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Project History & Development Timeline

Overview​

The LyfeAI Provider Platform has undergone several significant pivots during its development lifecycle. This document provides a detailed history of these changes to help the incoming development team understand the context and decisions that shaped the current implementation.

Development Phases​

Phase 1: Initial Vision (Original Scope)​

Timeline: Project inception
Team Size: Full development team planned
Duration: 3-month build timeline

Original Requirements:

  • Comprehensive provider platform for healthcare professionals
  • Full-featured EHR integration
  • Real-time collaboration tools
  • Advanced AI-powered clinical decision support
  • Complete patient management system
  • Integrated billing and coding assistance

Why It Changed:

  • Funding constraints emerged that made the timeline unsustainable
  • Full team costs exceeded available budget
  • 3-month timeline deemed too aggressive for feature set

Lessons Learned:

  • Healthcare platforms require significant upfront investment
  • Feature scope must align with available resources
  • MVP approach more suitable for funding constraints

Phase 2: Patient App Retrofit​

Timeline: After funding constraints identified
Approach: Leverage existing patient app codebase
Goal: Reduce development time by reusing architecture

What Was Attempted:

  • Take existing LyfeAI patient application
  • Modify UI/UX for provider workflows
  • Adapt patient-centric features for clinical use
  • Reuse authentication and data models

Why It Failed:

  • Provider workflows fundamentally different from patient workflows
  • Data models incompatible (patient view vs. clinical view)
  • UI patterns that work for patients confuse providers
  • More time spent removing features than building new ones
  • Cost of retrofitting exceeded building from scratch

Technical Debt Created:

  • Remnants of patient-focused code remain in some utilities
  • Database schema has unused patient-app tables
  • Some component names reflect patient terminology

Phase 3: Clickable Prototype​

Timeline: Mid-development pivot
Developer: Charles Sims (solo)
Duration: 2-3 weeks
URL: https://lyfe.skafldstudio.com/dashboard

What Was Built:

  • Complete UI/UX for provider platform
  • All screens and user flows
  • Responsive design
  • Modern healthcare interface
  • Interactive but non-functional

Technology Choices:

  • Next.js for React framework
  • Tailwind CSS for styling
  • shadcn/ui for component library
  • Mock data for all displays

Reception:

  • Positive feedback from stakeholders
  • UI/UX approved by target users
  • Demonstrated product vision effectively
  • Set visual standard for final product

Limitations:

  • No backend functionality
  • No data persistence
  • No real authentication
  • Static mock data only
  • No API connections

Phase 4: Backend Development (FHIR Focus)​

Timeline: After prototype approval
Focus: Core technical infrastructure
Key Achievement: FHIR document ingestion

What Was Built:

  • AWS Textract integration for OCR
  • AWS Comprehend for medical NLP
  • FHIR R4 parser
  • Document upload pipeline
  • Basic API structure
  • Supabase database setup

Technical Stack:

  • AWS SDK for document processing
  • Medplum FHIR types for type safety
  • PostgreSQL for data storage
  • Supabase for real-time features
  • OpenAI as fallback for AWS services

DevOps Work (handled by Charles):

  • AWS Lambda functions for processing
  • S3 bucket configuration
  • IAM roles and permissions
  • Cost optimization strategies
  • Error handling and retry logic

Why It Lacked Adoption:

  • No polished UI connected
  • Required technical knowledge to use
  • Missing provider-friendly features
  • Focused on data ingestion over usability

Phase 5: Current Merged Version​

Timeline: Recent development
Goal: Combine prototype UI with backend capabilities
Status: Partially complete

Integration Points Completed:

  • Document upload connected to backend
  • Basic patient data flow
  • Authentication system (mock)
  • FHIR import functionality
  • AI-powered data extraction

Integration Points Pending:

  • Real-time features
  • Appointment scheduling logic
  • Order management backend
  • Clinical notes persistence
  • Analytics data pipelines
  • Patient portal connectivity

Current State Assessment:

  • Visually complete product
  • Core technical features proven
  • Many features remain frontend-only
  • Requires significant backend work
  • Not production-ready

Key Technical Decisions​

1. Choosing Next.js 14​

  • Why: Modern React framework with app router
  • Benefits: SSR, API routes, good DX
  • Drawbacks: Learning curve for app router

2. Supabase over Custom Backend​

  • Why: Rapid development, built-in auth
  • Benefits: Real-time, PostgreSQL, RLS
  • Drawbacks: Vendor lock-in, cost at scale

3. OpenAI Integration​

  • Why: Best-in-class medical understanding
  • Benefits: High accuracy, structured output
  • Drawbacks: Cost, privacy concerns, latency

4. shadcn/ui Components​

  • Why: Customizable, well-designed
  • Benefits: Consistent UI, accessibility
  • Drawbacks: Manual updates needed

5. AWS Services for Documents​

  • Why: Enterprise-grade OCR/NLP
  • Benefits: HIPAA compliance options
  • Drawbacks: Complex setup, cost

Stakeholder Feedback History​

Prototype Phase​

  • "Looks exactly like what we envisioned"
  • "Can we add more AI features?"
  • "When will this be functional?"

Backend Demo​

  • "Where's the UI we saw before?"
  • "This seems too technical"
  • "Can you make it look like the prototype?"

Current Version​

  • "Getting closer to what we need"
  • "Still missing key features"
  • "Need full functionality for investors"

Resource Constraints Impact​

Development Resources​

  • Single developer for most phases
  • No dedicated DevOps support
  • Limited AWS expertise
  • No UI/UX designer after prototype

Time Constraints​

  • Compressed timelines for each pivot
  • Pressure for investor demos
  • Limited time for proper testing
  • Documentation often deferred

Budget Constraints​

  • Could not maintain full team
  • Limited AWS service usage
  • Minimal third-party tools
  • No penetration testing

Conclusion​

The project's history reflects the challenges of building healthcare technology with limited resources. Each pivot represented an attempt to deliver value within constraints, but also created technical debt and partial implementations.

The current version successfully demonstrates the vision through its UI while proving core technical capabilities like FHIR ingestion. However, it requires significant investment to become a production-ready platform that fulfills the original vision of a comprehensive provider solution.

The incoming team should view this history as context for the architectural decisions and current state, not as constraints on future development. Many original goals remain valid and achievable with proper resources.