Nursely

Inspiration

Nurses face constant cognitive overload from fragmented documentation systems and delayed access to critical patient information. We were inspired to build a tool that surfaces the most important data instantly, reducing friction between documentation and decision-making.

What it does

Nursely is an AI-powered nursing assistant that centralizes real-time patient data, risk scoring, and evidence-based clinical guidance into one desktop application. Nurses can view their entire census at a glance, identify high-risk patients, and ask clinical questions grounded in structured patient data.

How we built it

Nursely is a cross-platform desktop application built with Electron for Mac and Windows. The frontend uses React, Tailwind CSS, and Shadcn UI for a modern, responsive interface. Supabase powers authentication and backend services, while Snowflake serves as the cloud data warehouse for structured patient data and real-time SQL queries. An AI layer sits on top of this architecture to provide contextual, guideline-backed responses.

Challenges we ran into

Simulating realistic patient data while maintaining performance required careful schema design. Balancing AI-generated responses with evidence-based grounding was also a key technical challenge.

Accomplishments that we're proud of

We successfully built a working live census dashboard with real-time risk scoring, integrated an AI assistant grounded in structured data, and deployed a cross-platform desktop application. The system demonstrates how documentation can become proactive clinical intelligence.

What we learned

We learned the importance of clean data architecture in healthcare systems, how to integrate AI responsibly with structured clinical data, and how thoughtful UI design can reduce cognitive load in high-stakes environments.

What's next for Nursely

Next, we plan to integrate directly with EHR systems, expand clinical guideline datasets, implement advanced role-based access control, and validate Nursely in real clinical workflows to measure impact on efficiency and patient safety.