AI-Powered Medical Decision Support System
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.

93% diagnostic assistance accuracy
67% reduction in clinical documentation time
41% increase in early intervention cases

Project Overview

We developed an advanced AI-powered medical decision support system for a major healthcare provider. Our solution combines cutting-edge language model technology with traditional healthcare information systems to create a comprehensive platform that assists medical professionals with diagnoses, reduces documentation time, and improves patient outcomes.

The heart of our solution is an open-source LLM that we fine-tuned specifically for medical terminology and contexts. We implemented a continuous learning pipeline that constantly improves the model based on new data and feedback, all while maintaining strict compliance with healthcare security and privacy regulations.

The 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.

  • Medical Accuracy: Achieving high diagnostic accuracy with AI that could safely assist medical professionals
  • Documentation Burden: Reducing the significant time clinicians spend on documentation while maintaining quality
  • Early Intervention: Identifying patients who would benefit from early interventions by analyzing patterns in medical data
  • Regulatory Compliance: Meeting stringent HIPAA requirements and other healthcare regulations
  • Integration: Creating a solution that worked seamlessly with existing healthcare workflows and systems

Technical Implementation

AI & Machine Learning

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

System 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 Implementation

  • 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 Implementation

  • 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 Applications

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

Database & Storage

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

DevOps & Infrastructure

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

Results & Impact

93%

diagnostic assistance accuracy

67%

reduction in clinical documentation time

41%

increase in early intervention cases

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.

The healthcare provider has seen significant improvements in clinician satisfaction and patient outcomes since implementing our solution. The system continues to evolve with the continuous learning pipeline, ensuring it remains at the cutting edge of medical AI technology.

Project Information

Project Type

Healthcare AI & Infrastructure

Duration

14 months (LLM training: 3 months, Platform: 11 months)

Team Size

9 specialists (2 ML/AI, 2 backend, 2 frontend, 2 mobile, 1 DevOps)

Development Approach

Agile/Kanban with continuous delivery

Key Technologies

Fine-tuned LLMsKubernetesNext.jsNestJSTurborepoTypeScriptShadCNTailwind CSSPostgreSQLSurrealDBAWSSwiftKotlinPythonTensorFlow+4 more

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