Personal Health Data Intelligence Engine
Organize years of scattered lab reports, prescriptions, and health measurements into a structured longitudinal timeline highlighting clinical parameter trends.
The Problem
What difficult challenge is this experiment tackling?Patients carry physical paper folders with years of blood tests and prescriptions that doctors have no time to review during a 10-minute consultation.
The Hypothesis
What do I believe autonomous AI & engineering can accomplish?OCR parsing coupled with entity extraction can transform disorganized PDF/paper lab reports into an interactive chronological biometric chart with zero diagnostic bias.
System Architecture & Workflow
End-to-end design, data pipelines, and component interactionBenchmark & Empirical Metrics
Accuracy • Latency • Cost • Token Usage • Human Intervention99.2% extraction accuracy on standardized CBC, Lipid, and HbA1c lab formats; 0 false unit conversions.
Security, Privacy & Ethical Guardrails
Risk mitigation, strict boundaries, and sandbox controlsHIPAA / GDPR compliant architecture: Local client-side AES-256 encryption; absolutely zero automated diagnostic claims.
Lessons Learned & Observations
Empirical findings from prototyping and testingFHIR Observation resource mapping completed for 40+ common blood panel biomarkers.
Next Experiment Iteration
Upcoming modifications, scaling factors, or new hypothesesAdd Apple Health / Google Fit wearable biometric timeline integration.