Healthcare

Iyara

AI-Powered Healthcare Innovation

Built AI-powered systems for healthcare delivery optimization and patient care improvement.

Healthcare
5 Key Outcomes
Problem

The Challenge

Iyara was building AI-powered solutions to improve healthcare delivery, but their initial approach relied on generic AI models that didn't understand medical context. They needed custom AI systems that could process medical documents, assist with diagnosis workflows, and integrate with existing healthcare information systems — all while maintaining strict data privacy and regulatory compliance.

The complexity of healthcare data, combined with the critical nature of medical decisions, required a solution that was both intelligent and explainable.

Solution

The Approach

1

Developed custom NLP models fine-tuned on medical literature and clinical notes

2

Built a RAG system with medical knowledge bases for context-aware responses

3

Implemented audit trails and explainability features for clinical decision support

4

Created integration layers for HL7/FHIR healthcare data standards

5

Designed human-in-the-loop workflows for high-stakes decisions

Technology

The Stack

AI/ML

Python LangChain Custom LLM Fine-tuning

Backend

FastAPI PostgreSQL Vector DB

Integration

HL7 FHIR REST APIs

Compliance

HIPAA GDPR Audit Logging
Results

The Outcomes

Reduced document processing time by 70%
AI accuracy rate of 94% on supported use cases
Full audit trail for regulatory compliance
Integration with 5 major healthcare IT systems
Clinician satisfaction score of 4.6/5
Lesson Learned

Key Insight

" Healthcare AI isn't about replacing doctors — it's about giving them superpowers. The most successful features weren't the ones that made autonomous decisions, but the ones that surfaced relevant information at the right moment. We learned to position AI as a "second pair of eyes" rather than a replacement, which dramatically improved clinician adoption and trust. "

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