability built on transparency.

Monitor usage, errors, policy outcomes, and retention across every workflow. NoteGuard maintains a complete record of how data was processed and what policies were applied, readily available for review or audit.

  • Policy revision history
  • Source-to-deidentified crosswalk logs
  • Immutable, exportable evidence packet
GOVERNANCE
DELIVER
Prevent downstream exposure with privacy controls at the source.
– NoteGuard design principle
Before and After

Protect the identity. Preserve the insight.

NoteGuard strips sensitive identifiers with >99.3% precision while leaving the clinical context that makes unstructured note data valuable intact. Masking, pseudonymization, replacement, or suppression methods based on your policy regulations and use case.

Original · Note 1284 8 identifiers
Patient Maria Gonzalez presented on 06/02/2024 with recurring chest discomfort. MRN 4471-8823, DOB 03/14/1972. Seen by Dr. James Whitfield at Cedar Valley Medical Center. Reachable at (415) 555-0192. Follow-up appointment scheduled 06/16/2024.
Redacted · Note 1284 Tokenized
Patient [NAME] presented on [DATE] with recurring chest discomfort. MRN [MRN], DOB [DOB]. Seen by Dr. [NAME] at [FACILITY]. Reachable at [PHONE]. Follow-up appointment scheduled [DATE].
Anonymized · Note 1284 Realistic surrogates
Patient Diane Foster presented on 05/28/2024 with recurring chest discomfort. MRN 6620-3391, DOB 01/09/1972. Seen by Dr. Robert Hale at Lakeside Medical Center. Reachable at (628) 555-0473. Follow-up appointment scheduled 06/11/2024.
Detected identifier De-identified token Realistic surrogate
Use cases

One privacy layer for every workflow.

From frontline documentation to large-scale model development, NoteGuard fits into the way clinical, research, and AI teams already work.

Clinical operations

Protect documentation and coding workflows while reducing manual review.

Use cases
  • Quality improvement
  • Dashboard Builds
  • Coding Audits
  • Outcomes Research

Research

Build compliant, analysis-ready datasets without losing longitudinal context.

Use cases
  • Cohort creation
  • Clinical research
  • Registry development
  • Longitudinal Studies

AI & ML

Prepare high-volume text data for model development and evaluation.

Use cases
  • LLM pipelines
  • NLP development
  • Model training
  • AI evaluation

Flexible deployment. Configurable policies.
Reliable protection.

Governance

Satisfy security requirements while keeping teams moving forward.

Privacy should never be the bottleneck. We focus on dependable obfuscation quality, low operational lift, and clear governance.

NoteGuard is built for high throughput environments where clinical notes and free-text records are continuously created, processed, and shared. Teams can customize redaction rules, apply them in real time or batch mode, and validate every output with consistent logs and review traces.

Zero-Retention Modes

Run ephemeral processing paths with no raw data persistence.

Access Governance

Restrict policy edits, approvals, and exports by role, and set up SSO access.

Operational Speed

Process high-volume streams with low-latency inference paths.

Measurable Outcomes

Benchmark quality, number of files processed, and reviewer workload.

NoteGuard + PixelGuard

Extend privacy across your data ecosystem.

Extend NoteGuard's intuitive de-identification workflow from clinical text to imaging and video with PixelGuard.

NoteGuard + PixelGuard is the only solution on the market that make it possible to de-identify multimodal data while preserving the relationships that make it useful. Build trustworthy datasets for the next generation of research, analytics, and AI.

Explore PixelGuard

One Connected Dataset

Create synchronized, analysis-ready datasets that keep clinical and visual data connected across ingestion and processing runs.

Multimodal Data Index

Bring de-identified notes, annotations, and imaging frames together in a single searchable record.

Consistent Patient Mapping

Maintain consistent record relationships across reruns without exposing source identity.

Timelines That Stay Intact

Preserve temporal patterns needed for longitudinal modeling while protecting original dates.

Start safeguarding note data

What could your teams do with secure access to every note?

De-identify clinical notes with confidence and empower your teams to move faster across research, collaborations, analytics, and AI.

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