Nura Health Diagnostics

A complete overhaul of the Nura platform utilizing high-fidelity web engineering and cinematic interaction models for medical analytics. The goal was to make complex biomarker data feel intuitive for both clinicians and patients — turning walls of lab results into actionable health narratives.

The biomarker panel above shows the real-time monitoring view — ECG waveform with heart rate, and three key health markers (VO2 max, heart rate variability, C-reactive protein) rendered as animated progress bars.

Context

Nura Health is a longevity medicine platform that aggregates lab work, wearable data, and genomic markers into a unified patient profile. The existing dashboard was a standard React table grid — functional but impenetrable for patients and tedious for clinicians reviewing dozens of profiles a day.

Design Philosophy

We approached the redesign with three principles:

1. Data as narrative — biomarker trends should tell a story, not present a spreadsheet
2. Progressive disclosure — surface the signal, let users drill into the noise
3. Cinematic quality — health data deserves the same visual care as a Bloomberg terminal

Architecture

The frontend is a SvelteKit application backed by a Python API layer that handles data aggregation and LLM-powered insight generation. Patient data flows through a pipeline that normalizes heterogeneous lab formats, computes derived metrics, and generates natural-language summaries.

LLM Integration

Each patient profile includes an AI-generated health brief produced by a fine-tuned model that ingests the normalized biomarker history and outputs a structured summary. The model was trained to flag clinically relevant trends (e.g., “fasting glucose trending upward over 6 months despite stable HbA1c”) rather than generic observations.

Visualization Layer

Biomarker data is rendered as animated sparkline rivers — continuous curves with reference range bands that shift from green to amber to red. Hovering a point reveals the exact value, lab source, and date. Zooming out collapses individual markers into organ-system health scores.

Key Features

• Unified patient timeline across labs, wearables, and genomics
• LLM-generated health briefs with citation links to source data
• Sparkline rivers for multi-year biomarker trends with reference bands
• Organ-system health scores aggregated from individual markers
• Clinician batch review mode for efficient multi-patient workflows

Stack

Layer	Technology
Frontend	SvelteKit
API	Python (FastAPI)
LLM	Fine-tuned GPT-4 via Azure OpenAI
Data	PostgreSQL, Redis
Visualization	D3.js, custom SVG