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LyfeAI Provider Platform - Handoff Documentation

Date: December 2024
Prepared by: Charles Sims
For: Revyrie Dev Team

Table of Contents​

  1. Executive Summary
  2. Project History
  3. Current State
  4. Architecture Overview
  5. Repository Access
  6. Key Features
  7. Known Limitations
  8. Deployment Guide
  9. Next Steps Recommendations
  10. Technical Debt

Executive Summary​

The LyfeAI Provider Platform is a medical AI system designed for healthcare professionals. The current version represents a merger of a polished frontend prototype with backend FHIR ingestion capabilities. While the UI is visually complete, many features require backend implementation to become fully functional.

Quick Facts​

  • Tech Stack: Next.js 14, TypeScript, Supabase, OpenAI API
  • Current State: Frontend-complete with partial backend functionality
  • Key Achievement: Automatic FHIR document ingestion using AWS Textract/Comprehend
  • Primary Gap: Full backend implementation for all UI features

📊 For a detailed assessment, see Current State Summary which provides completion percentages, timelines, and honest evaluation of what works and what doesn't.

Project History​

Timeline of Development Pivots​

Phase 1: Original Scope (3-month plan)​

  • Full development team planned
  • Comprehensive provider platform
  • Outcome: Funding constraints made this unsustainable

Phase 2: Patient App Retrofit Attempt​

  • Tried reusing existing patient app architecture
  • Aimed to modify for provider-specific functionality
  • Outcome: Proved inefficient and costly

Phase 3: Clickable Prototype​

Phase 4: Backend Development​

  • Focus on FHIR ingestion capabilities
  • AWS Textract and Comprehend integration
  • Outcome: Technically successful but lacked UI polish

Phase 5: Current Merged Version​

  • Combined prototype UI with ingestion backend
  • Partial feature implementation
  • Outcome: Demo version requiring completion

Current State​

What Works​

  1. Authentication System (Mock)

    • Role-based access control
    • Multiple user types (Admin, Doctor, Nurse, Staff)
    • Session management

  2. FHIR Document Processing

    • PDF upload and parsing
    • AWS Textract integration (when configured)
    • OpenAI fallback for data extraction
    • Structured data extraction

  3. Patient Management

    • Basic CRUD operations
    • Patient list view
    • Individual patient details
    • Data import from FHIR

  4. UI/UX

    • Complete responsive design
    • All screens and components built
    • Modern healthcare interface
    • Accessibility considerations

What Needs Implementation​

  1. Real-time features (chat, notifications)
  2. Appointment scheduling backend
  3. Order management system
  4. Clinical notes persistence
  5. Care plan execution
  6. Analytics and reporting
  7. Patient portal integration
  8. Video consultation infrastructure

Architecture Overview​

Frontend Architecture​

app/                    # Next.js 14 App Router
├── (auth)/            # Authentication pages
├── admin/             # Admin dashboard
├── patients/          # Patient management
├── dashboard/         # Main dashboard
├── care-plans/        # Care plan management
├── orders-results/    # Orders and lab results
├── communication/     # Messaging features
├── scheduling/        # Appointment scheduling
└── patient-portal/    # Patient portal features

components/            # Reusable React components
├── ui/               # Base UI components (shadcn/ui)
├── admin/            # Admin-specific components
└── [feature]/        # Feature-specific components

lib/                  # Core utilities and services
├── ai-service.ts     # AI integration (OpenAI)
├── enhanced-fhir-service.ts  # FHIR parsing
├── supabase-admin.ts # Database client
└── use-auth.ts       # Authentication hooks

Backend Services​

Database (Supabase/PostgreSQL)​

  • Tables: users, patients, appointments, notes, medications, etc.
  • Row-level security policies implemented
  • Real-time subscriptions available

AI Services​

  • OpenAI GPT-4 integration for document processing
  • Structured output for medical data extraction
  • Fallback mechanisms for offline operation

FHIR Integration​

  • Comprehensive FHIR R4 support
  • Patient resource parsing
  • Bundle processing
  • Extensible for additional resources

Repository Access​

GitHub Repository​

Required Environment Variables​

# Supabase
NEXT_PUBLIC_SUPABASE_URL=your_supabase_url
NEXT_PUBLIC_SUPABASE_ANON_KEY=your_supabase_anon_key
SUPABASE_SERVICE_ROLE_KEY=your_service_role_key

# OpenAI
OPENAI_API_KEY=your_openai_api_key

# AWS (Optional - for Textract/Comprehend)
AWS_ACCESS_KEY_ID=your_access_key
AWS_SECRET_ACCESS_KEY=your_secret_key
AWS_REGION=us-east-1

Deployment Platforms​

  • Main App: Vercel Project ID: prj_9yE8v6ltrAlegwkYRD75VDXmFwsy
  • Documentation: Vercel Project ID: prj_l9adZiu4VKh9gLNBUSH368ZUADEE

Key Features​

1. Document Ingestion Pipeline​

// Flow: Upload → Parse → Extract → Structure → Store
1. User uploads PDF/Image
2. System attempts AWS Textract (if configured)
3. Falls back to OpenAI vision API
4. Extracts structured medical data
5. Stores in PostgreSQL via Supabase

