Under the Hood
Technology
MediFlow AI is built on a modern, secure, scalable stack — real components chosen for reliability, not a marketing slide. Here is exactly what runs underneath the platform.
Backend
- FastAPI (Python) — the REST API that powers every workflow
- SQLAlchemy ORM for typed, maintainable data access
- PostgreSQL as the system of record, Supabase-hosted
- Interactive Swagger/OpenAPI docs generated automatically at /docs
Authentication & Access Control
- JWT-based authentication (PyJWT) with token expiry
- Password hashing with bcrypt — passwords are never stored in plain text
- Role-based access control across admin, doctor, pharmacist, and patient roles
- Every protected endpoint requires a valid Bearer token
AI & Clinical Intelligence
- Groq-hosted large language model (llama-3.3-70b-versatile)
- Used for AI-assisted clinical assessment and patient counselling generation
- Structured JSON responses with explicit confidence scores
- Every AI output is labeled as decision support, not a diagnosis, and requires clinician review
Frontend
- React 19 for the UI layer
- TanStack Start & TanStack Router for file-based routing and SSR
- TanStack Query for data fetching and caching
- Tailwind CSS v4 for styling
Data & Documents
- Relational schema covering patients, vitals, symptoms, assessments, prescriptions, and audit logs
- Server-generated prescription PDFs via ReportLab
- Structured audit log table capturing who, what, when, before-state, and after-state
Security by Design
- Encrypted connections to the PostgreSQL database
- Least-privilege access enforced at the API layer per role
- Full audit trail for every create, update, and review action
- Secrets and API keys managed via environment configuration, never hard-coded
Curious about the exact API surface? See the API Reference for every endpoint, or run the backend locally and browse the interactive Swagger docs at /docs.