Architecture

Technical architecture overview

SynThera System Architecture

SynThera's architecture is built on modern cloud-native principles, designed for scalability, reliability, and security. Our microservices-based system leverages advanced AI/ML pipelines to deliver real-time clinical intelligence while maintaining HIPAA compliance and enterprise-grade performance.

Key Architectural Principles

• Cloud-native microservices
• Event-driven architecture
• Zero-trust security model

• Auto-scaling infrastructure
• Multi-region deployment
• API-first design

High-Level System Overview

SynThera Platform Architecture


┌─────────────────────────────────────────────────────────────────────┐
│                          Client Applications                         │
│  ┌─────────────┐  ┌─────────────┐  ┌─────────────┐  ┌─────────────┐ │
│  │   Web App   │  │  Mobile App │  │  EHR Plugin │  │  API Client │ │
│  └─────────────┘  └─────────────┘  └─────────────┘  └─────────────┘ │
└─────────────────────────────────────────────────────────────────────┘
                                    │
                                ┌───▼───┐
                                │  CDN  │
                                └───┬───┘
                                    │
┌─────────────────────────────────────────────────────────────────────┐
│                            API Gateway                              │
│  ┌─────────────┐  ┌─────────────┐  ┌─────────────┐  ┌─────────────┐ │
│  │    Auth     │  │Rate Limiting│  │  Routing    │  │  Monitoring │ │
│  └─────────────┘  └─────────────┘  └─────────────┘  └─────────────┘ │
└─────────────────────────────────────────────────────────────────────┘
                                    │
┌─────────────────────────────────────────────────────────────────────┐
│                         Core Microservices                         │
│  ┌─────────────┐  ┌─────────────┐  ┌─────────────┐  ┌─────────────┐ │
│  │  Clinical   │  │    Voice    │  │   Imaging   │  │    FHIR     │ │
│  │  Analysis   │  │ Processing  │  │  Analysis   │  │ Integration │ │
│  └─────────────┘  └─────────────┘  └─────────────┘  └─────────────┘ │
│  ┌─────────────┐  ┌─────────────┐  ┌─────────────┐  ┌─────────────┐ │
│  │   Patient   │  │    Risk     │  │    Lab      │  │  Workflow   │ │
│  │    Data     │  │ Assessment  │  │Integration  │  │   Engine    │ │
│  └─────────────┘  └─────────────┘  └─────────────┘  └─────────────┘ │
└─────────────────────────────────────────────────────────────────────┘
                                    │
┌─────────────────────────────────────────────────────────────────────┐
│                         AI/ML Infrastructure                       │
│  ┌─────────────┐  ┌─────────────┐  ┌─────────────┐  ┌─────────────┐ │
│  │   Model     │  │  Training   │  │ Inference   │  │  Knowledge  │ │
│  │ Repository  │  │  Pipeline   │  │   Engine    │  │    Graph    │ │
│  └─────────────┘  └─────────────┘  └─────────────┘  └─────────────┘ │
└─────────────────────────────────────────────────────────────────────┘
                                    │
┌─────────────────────────────────────────────────────────────────────┐
│                           Data Layer                               │
│  ┌─────────────┐  ┌─────────────┐  ┌─────────────┐  ┌─────────────┐ │
│  │   Primary   │  │   Search    │  │    Cache    │  │   Object    │ │
│  │  Database   │  │   Engine    │  │    Layer    │  │   Storage   │ │
│  └─────────────┘  └─────────────┘  └─────────────┘  └─────────────┘ │
└─────────────────────────────────────────────────────────────────────┘

Client Layer

Multi-platform client applications and integrations

API Gateway

Authentication, routing, and traffic management

Microservices

Domain-specific business logic and processing

Data Layer

Persistent storage and caching infrastructure

Core Microservices

Clinical Analysis Service

Core AI engine for patient data analysis and clinical decision support.

Technologies: Python, TensorFlow, PyTorch, FastAPI

• Multi-modal data processing
• Real-time risk assessment
• Specialty-specific models

Voice Processing Service

Advanced speech recognition and natural language processing for medical content.

Technologies: Whisper, spaCy, BERT, WebRTC

• Real-time transcription
• Medical entity extraction
• SOAP note generation

Imaging Analysis Service

AI-powered medical imaging analysis and interpretation.

Technologies: MONAI, PyTorch, DICOM, OpenCV

• Multi-modality support
• Anomaly detection
• Measurement tools

FHIR Integration Service

Healthcare data interoperability and EHR integration.

Technologies: HAPI FHIR, HL7, Node.js

• FHIR R4 compliance
• Data transformation
• EHR connectors

Patient Data Service

Secure patient data management and privacy protection.

