Healthcare AI & Infrastructure

AI-Powered Medical Decision Support System

Developed an advanced healthcare AI system that combines fine-tuned large language models with a comprehensive EHR platform. The solution includes clinical decision support, medical documentation automation, and early intervention identification deployed across web and native mobile applications.
AI-Powered Medical Decision Support System
Case study

Context, approach, and outcome

The project page should read like an actual delivery narrative: what had to change, how the system was structured, and what happened once it shipped.

Challenge

A major healthcare provider needed to improve diagnostic accuracy, reduce clinician documentation burden, and identify early intervention opportunities while ensuring strict compliance with healthcare regulations and maintaining the highest level of data security.

Solution

We created a comprehensive AI-powered medical system that included a fine-tuned LLM for clinical decision support, a complete EHR platform, and native mobile applications, all powered by a secure cloud infrastructure and continuous learning pipeline.

Outcome

The system achieved 93% accuracy in diagnostic assistance, reduced clinical documentation time by 67%, and increased early intervention case identification by 41%, all while maintaining full HIPAA compliance and seamless integration with existing workflows.

Technical detail

Inside the build

These details are here to show how the system was actually put together, not to inflate the stack for its own sake.

AI

  • Open-source LLM model fine-tuned on medical terminology and data
  • Continuous learning pipeline with automated retraining
  • Python-based inference API with TensorFlow
  • Custom prompt engineering for medical contexts

Architecture

  • Monorepo structure using Turborepo for code sharing
  • Microservices architecture for scalable backend
  • Event-driven design for real-time updates
  • HIPAA-compliant data flows and storage

Backend

  • NestJS backend with TypeScript
  • Custom APIs for EHR integration
  • AWS Lambda functions for serverless operations
  • API Gateway for secure access control
  • EC2 instances for compute-intensive tasks

Frontend

  • Next.js web application with SSR for performance
  • ShadCN component library with Tailwind CSS
  • Real-time dashboard for clinical metrics
  • Adaptive UI for different medical specialties

Mobile

  • Native iOS development with Swift
  • Native Android development with Kotlin
  • Offline capabilities for remote healthcare settings
  • Secure biometric authentication

Data

  • PostgreSQL for structured clinical data
  • SurrealDB for complex healthcare relationships
  • FHIR-compliant data modeling
  • Encrypted data at rest and in transit

DevOps

  • Custom Kubernetes cluster for LLM hosting
  • Docker containerization for all services
  • CI/CD pipeline with automated testing
  • Infrastructure as Code with Terraform
Technology

Stack in play

The stack matters because it shows the shape of the system and the constraints it had to satisfy.
Fine-tuned LLMsKubernetesNext.jsNestJSTurborepoTypeScriptShadCNTailwind CSSPostgreSQLSurrealDBAWSSwiftKotlinPythonTensorFlowDockerLambdaAPI GatewayEC2

Start here

Need this kind of delivery pressure handled well?

We can help shape the next product system, technical platform, or AI workflow so the outcome is legible from architecture through launch.

Start the conversation