🧬HyperHealth

AI Infrastructure for Clinical Diagnostics, Research, and Insurance Risk Intelligence

Overview

Purpose: HyperHealth is a modular, privacy-preserving AI ecosystem designed for hospitals, laboratories, and insurance companies. It connects real-time medical data (EHRs, test results, imaging, and clinical notes) with advanced AI models to support diagnosis, research, and risk evaluation — while ensuring full patient data control through federated learning and blockchain consent management.

Core Modules:

Medical Data Copilot — Generates diagnostic insights from patient histories, lab tests, and doctor notes.
AI Clinical Trial Assistant — Optimizes patient recruitment, data validation, and outcome prediction for trials.
Insurance Risk AI — Calculates AI-driven health risk scores to support insurance underwriting and policy design.

Technical Architecture

System Layers

Layer

Components

Description

Data Layer

Federated Data Vaults, FHIR Connectors

Connects distributed hospital/lab systems while keeping raw data local.

AI Layer

Multi-Modal Diagnostic Transformer, Risk LLM, Predictive Models

Learns from text (notes), structured (lab results), and image (radiology) data.

Privacy Layer

Differential Privacy Engine, Federated Aggregator

Ensures model training without centralized data collection.

Blockchain Layer

Consent Smart Contracts, Audit Ledger

Handles patient permissions, access tracking, and data provenance.

Application Layer

Dashboards, APIs, Insurance Scoring Tools

Provides interfaces for clinicians, researchers, and insurers.

Model Explanation

A. Medical Data Copilot

Input: EHR (structured), clinical notes (text), imaging metadata.
Architecture: Multi-modal Transformer combining BERT-based medical text encoder + tabular feature network.
Output: Diagnostic suggestions with confidence levels and reasoning trail.
Training: Federated learning across hospital clusters, aggregated via secure multiparty computation.

B. AI Clinical Trial Assistant

Input: Patient attributes, eligibility criteria, genomic/lab data.
Architecture: Graph Neural Network (GNN) models patient–criteria relations.
Output: Ranked patient selection list, predicted trial success probability.

C. Insurance Risk AI

Input: Health records, lifestyle data, claim history.
Architecture: Gradient-boosted ensemble + Transformer for feature extraction.
Output: Health risk score (0–1), claim probability model, and policy premium range.

Data Flow & Diagram

Simplified Flow Diagram

[Hospitals/Labs] → [Federated Data Vaults] → [AI Diagnostic Agents] → [Blockchain Consent Layer]
                      ↘                                      ↙
                 [Clinical Trials]                 [Insurance Partners]

Process Explanation

Data Ingestion: FHIR connectors map local health data into standardized schema.
Federated Training: Models train locally, share encrypted gradients only.
Consent Management: Patients approve or revoke AI model access via on-chain smart contracts.
Output Delivery: AI agents generate diagnostic or risk insights accessible through APIs.

Integration Scenarios

Stakeholder

Integration Example

Benefit

Hospitals

Connect existing EHR (HL7/FHIR) systems via HyperHealth API.

Real-time AI diagnosis without data sharing.

Labs

Link lab instruments or LIMS to AI analysis module.

Automatic anomaly detection, pattern recognition.

Pharma / CROs

Integrate clinical trial datasets for patient matching.

40–60% faster recruitment cycles.

Insurance Providers

Use Insurance Risk AI through HyperHealth Oracle.

Data-driven policy pricing and fraud reduction.

Web2 Integration: EHR APIs, Lab Information Systems, CRM tools. Web3 Integration: Data NFT issuance (for anonymized datasets), smart contract–based patient consent.

Blockchain & Privacy Design

Data Privacy

Federated Learning: Raw data never leaves local hospital servers.
Homomorphic Encryption: Gradient updates are encrypted end-to-end.
Differential Privacy Noise: Prevents patient re-identification from model outputs.

Blockchain Integration

Consent Ledger: Each patient’s consent stored as an immutable smart contract.
Data NFT (optional): Allows patients to monetize anonymized datasets for research.
Audit Chain: Logs every model access and output generation event.

Token Utility Model

Function

Description

Token Mechanism

AI Compute Access

Hospitals/labs spend HGPT to query AI diagnostics or training cycles.

Pay-per-inference model.

Data Sharing Rewards

Patients earn HGPT for voluntarily sharing anonymized health data.

Incentive staking.

Model Governance

Medical institutions stake HGPT to vote on AI model updates.

DAO-based model validation.

Insurance Oracle Access

Insurers use HGPT to retrieve risk metrics securely.

Subscription/stake-to-access.

Example Use Case

Scenario: A regional hospital consortium integrates HyperHealth.

Doctors input patient symptoms → AI suggests possible conditions with ranked confidence.
Trial team searches the AI Clinical Trial Assistant → identifies qualified patients automatically.
Insurance partner queries Insurance Risk AI → generates personalized premium pricing.
All data interactions are governed by patient smart contracts and logged on-chain.

Outcome:

30–50% faster diagnostic cycles
Federated, compliant data learning
Lower fraud & higher accuracy in insurance modeling

Diagram (Conceptual Architecture)

                    ┌──────────────────────────────┐
                    │     HyperHealth Cloud AI     │
                    │ ┌──────────────────────────┐ │
                    │ │  Diagnostic AI (Copilot) │ │
                    │ │  Trial Assistant (GNN)   │ │
                    │ │  Risk AI (Ensemble)      │ │
                    │ └──────────────────────────┘ │
                    └──────────────┬───────────────┘
                                   │
     ┌─────────────────────────────┼────────────────────────────┐
     │                             │                            │
 [Hospital Nodes]         [Lab Systems]                [Insurance Portals]
  (Federated Learning)     (FHIR APIs)                 (Risk Scoring Engine)
     │                             │                            │
     └─────────────→ [Blockchain Consent & Audit Layer] ←──────────────┘