Technologies: PostgreSQL, Redis, Vault

• Data anonymization
• Consent management
• Audit trails

Workflow Engine

Orchestrates complex clinical workflows and business processes.

Technologies: Temporal, Go, Apache Kafka

• Process automation
• Error handling
• State management

AI/ML Infrastructure

Model Lifecycle Management

Training Pipeline

Stack: Kubeflow, MLflow, DVC, Weights & Biases

• Automated data preprocessing
• Distributed training on GPUs
• Hyperparameter optimization
• Model versioning and lineage

Model Registry

Stack: MLflow Model Registry, Harbor

• Centralized model storage
• Version control and metadata
• Model approval workflows
• A/B testing framework

Inference Infrastructure

Model Serving

Stack: TensorFlow Serving, ONNX Runtime, Triton

• High-throughput inference
• Auto-scaling based on load
• Multi-model endpoints
• GPU acceleration support

Feature Store

Stack: Feast, Apache Kafka, Redis

• Real-time feature serving
• Feature versioning
• Training-serving consistency
• Feature monitoring

Data Architecture

Data Storage

Primary Database

PostgreSQL Cluster

• Multi-master replication
• Automatic failover
• Point-in-time recovery
• Encrypted at rest

Object Storage

Amazon S3 / MinIO

• Medical images & files
• Model artifacts
• Backup & archival
• Lifecycle policies

Data Processing

Stream Processing

Apache Kafka & Flink

• Real-time data ingestion
• Event sourcing
• Complex event processing
• Exactly-once semantics

Batch Processing

Apache Spark

• ETL pipelines
• Data analytics
• Model training data prep
• Historical analysis

Data Services

Search Engine

Elasticsearch

• Full-text search
• Clinical terminology
• Semantic search
• Analytics & reporting

Cache Layer

Redis Cluster

• Session management
• API response caching
• Real-time features
• Rate limiting

Security Architecture

Zero-Trust Network

┌─────────────────────────────┐
│        Internet             │
└────────────┬────────────────┘
             │
        ┌────▼────┐
        │   WAF   │ ← DDoS Protection
        └────┬────┘
             │
        ┌────▼────┐
        │   ALB   │ ← Load Balancing
        └────┬────┘
             │
    ┌────────▼────────┐
    │  API Gateway    │ ← Auth & Rate Limiting
    └────────┬────────┘
             │
    ┌────────▼────────┐
    │ Service Mesh    │ ← mTLS & Observability
    │  (Istio)        │
    └─────────────────┘

End-to-end encryption (TLS 1.3)
Mutual TLS between services
Network segmentation
Identity-based access control

Data Protection

Encryption Strategy

• AES-256 for data at rest
• Hardware Security Modules (HSM)
• Key rotation every 90 days
• Envelope encryption pattern

Privacy Controls

• Data anonymization pipelines
• Differential privacy
• Consent management
• Right to be forgotten

Audit & Compliance

• Immutable audit logs
• HIPAA compliance monitoring
• Data lineage tracking
• Automated compliance reporting

Deployment Patterns

Cloud-Native

Technologies: Kubernetes, Helm, ArgoCD

• Multi-cloud deployment
• Auto-scaling workloads
• GitOps workflows
• Blue-green deployments
• Canary releases

Hybrid Deployment

Technologies: Anthos, Arc, VPN Gateway

• On-premises + cloud
• Edge computing nodes
• Data locality compliance
• Federated identity
• Unified monitoring

Air-Gapped

Technologies: K3s, Harbor, Longhorn

• Completely isolated
• Local container registry
• Offline model updates
• Manual security patches
• Local data processing

Monitoring & Observability

Three Pillars of Observability

Metrics

Stack: Prometheus, Grafana, AlertManager

• Application performance metrics
• Infrastructure monitoring
• AI model performance
• Business metrics

Logging

Stack: ELK Stack, Fluentd, Loki

• Centralized log aggregation
• Structured logging
• Log correlation
• Audit trail

Tracing

Stack: Jaeger, OpenTelemetry

• Distributed tracing
• Request flow visualization
• Performance bottlenecks
• Error root cause analysis

AI/ML Monitoring

Model Performance

• Accuracy drift detection
• Prediction latency
• Feature drift monitoring
• Model bias detection

Data Quality

• Schema validation
• Data completeness checks
• Anomaly detection
• Data freshness monitoring

Business Impact

• Clinical outcome tracking
• User satisfaction metrics
• Cost per prediction
• ROI measurement

View Deployment Guide Security Documentation