Healthcare & Life Sciences
HIPAA-Compliant AI for Healthcare
Process patient data with AI while maintaining full HIPAA compliance. Automatic PHI detection, tokenization, and Safe Harbor de-identification.
The challenge
Without protection
- PHI exposed to AI model training data
- HIPAA violations with potential $50K+ fines per record
- Patient names, DOBs, SSNs in application logs
- Manual redaction = human error + compliance risk
With Blindfold
- Automatic PHI tokenization before AI processing
- HIPAA Safe Harbor de-identification compliance
- Zero PHI retention — processed in RAM only
- 2-line integration with existing healthcare systems
Common use cases
Clinical Documentation
Use AI to generate clinical notes, discharge summaries, and treatment plans without exposing patient PHI.
Patient Support
AI-powered chatbots and support systems that handle patient inquiries while maintaining HIPAA compliance.
Medical Research
Analyze patient data with AI while ensuring de-identification for research and analytics purposes.
HIPAA Safe Harbor compliance
Blindfold automatically detects and protects all 18 HIPAA PHI identifiers.
NamesAddressesDates (birth, admission, discharge, death)Phone numbersEmail addressesSSNMedical record numbersHealth plan numbersAccount numbersCertificate/license numbersVehicle identifiersDevice identifiersBiometric identifiersFull-face photosIP addressesURLsFax numbersAny unique identifying number
Integration example
python
from blindfold import Blindfold client = Blindfold(api_key="...") # Patient data with PHI note = """ Patient: Sarah Johnson DOB: 1985-03-15 SSN: 123-45-6789 Diagnosis: Type 2 Diabetes Email: sarah.j@email.com """ # Tokenize PHI automatically safe = client.tokenize(note) # Send to AI safely from openai import OpenAI response = OpenAI().chat.completions.create( model="gpt-4", messages=[{"role": "user", "content": f"Summarize: {safe.text}"}] ) # Restore original PHI in the response result = client.detokenize(response.choices[0].message.content, safe.mapping)