Quantified SelfAI CoachAnalytics

Health OS

Architected a secure personal health operating system. Embedded rigid FHIR standards directly into consumer flows, automatically pulling wearables and lab data into a unified, highly actionable clinical timeline.

RoleBuilder / Founder
Timeframe6 Months
ImpactBuilt an HL7/FHIR + HIPAA compliant data aggregation architecture from absolute zero
StackNext.js, HL7/FHIR, AI Coaching
01 / Context

The Problem

Wearables generate gigabytes of health data. Lab results pile up in PDFs. Most of this gets ignored because there's no system tying it together—no way to see patterns across sleep, nutrition, activity, and bloodwork.

I built Health OS to fix that. A personal operating system that ingests data from every source, applies FHIR/HL7 standards, and surfaces insights that actually matter.

02 / Strategy

Approach

Build Principles

  • • Ship fast, iterate on real feedback
  • • Start with constraints, not features
  • • Measure what actually matters

Technical Moat

Domain expertise in Quantified Self. Systems built for scale without overengineering. Pragmatic tech choices that ship.

03 / Execution

What We Built

Health OS Architecture

Systems Architecture & Data Flow

Technical constraints forced creative solutions. We optimized for Quantified Self from day one, which meant rethinking architecture at every layer. Shipped incrementally, validated with real users, and scaled what worked.

04 / Results

Impact

Built

Built an HL7/FHIR + HIPAA compliant data aggregation architecture from absolute zero

What I Learned

AI products need to be grounded in real constraints. Hallucinations kill trust. Accuracy at scale beats feature bloat.

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A methodology for moving AI from experiments to operating systems. Four phases, built on lessons from digital and cloud transformation waves, tested across healthcare, consumer tech, and agencies.