Atria

Inspiration

Hospitals around the world struggle with overcrowding, especially during peak hours or mass casualty incidents (MCI). When patient volume spikes, inefficient use of space can lead to congestion, which studies have shown can delay treatment, increase wait times, place dangerous strains on medical staff, and even result in death. These delays are not just operational issues. They can directly impact patient outcomes, especially for those with time-sensitive or critical conditions. We were inspired by the idea that even without increasing staffing levels or building new infrastructure, better insight into how hospital space is actually used could help reduce bottlenecks, improve patient flow, and ultimately support safer, more timely care.

What it does

Atria works by simulating hospital movement and visualizing congestion through an interactive, web-based dashboard. It allows the user to first build their own hospital layout, where they can add walls, assign rooms for different purposes, and assign nurses and doctors to each room. The user is then able to run simulations based on their chosen layout. The simulation models real hospital interactions, such as patients waiting in the waiting room and nurses periodically escorting patients to treatment areas. As the simulation runs, the system generates real-time heatmaps showing where patients, nurses, and doctors accumulate over time. We then identify hotspots, or areas where there was too much congestion. These detected hotspots are then passed into an AI system that analyzes the hospital layout and congestion patterns to generate actionable recommendations, such as rerouting patient flow, redistributing staff, or reconsidering room placement. Users are then able to reconfigure the layout as needed, as well as save it onto their computer for future use. This allows users to directly explore potential solutions through a fully functioning website interface. Users are able to log into the website to view simulation results, explore detected hotspots, and interact with the generated recommendations through a clear and intuitive interface.

How we built it

We built a grid-based hospital simulation that models patient arrivals, staff movement, waiting areas, and treatment rooms. As the simulation runs, we track where patients, nurses, and doctors spend time and aggregate this data into a 2D congestion heatmap. Gemini then produced actionable suggestions for addressing congestion issues. All graphs and visualizations were generated using Matplotlib. The website was built with React, Vite, Typescript, and Supabase.

Challenges we ran into

One significant challenge was access to real hospital video data. Due to privacy regulations such as HIPAA, obtaining footage of patient movement is extremely difficult, which is why we decided to create our own simulation. Even if we had access to real video, a single camera cannot capture an entire hospital floor, meaning multiple camera feeds would need to be combined to form a complete view of movement and congestion. Our own simulation to model hospital activity in a controlled and privacy-safe way. This allowed us to experiment with different layouts, traffic patterns, and staffing scenarios while still producing realistic congestion data for analysis.

Accomplishments that we're proud of

We are proud of building a fully functioning hospital simulation that realistically models patient flow, staff movement, and space usage. We are also proud of learning and successfully integrating tools that were new to us, including Supabase for user authentication and storage. We found these tools to be incredibly useful in streamlining our application, simplifying elements that were difficult to work with before. Applying these tools helped us better understand how to quickly learn, adapt, and iterate with unfamiliar technologies.

In addition, we are proud of the user interface and overall usability of the website. We spent significant time refining the frontend and design to ensure that congestion patterns, detected hotspots, and suggested improvements were easy to understand and navigate. We even communicated with real healthcare professionals to do UX testing with our application, with positive feedback given all across our project.

What we learned

Throughout the project, we learned how to build on our existing knowledge while adapting to new technologies. Integrating unfamiliar frameworks like Supabase pushed us to carefully read, understand, and effectively use API documentation rather than relying on trial and error.

We also learned the importance of having a well-thought-out plan before starting development. Taking time early on to think through data flow, system boundaries, and end goals made the implementation process smoother and helped us avoid major redesigns later in the project.

What's next for Atria

Next, we plan to focus on securing data handling to meet HIPAA compliance requirements so the system can be used safely in real clinical environments. We also want to reach out to hospitals and clinics to understand their operational needs better and explore potential pilot use cases.

From a technical perspective, we aim to move beyond purely simulated inputs by supporting real camera feeds. This includes adapting the system to work with standard webcams and combining multiple viewpoints to better capture movement across larger spaces.

Made by Pranay M, Zinnun M, Tarun T, and Ethan Z