JeevanDhara

Inspiration

The concept for JeevanDhara arose from a genuine need experienced by Thalassaemia patients: the crucial scarcity and unpredictability of blood donors. Despite having tools like e-RaktKosh, we found a lack of real-time availability, predictive outreach and personalised donor interaction. We wanted to create something that goes beyond static databases, something intelligent, humane, and usable.

What it does

JeevanDhara is an AI-powered, multilingual mobile platform that serves as an intelligent bridge between blood donors and Thalassemia patients. It goes beyond traditional systems — it doesn’t just react, it predicts.Using deep learning on historical donation patterns, demographic trends, and location analytics, JeevanDhara: Suggests optimal hotspots for mobile blood donation drives across cities. Uses AI-powered heatmaps to identify high-donation and underserved areas. Guides blood banks on where to deploy mobile vans in real-time.

How we built it

We developed JeevanDhara as a full-stack, AI-powered, multilingual mobile platform combining real-time intelligence, healthcare needs, and community-driven design. Frontend (Mobile App) Built using Flutter for seamless cross-platform (Android & iOS) support. Integrated Google Maps API to show real-time drive locations and donor density heatmaps. Designed a multilingual UI to ensure accessibility across diverse user bases in India. Implemented screens for blood request, donation scheduling, reminders, and gamified milestones. AI/ML Engine Trained using Python (TensorFlow & Keras) on synthetic donor datasets. Analyzed donation frequency, geolocation behavior, and Thalassemia demand patterns. Built a Donor Availability Prediction Model to forecast donation likelihood based on: Last donation date Blood group demand in area User engagement frequency AI-driven geo-clustering to suggest optimal mobile blood drive locations dynamically. Backend & APIs Backend built with Node.js + Express.js for scalability and RESTful API support. Used MongoDB Atlas and Firebase Firestore for secure and scalable data storage. Designed a real-time blood request/donation API, enabling quick donor-recipient matching. Integrated mock data and endpoints for: e-RaktKosh (government blood bank network) Blood Bridge (NGO campaign data) Security & Privacy Implemented JWT-based authentication for secure access. Designed a consent-based data-sharing mechanism to protect patient and donor privacy. Data encrypted at rest and in transit to ensure confidentiality and trust. Development Tools Figma – for UI/UX prototyping and wireframing Postman – for backend API testing Git & GitHub – for collaboration and version control Firebase Cloud Functions – for triggering reminders and real-time events.

Challenges we ran into

Challenges We Faced: Due to a lack of real-world blood donor datasets, we had to train our models utilising simulated data. API Limitations: The e-RaktKosh API is not clear and requires bespoke wrappers. Multilingual Interface Complexity – Making the software inclusive and accessible in different Indian languages was more challenging than imagined. Balancing privacy and usability in a mobile-first app requires additional backend design to ensure GDPR-compliant data control.

Accomplishments that we're proud of

Successfully built an AI-powered blood prediction model that identifies donor availability and drive hotspots. Designed and deployed a fully functional multilingual mobile app using Flutter with real-time blood request/donation features. Integrated a donation milestone tracker and gamified incentives to encourage recurring donations. Simulated API integration with e-RaktKosh and Blood Warriors' Blood Bridge, laying groundwork for real-world deployment. Created a consent-first data sharing model to balance healthcare access with privacy.

What we learned

How to apply AI and geospatial data to solve real-world healthcare problems. The importance of designing inclusively for multilingual, diverse communities. How to implement secure, privacy-respecting health data handling in mobile apps. That technology alone isn't enough — building trust through transparency and user empowerment is key in healthcare. Rapid prototyping, iteration, and team collaboration under tight deadlines.

What's next for JeevanDhara

Live integration with e-RaktKosh and onboarding real NGOs & hospitals. Dynamic drive routing with mobile van GPS tracking and AI-suggested stops. Add Aadhaar-based donor verification (with user consent) for secure registration. Introduce in-app doctor consultation and Thalassemia caregiver chatrooms. Launch a pilot program in tier-1 cities in collaboration with local health departments. Expand AI capabilities to forecast future shortages and optimize donation campaigns.

Built With

• blood-bridge-tools:-figma
• e-raktkosh-(simulated)
• express.js
• github-apis-&-services:-google-maps-api
• javascript-(node.js)
• jwt
• keras-databases:-firebase-firestore
• languages:-dart-(flutter)
• mongodb-atlas-cloud-&-platforms:-firebase
• postman
• python-(ai/ml)-frameworks:-flutter
• tensorflow