HospiShuttle

Where do patients go for treatment? Us. We know where treatments are lacking and provide transportation to the hospital they'll need to reduce the burden and bridge the gap.

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Inspiration

This project, we knew that our team of two newly-minted data analysts might struggle in the realm of learning, applying, and adapting to big-tech. We decided to hone-in on a human-level issue and hoped to tackle that scope.

What it does

Our website takes surveyed health data for districts in India and identifies where treatments may be lacking and needed. We propose routes for shuttles to make the care feel closer to the patient. Our routes are optimized and targeted for regions with specific needs and the hospitals that have the capacity to treat those needs.

How we built it

We started with a development system that we were familiar with, Python notebooks and SQL queries. Through the hackathon, we experimented with using agents to build the project and were very impressed. our reasoning and logic got operationalized into an app in a way that we wouldn't have been able to achieve in a month, let alone 48 hours.

Challenges we ran into

We were unfamiliar with the tech stack for this project - connecting to Lakebase, building the front-end, and even making our logic accessible to non-technical users were all major challenges that AI helped us overcome.

Github Repo: https://github.com/ch-lum/dais-healthcare-access

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