MediFlow-02

Inspiration

   Our inspiration came from identifying a real emerging problem in healthcare.
   By 2030, healthcare systems including general practisioners are expected to face overwhelming
   patient volume, resulting in overcrowded waiting rooms and long delays.
   By introducing our Medify app we tackle these problems with digital solution 
   for appointment managment.

What it does

 - Understand patients symptoms
 - Books suitable appointment with a GP
 - Can Schedule follow-up appointment with specialist if it's recommended by GP
 - Reduce waiting time and optimize appointments queue

How we built it

 - Backend service written in .NET
 - Responsive web interface built in modern frontend technologies
 - Role-based AI agents integrated through OpenAI API
 - JSON-based temporary data storage for rapid prototyping

Challenges we ran into

 - Integrating AI Agents into backend logic
 - Improving accuracy when extracting medical issue from patient text
 - Designing reliable patient data mapping using mocked datasets

Accomplishments that we're proud of

 - Successfully integrating AI Agents into a working appointment system
 - Automating extraction of clinical information from patient message
 - Building a healthcare solution that addresses future societal needs

What we learned

 - How to design and prompt AI Agents for healthcare decision support
 - Best practices for integrating AI into backend workflows
 - Structuring data and responses for smooth user experience

What's next for MediFlow-02

 - Advanced AI question checks when information is unclear
 - Integration with a digital health card system
 - Dedicated mobile app for easier access
 - Moving from mocked JSON  to secure real database
 - Adding clinician dashboard and patient history

Built With

• .net
• c#
• javascript
• react