Inspiration

MediTrack was inspired by the struggles of managing chronic conditions like hypertension, where fragmented tools lead to missed medications and overlooked health patterns. We aimed to create a unified web app using AI and modern tech to make health tracking proactive and stress-free, drawing from personal experiences with family health challenges.

What it does

MediTrack tracks blood pressure with logs, timestamps, and interactive trend charts, while managing medications through flexible schedules, browser notifications, and adherence logging (taken, missed, or skipped). It provides AI-driven insights like health summaries and personalized recommendations, all secured with Firebase authentication for encrypted, privacy-focused data handling.

How we built it

We used Next.js and React for the frontend, TypeScript for type safety, Tailwind CSS for styling, and Recharts for charts. Firebase handles backend authentication, Firestore storage, and AI logic, with service workers enabling persistent notifications. The app was developed iteratively, tested with Jest, and open-sourced on GitHub for easy Vercel deployment.

Challenges we ran into

Implementing reliable notifications across browsers required debugging service workers and permissions. Ensuring HIPAA-compliant privacy in Firebase and simplifying AI for accurate insights without complexity were tough under time constraints, as were real-time data syncing bugs.

Accomplishments that we're proud of

We built a polished, AI-enhanced health app in hackathon time, with seamless notifications and secure features that improve real medication adherence. Its modular design, thorough testing, and open-source availability make it community-ready and impactful.

What we learned

We advanced skills in Next.js, TypeScript, and Firebase for scalable apps, plus service workers and accessible design. Emphasizing user privacy and iterative testing highlighted the balance of innovation with empathy in health tech.

What's next for MediTrack

We'll add mobile responsiveness, wearable integrations, and advanced ML for predictive analytics. Community contributions via GitHub will drive features like family sharing and internationalization, aiming for a full production release based on user feedback.

Built With

• and-backend-services.-styling-is-handled-by-tailwind-css-(3.4.1)
• and-typescript-(5.0)-for-type-safety.-it-uses-firebase-(11.9.1)-for-authentication
• database
• database.com
• firebase
• nextjs
• radix
• react
• react-(18.3.1)-for-ui-components
• recharts
• tailwind
• typescript