Technical Infrastructure
MedAxis runs a Next.js/TypeScript web app on Vercel with an API gateway routing secure requests to Python microservices on AWS. Streaming vitals and clinic datasets flow into a data lake, with PostgreSQL storing clinical records and a time-series layer handling high-frequency vitals. ML pipelines build features, train predictive risk models, register and deploy optimized inference endpoints (TensorRT/Triton-ready), and trigger real-time clinical alerts via dashboards and notifications.
Frontend & Edge Delivery
A blazing-fast, globally distributed frontend ensures clinicians and patients experience near-zero latency regardless of location.
Web Application
Next.js + TypeScript on Vercel hosting for the marketing site and clinician/patient dashboards.
Edge + CDN
Vercel Edge Network for low-latency routing with optional CloudFront/Cloudflare for enterprise controls — WAF rules, bot protection, and geo-restrictions.
Auth UI
OAuth2/OIDC login flow (Cognito/Auth0/Keycloak) supporting clinicians, admins, and patients with compliance-grade session management.
API Layer & Core Backend
A secure API gateway and Python microservices power the core logic — from patient records to AI model orchestration.
API Gateway
Single entry point with REST/GraphQL endpoints for patient profiles, vitals ingestion, alerts, and analytics. Includes rate limiting, request validation, and JWT verification.
Core Microservices (Python)
Specialized services for patient context aggregation, clinical alerting rules engine, workflow routing, and model orchestration (feature creation → inference → post-processing).
Asynchronous Jobs
Queue-based workers (SQS/Kafka + Celery or serverless Lambda) handle data normalization, batch scoring, and cohort analytics.
Data Ingestion — Real-Time + Batch
Continuous monitoring and early detection demand both streaming and batch data pathways ingesting from devices, EHRs, and clinical systems.
Real-Time Vitals Streaming
Devices, EHR systems, and clinic apps push events (heart rate, SpO₂, BP, temperature, respiration, glucose) into Kinesis/Kafka streams or API Gateway + Lambda for simpler paths.
Batch Clinical Datasets
Periodic uploads from clinic EHR exports and historical data dumps land in object storage (S3) — raw/immutable buckets plus structured "curated" datasets.
Data Storage & Analytics
A purpose-built storage architecture optimized for clinical record management, high-frequency vitals, and population-scale analytics.
PostgreSQL (RDS/Aurora)
Patient profiles, consents, clinic org structure, care plans, intervention logs, clinician notes, and alert lifecycle tracking (created → acknowledged → resolved).
Time-Series Store
TimescaleDB (Postgres extension) or Timestream/InfluxDB optimized for high-frequency vitals ingestion and clinical trend queries.
Data Lake (S3)
Raw device/EHR events, cleaned/standardized datasets, feature tables, training snapshots, labels, and model artifacts.
Warehouse (Enterprise)
Redshift / BigQuery / Snowflake for cohort analytics, population health dashboards, and cost/outcome reporting.
AI/ML Pipeline
End-to-end machine learning — from feature engineering through validation to production deployment of predictive risk models.
Feature Engineering
De-identified data mapped to clinical schemas (FHIR-like). Features include rolling vitals windows (1h/6h/24h), trend slope/variance, missingness patterns, and comorbidity context. Built on Spark/Glue/Ray at scale.
Model Training
Temporal deep learning (LSTM/TCN/Transformer) paired with gradient boosting for interpretability. Ensemble approach combines deep models with calibrated risk models. GPU-accelerated on NVIDIA hardware.
Validation & Governance
Offline evaluation via AUROC/AUPRC, calibration curves, and subgroup performance. Model registry (MLflow/SageMaker) with full audit trails: dataset, feature, model, and inference versioning.
Deployment
Real-time inference via containerized model servers with autoscaling. Triton Inference Server for multi-model serving. TensorRT optimization for lower latency. Nightly batch scoring for population risk.
Alerting & Clinical Workflow
Risk scores become actionable through severity-tiered alerts routed to the right clinician at the right time.
Alert Engine
Converts risk scores into severity tiers (low/medium/high) and routes alerts to clinician dashboards and care coordination queues.
Notification Channels
Multi-channel delivery via SMS, email, and WhatsApp push notifications (SNS/Twilio/SendGrid).
Workflow Integration
Outbound webhooks + HL7/FHIR connectors for seamless integration into existing clinic workflows and EHR systems.
Security, Privacy & Compliance
Healthcare-grade security controls baked in from day one — encryption, access control, audit logging, and threat protection.
Encryption
TLS in transit. AES-256 at rest for all databases and object storage.
Access Control
Role-based access (clinician vs admin vs patient) with least-privilege IAM policies.
Data Protection
De-identification and tokenization pipeline for model training datasets. Per-tenant data isolation for multi-clinic deployments.
Monitoring & Threat Defense
CloudWatch/Datadog + centralized audit logs. Anomaly detection on auth and access patterns. AWS WAF / Shield or Cloudflare WAF for DDoS and bot mitigation.
Reliability & Scalability
Built for 99.9% uptime with container orchestration, autoscaling inference, and multi-region disaster recovery.
Container Orchestration
ECS/EKS for microservices and model servers with automated health checks and rolling deployments.
Autoscaling
Inference servers scale dynamically based on queue depth and latency metrics.
Multi-Region DR
Active-passive disaster recovery option for enterprise health networks requiring geographic redundancy.
Uptime Target
99.9% platform availability — monitored, measured, and continuously improved.
Ready to Build on a Platform That Scales?
MedAxis is designed to grow with your organization — from a single clinic pilot to a nationwide health network. Let's discuss your infrastructure needs.
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