🏥 Patient Health Management System

FastAPI • PostgreSQL • Chatbot • MCP • CrewAI

A modern backend system designed to help clinics manage patients, appointments, and health data — with a future-ready architecture for AI agents and automation.

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📌 Project Overview

The Patient Health Management System is a real-world healthcare backend that enables clinics to:

• Manage patient records
• Store and retrieve medical history
• Automatically calculate BMI
• Handle doctor schedules and appointment slots
• Book appointments seamlessly
• Provide a chatbot interface for patient queries
• Prepare for AI automation using CrewAI + MCP

This project is built with scalability and AI integration in mind.

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🏗️ System Architecture

Frontend (HTML/JS)
        ↓
FastAPI Backend
        ↓
PostgreSQL Database
        ↓
Business Logic (Slots, Booking, BMI)
        ↓
Chatbot Layer
        ↓
MCP Server (Tools)
        ↓
CrewAI Agents (Automation & AI)

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✨ Features

👩‍⚕️ Patient Management

• Add new patients
• Update patient details
• Delete patients
• View all patients
• View medical history

📊 Health Analytics

• Automatic BMI calculation
• Health verdict:
  • Underweight
  • Normal
  • Overweight
  • Obese

📅 Appointment System

• Provider schedules
• Slot generation
• Slot availability check
• Appointment booking
• Overlap prevention

💬 Chatbot

Patients can ask about:

• BMI
• Allergies
• Last visit
• Available slots
• Booking instructions

🤖 Agentic AI (Design Ready)

• MCP server exposes backend tools
• CrewAI agents automate workflows
• LLM-ready for natural language operations

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🛠️ Tech Stack

Layer	Technology
Backend	FastAPI
Database	PostgreSQL
Frontend	HTML, CSS, JavaScript
AI Framework	CrewAI
Tool Protocol	MCP
ORM / Driver	psycopg2
Server	Uvicorn

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📂 Project Structure

FastApi Project/
│
├── main.py            # FastAPI application
├── db.py              # Database connection
├── migrate.py         # Patient table migration
├── migrate_slots.py   # Slot tables migration
├── mcp_server.py      # MCP tool server
├── crew_runner.py     # CrewAI agent runner
├── index.html         # Frontend UI
├── requirements.txt
└── myenv/             # Virtual environment

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🚀 How It Works (Workflow)

1. User opens index.html
2. UI sends requests to FastAPI
3. FastAPI:
   • Reads/writes to PostgreSQL
   • Handles slot logic & booking
4. Chatbot endpoint:
   • Understands intent
   • Fetches patient data
5. MCP server:
   • Exposes booking & info tools
6. CrewAI:
   • Uses MCP tools
   • Automates scheduling tasks

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▶️ Running the Project

1️⃣ Activate Virtual Environment

.\myenv\Scripts\activate

2️⃣ Start PostgreSQL

Make sure PostgreSQL is running on port 5432.

3️⃣ Run Migrations

python migrate.py
python migrate_slots.py

4️⃣ Start FastAPI Server

uvicorn main:app --reload

Open:

• http://127.0.0.1:8000
• http://127.0.0.1:8000/docs

5️⃣ (Optional) Run MCP Server

python mcp_server.py

6️⃣ (Optional) Run CrewAI Agent

python crew_runner.py

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🧪 Example Queries

Show available slots for D001 on 2025-12-30
Book D001 for patient P001 on 2025-12-30 10:00 to 10:30
What allergies does patient P001 have?

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🔧 Troubleshooting

❌ Server Not Starting

.\myenv\Scripts\activate
pip install -r requirements.txt

❌ Database Connection Error

Check:

• PostgreSQL is running
• DATABASE_URL is correct
• Port 5432 is free

❌ Booking Fails

Make sure:

• Slot time is valid
• No overlapping appointment exists

❌ CrewAI / MCP Errors

If you see:

OPENAI_API_KEY is required

or

insufficient_quota

That means:

• API key is missing or
• Quota is exhausted

✅ Solution:

• Add API credits
• Set OPENAI_API_KEY in environment variables

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🆘 Support

• 📧 Email: sidchavan6557@gmail.com
• 💼 LinkedIn: https://www.linkedin.com/in/siddhesh-chavan-36718622b/
• 🐞 Issues: Open an issue in this repository

You can ask about:

• FastAPI
• PostgreSQL
• Booking logic
• Chatbot flow
• MCP / CrewAI

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🔮 Future Enhancements

🤖 AI & Automation

• Full CrewAI agent for appointment scheduling
• MCP tools for billing & reports
• Smarter LLM chatbot

🔐 Security

• Login system
• Role-based access (Admin / Doctor / Patient)

📊 UI & UX

• Doctor dashboard
• Appointment calendar
• Patient self-service portal

🗄️ Backend

• Alembic migrations
• Redis caching
• Email/SMS reminders

☁️ Deployment

• Docker support
• Cloud hosting (AWS / Azure / GCP / Render)

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👨‍💻 Author

Siddhesh Chavan
Data Engineer | Backend Developer | AI Enthusiast

This project demonstrates real-world backend engineering combined with modern agentic AI architecture using FastAPI, PostgreSQL, MCP, and CrewAI.