2. Role-Based Access Control​

  • Different UI elements per role
  • Permission-based API access
  • Secure data filtering

3. AI-Powered Features​

  • Clinical decision support (UI only)
  • Document summarization
  • Smart scheduling (UI only)
  • Predictive analytics (UI only)

Known Limitations​

Technical Limitations​

  1. Mock Authentication: Currently using hardcoded users
  2. Limited Backend: Many UI features lack backend implementation
  3. No Real-time Updates: WebSocket connections not implemented
  4. Missing Integrations: EHR systems, labs, pharmacies not connected
  5. No Production Security: Needs proper auth, encryption, audit logging

Functional Limitations​

  1. Appointments: UI complete but no scheduling logic
  2. Messaging: Interface ready but no message delivery
  3. Orders: Can't actually submit lab/medication orders
  4. Analytics: Charts display mock data only
  5. Patient Portal: No actual patient access system

Deployment Limitations​

  1. Environment Setup: Complex AWS configuration required
  2. Database Migrations: Manual SQL script execution needed
  3. Cost Considerations: OpenAI API usage can be expensive
  4. Scaling Issues: Not optimized for high load

Deployment Guide​

Local Development​

# Clone repository
git clone https://github.com/SkaFld-Ignite/lyfe-provider-ui
cd lyfe-provider-ui

# Install dependencies
npm install

# Set up environment variables
cp .env.example .env.local
# Edit .env.local with your values

# Run database migrations
# Execute scripts in order from scripts/ directory

# Start development server
npm run dev

Production Deployment (Vercel)​

  1. Connect GitHub Repository

    • Link to Vercel project
    • Set production branch to main

  2. Configure Environment Variables

    • Add all required env vars in Vercel dashboard
    • Mark sensitive keys as secret

  3. Deploy

    git push origin main
    # Or trigger manually in Vercel dashboard

Database Setup​

-- Run scripts in this order:
1. scripts/01_create_tables.sql
2. scripts/02_create_views.sql
3. scripts/03_create_policies.sql
4. scripts/04_insert_sample_data.sql

Next Steps Recommendations​

Immediate Priorities (Week 1-2)​

  1. Complete Authentication System

    • Replace mock auth with Supabase Auth
    • Implement proper session management
    • Add password reset flow

  2. Stabilize Core Features

    • Patient data persistence
    • Document upload reliability
    • Basic appointment creation

Short-term Goals (Month 1)​

  1. Real-time Features

    • Implement WebSocket connections
    • Live chat functionality
    • Push notifications

  2. Order Management

    • Lab order submission
    • Prescription management
    • Results tracking

  3. Care Plans

    • Template system
    • Task assignment
    • Progress tracking

Medium-term Goals (Month 2-3)​

  1. Integrations

    • Epic MyChart API
    • Lab interfaces (HL7)
    • Pharmacy connections

  2. Analytics

    • Real data pipelines
    • Performance metrics
    • Clinical outcomes tracking

  3. Patient Portal

    • Separate patient app
    • Secure messaging
    • Appointment booking

Long-term Considerations​

  1. Compliance

    • HIPAA audit
    • Security assessment
    • Penetration testing

  2. Scalability

    • Performance optimization
    • Caching strategy
    • CDN implementation

  3. AI Enhancement

    • Fine-tuned models
    • Local inference options
    • Advanced clinical decision support

Technical Debt​

High Priority​

  1. Type Safety: Many any types need proper interfaces
  2. Error Handling: Inconsistent error handling patterns
  3. Testing: No test coverage currently
  4. Documentation: Inline code documentation minimal

Medium Priority​

  1. Component Architecture: Some components too large
  2. State Management: Consider Redux/Zustand for complex state
  3. API Structure: Formalize API patterns
  4. Performance: Implement lazy loading and code splitting

Low Priority​

  1. Styling Consistency: Some inline styles remain
  2. Accessibility: Full WCAG compliance needed
  3. Internationalization: Prepare for multi-language support
  4. PWA Features: Offline capability

Support & Questions​

Documentation​

  • Main Docs: /docs directory
  • API Reference: See /docs/api
  • Architecture: See /docs/architecture

Key Files to Review​

  1. CLAUDE.md - AI assistant instructions
  2. ROADMAP.md - Original project roadmap
  3. DATABASE_MIGRATION_GUIDE.md - Database details
  4. /docs/deployment/production-checklist.md - Deployment guide

Contact​

  • Charles Sims: [email protected]
  • Repository Issues: Use GitHub issues for technical questions

Final Notes​

This project represents significant effort in creating a polished healthcare provider interface. While the frontend is largely complete, the backend implementation is the critical next phase. The architecture is sound and extensible, but requires dedicated development resources to reach production readiness.

The FHIR ingestion system is a particular strength and differentiator. With proper investment in completing the backend services and ensuring compliance, this platform could serve as a powerful tool for healthcare providers.

Good luck with the next phase of development. The foundation is solid; it now needs the full implementation to match the vision demonstrated in the UI.