The problem
Government hospitals were dealing with serious queue problems. PranSaathi was built to manage those queues intelligently rather than leave them to the physical reality of a corridor and a clipboard.
What was built
An entire application, taken to the point where it could be shown to the people who would have to approve it.
- ABDM integration. Implemented the Ayushman Bharat Digital Mission API so the system could work with national digital health identity rather than around it.
- A custom queueing algorithm. Written from scratch to handle complex patient queues gracefully, rather than treating a hospital queue as a simple line.
- Outbreak prediction. An AI model that predicts virus and flu outbreaks four weeks in advance at district level.
Stack
| Layer | Technology |
|---|---|
| Application | Next.js, TypeScript, CSS |
| Models & services | Python, AI model fine-tuning |
| Data | PLpgSQL |
| Integration | ABDM API |
What happened
I went out on my own and met the ABDM Joint Director of Jharkhand to present it.
It was rejected due to unclear compliance — but it was a great learning, and that part of the exercise turned out to be worth more than the code.
Access
The deployment at pransaathi.vercel.app is access restricted.
If you need access, mail mukherjeeabhishek207@gmail.com.