Medical records that speak plainly to time-pressed doctors
KRS ingests entire patient medical histories — multi-year PDFs, lab reports, imaging notes, clinical letters and discharge summaries — and produces concise, clinically accurate summaries in seconds using a custom RAG pipeline. Designed for time-pressed clinicians who need the full patient picture in 4 minutes, not 40.
Medical professionals were spending 30–45 minutes per patient reviewing records scattered across multiple hospital systems. Critical context was regularly missed; consultation time was being consumed by record archaeology rather than patient care. The system needed to handle fragmented, multi-format data with clinical-grade accuracy and HIPAA-compliant privacy.
A focused, deliberate process — from discovery to deployment.
Built a document ingestion pipeline supporting PDF, DOCX, HL7 FHIR and image-based medical reports. Custom OCR with medical layout awareness handles handwritten notes, lab tables and ICD-coded discharge summaries.
Designed a RAG pipeline using medical-domain text embeddings and a vector database for semantic search across patient history — with context window management that handles records spanning 10+ years without truncation.
Fine-tuned GPT-4 with clinical prompt engineering and medical NLP post-processing to generate structured summaries covering presenting history, diagnoses, medications, allergies, investigations and recommendations — validated by a clinician panel.
Deployed on HIPAA-compliant infrastructure with end-to-end encryption at rest and in transit, no PII retention beyond session, full audit logging of every query and access, and role-based access control for clinical teams.
Real impact, measured and validated.
Every tool chosen deliberately for the problem at hand.
I can now get everything I need on a patient in 4 minutes instead of spending half my consultation reading notes from 3 different hospital systems. This is what clinical AI should feel like.