Case Study • AI & Healthcare

HEALTH
INTELLIGENCE.

Designing an AI Powered Health Intelligence System

Healthcare data is fragmented. Reports sit in PDFs. Prescriptions get ignored. Patterns go unnoticed. So I designed an AI driven system that does more than store reports. It learns from them.

Neural Health Engine

Zero-Trust Architecture

Role

Product Architect

Focus

AI/ML & Data Engineering

Infrastructure

Microservices

Stack

Next.js • LLMs • Python

The Vision.

The goal is simple: transform static medical documents into a living intelligence system. This is not a reminder app. This is a continuous health intelligence engine designed to understand long-term patterns and detect risks early.

Read medical reports

Understand prescriptions

Learn long term patterns

Detect risk trends early

Adapt based on behavior

Provide contextual AI help

System Architecture.

The system follows a structured, modular pipeline where each stage transforms raw health data into intelligent insights.

User Uploads Medical Reports

Secure Data Intake Layer

OCR + NLP Extraction Engine

Structured Medical Data (JSON)

Feature Engineering Layer

Pattern Learning Model

Risk Scoring Engine

Adaptive Reminder Engine

AI Health Assistant Interface

Continuous Learning Feedback Loop

Extraction Engine.

Medical reports are messy. They come in PDFs, scanned images, or lab formatted layouts. The system processes them using OCR for scanned documents and NLP based medical entity recognition.

OCR Extraction

Converting pixels to text

Term Identification

Recognizing medical entities

JSON Output

Structured normalization

Trend Analysis

Raw numbers are useless without context. The system converts medical values into meaningful indicators like longitudinal health tracking and sudden anomaly detection.

Deviation from normal
Month over month trends
Time series modeling
Risk indicator generation

Prescription IQ

Analyzes drug interaction possibilities and adherence patterns. If a user repeatedly ignores a morning dose, the system suggests optimized timing.

Adaptive Logic

"User ignores morning dose? Suggest evening alternative."

AdherenceDosageInteractions

The AI Assistant.

Powered by LLM Reasoning, it explains lab results in simple language and suggests lifestyle improvements based on historical data.

It does not guess. It reasons over structured medical context to generate doctor-ready summaries.

// AI Reasoning Loop

const context = getStructuredHistory(userId);

const response = await llm.reason({

data: context,

task: 'summarize_risk_trends',

tone: 'empathetic_professional'

});

Zero Trust.

Health data demands strict protection. The system implements end-to-end encryption and data anonymization before any AI processing.

Encryption

AES-256 at rest & TLS 1.3 in transit

Anonymization

PII removal before LLM inference

Audit Logging

Every data access is tracked

Understanding Health.

This platform transforms static medical documents into a living intelligence system. Not just tracking health. Understanding it.

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HEALTH INTELLIGENCE.