Beacon

Inspiration

While researching emergency medical infrastructure, we came across an alarming study from UChicago Medicine. It showed that every single hospital pharmacy manager they surveyed had faced supply shortages in the past year, and 81% confessed to hoarding medications just to get by. Even worse, over a third of them actually had to ration life-saving treatments. It became obvious to us that during a major regional crisis, a local ER could easily exhaust their critical gear while another facility just a few miles away has extra stock sitting in a storage room. The main issue we wanted to tackle is that lack of real-time visibility between disconnected healthcare networks.

What it does

Beacon acts as the automation layer for hospital supply chains, replacing the need for staff to manually call nearby facilities during a crisis. It is designed to automatically process a nurse’s supply update, check if a hospital has fallen below a safe threshold, locate the nearest facility with sufficient stock, and instantly generate a transfer request for dispatchers. The system is highly flexible, built to seamlessly handle both routine inventory changes and urgent requests for rare, out-of-network items.

How we built it

We developed the backend using Node.js and Express to handle API routing, combined with Firebase Firestore as our real-time database to seamlessly sync live data between the nurse inputs and the dispatcher dashboard. The backend relies on custom algorithms to handle inventory status, calculate available stock, filter viable donors, and perform closest-donor matching using coordinate distances. For the frontend, we utilized React and Firebase, bootstrapping our initial UI with a Google Stitch template to accelerate development. We integrated the Mapbox API to visually map out hospital markers, trigger live alerts for critical shortages, and draw supply routes between the donor and receiving hospitals.

Challenges we ran into

Our primary backend challenge was designing the system to handle both routine stock updates and totally new emergency requests without breaking. Because a requested emergency item (like a rare antidote) might not exist in a hospital's standard inventory, we had to split our logic so the system could create direct transfer requests that flag as "unmatched" if no donor is found, rather than silently failing. We also had to rigorously test our matching algorithm to ensure it didn't accidentally cause a new shortage at the donating hospital. On the frontend, our main hurdles involved wrangling the Mapbox integration to perfectly reflect real-time database state changes without lag, alongside heavily debugging and optimizing the dispatcher dashboard to ensure the UI remained frictionless and user-friendly for healthcare workers operating under immense pressure.

Accomplishments that we're proud of

We are incredibly proud of successfully building a fully automated, end-to-end data pipeline in under 48 hours. Bridging the gap between a nurse simply speaking into a microphone and a dispatcher instantly seeing a live route drawn on a map felt like a massive technical victory. We are also proud of the robust fail-safes we engineered, like the system’s ability to handle unmatched emergency requests gracefully. This ensures the platform remains reliable and informative even when a perfect donor isn't immediately available.

What we learned

This project taught us that in high-stress healthcare environments, the best software is the software that requires the least interaction. Building the voice UI reinforced how critical frictionless, zero-click design is for frontline workers during a crisis. On the backend, we learned a tremendous amount about managing complex asynchronous data streams and how to write smart filtering algorithms that balance geographic distance with strict inventory safety thresholds on the fly.

What's next for Beacon

Our immediate next step is integrating predictive AI modeling into the analytics dashboard. Instead of just reacting to shortages, Beacon could analyze incoming ambulance telemetry and regional historical data to automatically dispatch supplies before a hospital actually runs out.