Full-service Medical Data De-identification

Automatically de-identify structured data, unstructured data, documents, PDF files, and images in compliance with HIPAA, GDPR, or custom needs
>99%Accuracy on real-world documents
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Lessons Learned De-Identifying 700 Million Patient Notes with Spark NLP

The De-identification Service

1
Analyze
Human
  • Risk analysis​
  • Legal requirements review
  • HIPAA Safe Harbor, HIPAA Expert Determination​
  • CCPA​
  • GDPR pseudoanonymization, GDPR anonymization
  • Quality assurance strategy & process
Receive raw data
2
Identify
Software
  • ID, name, email, patient ID, SSN, credit card, address, birthday, phone, URL, license number
  • Physician name, hospital name, profession, employer, affiliation
  • Racial or ethnic origin, religion, political or union affiliation, biometric or genetic data, sexual practice or orientation
3
Measure
Human
  • Cleanroom AI Platform (on-site)
  • Annotation tool
  • Active learning
  • Accuracy Measurement & agreement processes
  • Correct sampling
  • Multi-lingual
4
De-identify
Software
We support:
  • Tabular (headers, values)
  • Text (NER, text matching)
  • PDF: Text or Scanned
  • Images (OCR & metadata)
  • DICOM (OCR & metadata)
So you can:
  • Replace (or delete a field)
  • Mask (hash identifiers or shift dates)
  • Obfuscate (name, locations, organizations)
  • Generalize (disease codes, dates, addresses)
Deliver de-identified data
5
Monitor
Human
  • Ongoing measurement & model improvement
  • Missed sensitive data
  • Incident response
  • GDPR & CCPA requests
  • Emergency unblinding
  • Audits

Full range of features

John Snow Labs’ De-identification solutions AWS Medical Comprehend Microsoft Presidio Google DLP
De-dentification tool
End-to-end service
Available also as a standalone library
Established new state of the art accuracy in peer reviewed publication
Real world reference with >99% correctly recognized PHI
Scanned PDF Integrated Separate service Separate service
DICOM Integrated Separate service Separate service
Obfuscation
Multilingual support
Built on big data framework
Possible to fine tune standard pre-trained models
Data does not leave your premise
Works in air gap insulated server with no internet access
  • Entities available out of box:
    ACCOUNT, AGE, BIOID, CITY, CONTACT, COUNTRY, DATE. DEVICE, DLN, DOCTOR, EMAIL, FAX, HEALTHPLAN, HOSPITAL, ID, IDNUM, IPADDR, LICENSE, LOCATION, LOCATION-OTHER, MEDICALRECORD, NAME, ORGANIZATION, PATIENT, PHONE, PLATE, PROFESSION, SSN, STREET, STATE, URL, USERNAME, VIN, ZIP
  • Easy to add other entities.
  • Works with virtually any input – text, scanned PDF, DICOM, docx, pptx.

De-identification in Action

Deidentify
structured data

Deidentify Protected Health Information (PHI) from structured datasets automatically while enforcing GDPR and HIPAA compliance and maintaining linkage of clinical data across files.

Deidentify free text
documents

Deidentify free text documents by either masking or obfuscating PHI using out-of-the-box, high-accuracy Spark NLP for Healthcare models.

Deidentify DICOM
documents

Deidentify DICOM documents by masking PHI information on the image and by either masking or obfuscating PHI from the metadata.

De-identify PDF documents – HIPAA Compliance

Deidentify PDF documents using HIPAA guidelines by masking PHI information using out of the box Spark NLP and Spark OCR models.

De-identify PDF documents – GDPR Compliance

Deidentify PDF documents using GDPR guidelines by anonymizing PHI information using out of the box Spark NLP and Spark OCR models.

De-identification Webinars