🦷 Inspiration

My main inspiration came from seeing the pain points of how patients connect with dental care. It often feels very fragmented—patients don't necessarily know how to find the right dentist, and sometimes practices have a hard time managing their schedules as efficiently as possible. I wanted to make a streamlined, modern application of technology to bridge this gap of oral healthcare accessibility, efficiency, and user-friendliness between patients and dentists.


✨ What it does

Dental Connect is a mobile/web application solely designed to make dental care hassle-free.

  • For Patients: It grants them the ability to see their future appointments, monitor their progress of dental health through different metrics, and get quick access to their profile and related information, making the management of their oral health proactive.
  • For Dentists (Future): Though the initial focus is on the patient dashboard and interface design, the longer-term aim is to offer a system for dentists to manage the roster, view health history, and handle scheduling.

🛠️ How I built it

I built Dental Connect primarily using a modern and efficient tech stack:

  • Frontend: The application was written in full in TypeScript and React, with React Native for giving a mobile feel to the application. This allowed me to create a robust and type-safe codebase.
  • Tools: I used Vite as the build tool; it allows for faster development and hot module reloading.
  • Architecture: I used a component-based architecture, where I had created a separate folder for components, and then services and utils for data handling, and mock data in mockData.ts to keep everything separated, which is the key to scalable code.
  • Initial Concepting: In addition, I used a template created through Google AI Studio to kickstart the repository structure. This will help speed along the initial setup and allow integration of advanced AI features into the future with ease.

🛑 Challenges I ran into

The biggest challenge was translating a complex set of user needs (scheduling, health tracking, profile management) into a simple and intuitive user interface. Specifically:

  • State Management: Much planning went into how the state of appointments and health metrics would be synchronized across components in a clean, efficient manner.
  • Mocking Realistic Data: Creating the mock data in mockData.ts and users.ts realistically to mirror what would come out of a functional dental practice has taken time but was necessary to test our UI components effectively.

🏆 Accomplishments that I'm proud of

The two things for which I am particularly proud are:

  1. The Dashboard Design: Creating a clean, intuitive dashboard that successfully aggregates key information—like upcoming appointments, profile snippets, and health stats—onto one single easily digestible screen. This is realized in Dashboard.tsx, AppointmentsCard.tsx, and StatCard.tsx.
  2. Leveraging TypeScript: The decision to start with TypeScript from the beginning has made the codebase much more maintainable and reliable; this is a major win when it comes to a complex application handling sensitive health information.

🧠 What I learned

This project has considerably strengthened my skills in TypeScript development and also in component lifecycle management within the React ecosystem. I have learned from developing the architecture of a scalable frontend application: how to structure folders for easy code reuse and maintenance. It reiterates the importance of planning the data structure (types.ts) before diving into UI implementation.


⏭️ What's next for Dental Connect

The existing version is already a robust proof-of-concept for the patient side. Further development will include:

  • Backend Integration: Replace mock data with a full-fledged backend database, such as Firestore or MongoDB, to enable user registration and persistence.
  • Dentist Portal: Developing the dedicated portal for dentists, including features for managing their schedules, reviewing patient charts, and sending reminders.
  • AI Integration: Employing the initial template in AI Studio by incorporating the Gemini API to provide functionality such as symptom pre-assessment automation or smart scheduling recommendations.

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