Structuring Clinical Text Data for AI Diagnostics

The Challenge:

A healthcare tech firm developing AI-powered diagnostic tools needed to train NLP models to extract medical insights from unstructured electronic health records (EHRs). Their biggest bottleneck was the lack of annotated data labeled with medical terminology, symptoms, medications, and patient behavior.

Our Solution:

LabelCo.AI assembled a specialized medical annotation team with prior experience in HIPAA-compliant data handling. The project involved:

• Named Entity Recognition (NER) for diseases, medications, dosages, allergies, procedures

• Sentiment tagging to distinguish between symptoms present, absent, or historical

• De-identification of patient-sensitive information (PHI masking)

• Multilingual annotation in English and regional languages

Results:

• Processed over 200,000 medical documents in 3 months

• Achieved 99.1% labeling accuracy

• Reduced clinical NLP model errors by 35%

• Enabled faster deployment of AI-powered medical assistants in remote clinics