Building My First FHIR based Patient Management Web App with AI

I recently built my first healthcare web application - a Patient Management system that connects to medical databases. It was an exciting journey filled with learning, challenges, and some help from AI. Let me share what I created and the story behind it.

What I Built

I created a complete web application that helps manage patient information. Think of it as a digital patient registry that can connect to different medical databases (called FHIR servers) and let you:

• View all patients in a clean, organized table
• Add new patients with their details
• Edit existing patient information
• Search for specific patients by name, phone, or ID
• Sort patients by when they were last updated

The main dashboard showing the patient list with search and management features

The Tech Stack I Chose

I decided to keep things simple but powerful:

Backend: FastAPI (Python)

• FastAPI is modern, fast, and automatically generates API documentation
• Perfect for building APIs that other applications can use
• Great for beginners because of its clear error messages

Frontend: Vanilla JavaScript, HTML, and CSS

• No complex frameworks - just clean, simple code
• Works on any device and loads quickly
• Easy to understand and modify

Database: No local database!

• Instead, the app connects directly to medical databases (FHIR servers)
• This keeps the app lightweight and always up-to-date
• No need to worry about data synchronization

Key Features I’m Proud Of

Smart Patient Management

The app can handle all the basic patient operations you’d expect:

• Patient List: Shows all patients with their key information
• Add New Patients: Simple form to add patients with validation
• Edit Patients: Update any patient’s information
• Search: Find patients by name, phone number, or ID
• Sort: Organize patients by when they were last updated

The clean, user-friendly form for adding new patients

Multi-Server Support

One of my favorite features is the ability to work with different medical databases:

• Switch Servers: Change between different medical databases on the fly
• Save URLs: Remember previously used database URLs
• Visual Feedback: Clear indicators when switching between databases

The interface for selecting and managing different medical databases

Smart Validation

The app includes several validation features to ensure data quality:

• Future Date Prevention: Can’t set birth dates in the future
• Phone Number Validation: Ensures phone numbers are properly formatted
• Required Fields: Makes sure essential information is provided
• Real-time Feedback: Shows errors immediately as you type

The Challenges I Faced

Learning FHIR Standards

The biggest challenge was understanding FHIR (Fast Healthcare Interoperability Resources) - the standard used in healthcare for sharing patient data. It’s quite complex, but I learned that:

• FHIR has very specific rules about how patient data should be structured
• Each piece of information has a specific format and location
• Validation is crucial to ensure the data meets medical standards

Building Without a Local Database

Not using a traditional database was both exciting and challenging:

• Pros: No data synchronization issues, always current information
• Cons: Had to handle network errors gracefully, ensure proper error messages
• Solution: Built robust error handling and user-friendly messages

Making It User-Friendly

Creating a good user experience is not that important but being an asthetic person i choose that as my main objective:

• Loading States: Added visual feedback during operations
• Error Messages: Clear, helpful error messages instead of technical jargon

How AI Helped Me Build This

I used AI assistance in Cursor throughout the development process, and it was incredibly helpful:

Code Generation: When I needed to create API endpoints or validation logic, the AI helped me write the boilerplate code quickly.

Understanding Standards: FHIR documentation can be overwhelming. The AI helped me understand the key concepts and requirements.

Debugging: When things didn’t work as expected, the AI helped me identify the issues and suggest solutions.

Architecture Decisions: The AI suggested good patterns for handling async operations and error management.

The User Experience

I focused on making the app simple and intuitive:

Clean Interface: The main page shows a clear list of patients with all the important information visible at a glance.

Easy Navigation: Simple buttons and forms that guide users through each action.

Helpful Feedback: Success messages when operations complete, clear error messages when something goes wrong.

Fast Performance: The app responds quickly to user actions, even when dealing with large amounts of patient data.

Clear feedback when operations complete successfully

What I Learned

Healthcare Data is Serious Business

Working with patient data taught me the importance of:

• Data Validation: Every piece of information must be accurate and properly formatted
• Security: Patient information is sensitive and must be handled carefully
• Standards: Following established standards ensures compatibility with other systems

The Power of AI-Assisted Development

Using AI tools like Cursor significantly accelerated my development:

• Faster Learning: AI helped me understand complex concepts quickly
• Better Code: Suggestions for cleaner, more maintainable code
• Problem Solving: When stuck, AI provided alternative approaches

The Result

The final application is a clean, functional patient management system that:

• Connects to real medical databases
• Handles patient data according to healthcare standards
• Provides a smooth user experience
• Can be easily extended with new features

An overview of the complete application interface