Medical Data De-identification
- Simple process & setup
- Automatically de-identify structured data, unstructured data, documents, PDF files, and images in compliance with HIPAA, GDPR, or custom needs
- Trusted by 5 of 8 Top Pharma Companies
>99%Accuracy on real-world documents
Accuracy:
99.19correctly de-identified sentences
Performance:
2.46hours
to de-identify 500K patient notes.
Test Your Data Live Through Interactive
De-identification Demo Tool
The De-identification Service
1
Analyze
- Risk analysis
- Legal requirements review
- HIPAA Safe Harbor, HIPAA Expert Determination
- CCPA
- GDPR pseudoanonymization, GDPR anonymization
- Quality assurance strategy & process
2
Identify
- 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
- Cleanroom AI Platform (on-site)
- Annotation tool
- Active learning
- Accuracy Measurement & agreement processes
- Correct sampling
- Multi-lingual
4
De-identify
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)
5
Monitor
- 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
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
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
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.
