Inspiration
Inspired by the pressing need for proactive health management in diabetes and cardiovascular care, WellSync aims to empower patients and providers with timely, AI-driven insights.
What it does
WellSync monitors patient vitals by aggregating data from wearables, lab reports, and historical records. It detects early warning signs and alerts both patients and doctors, facilitating timely interventions and personalized care.
How we built it
We built WellSync using Next.js with the App Router and Context API for global state management. The prototype leverages JSON mock data, Tailwind CSS for responsive design, and data visualization libraries to present health metrics clearly.
Challenges we ran into
Integrating diverse data sources like wearable data, lab reports, historical records Simulating real-time data in a demo environment Ensuring intuitive UI for varied user roles like patient, doctor, admin, receptionist Maintaining data accuracy and timely alert delivery
Accomplishments that we're proud of
Developing a fully functional multi-role dashboard prototype Seamless integration of AI-generated insights with user-friendly interfaces Effective collaboration and development despite complex healthcare data requirements
What we learned
We deepened our understanding of integrating heterogeneous data, managing global state in Next.js, AI integration and designing user-centric interfaces under strict demo constraints. This project also enhanced our skills in responsive design and quick problem-solving.
What's next for WellSync
We're planning to integrate live API data, refine our AI predictive algorithms, and further enhance the user experience. Future developments include expanded functionalities and scalability for full production deployment.
Built With
- amazon-web-services
- cnn
- keras
- lstm
- next.js
- postgresql
- python
- pytorch
- tailwindcss
- tensorflow
- typescript
- xgboost

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