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🤓 Latest Submissions
Bridge-PA
Prior authorization in healthcare is notoriously slow, heavily manual, and prone to administrative errors, leading to severely delayed patient care. Bridge-PA is an intelligent, multi-agent enterprise orchestration system designed to completely automate and streamline this entire workflow. Powered by Google Gemini AI and LangGraph, our platform orchestrates specialized AI agents to handle document intake, clinical data extraction, policy review, and final decision-making. Deployed on robust, high-performance Vultr Cloud server instances with a secure PostgreSQL backend (Supabase), Bridge-PA ensures scalable, real-time, and reliable processing. By replacing outdated manual reviews with collaborative AI agents, our solution significantly reduces turnaround times from days to just a few minutes. It minimizes administrative burnout for healthcare professionals and accelerates patient access to critical medical treatments.
19 May 2026
Bridge-PA: Autonomous Prior Authorization System
The Problem: The healthcare Prior Authorization (Concurrent Review) process is notorious for being manual, slow, and prone to administrative bottlenecks, leading to delayed patient care and high costs for payer organizations. Our Solution - Bridge-PA: We built a multi-agent orchestration pipeline designed to securely automate this workflow while strictly adhering to HIPAA regulations. How it Works (The Prototype Architecture): Powered by LangGraph and LangChain, our deterministic state machine orchestrates four specialized AI agents. (Note: For this hackathon prototype, we are using simulated medical data and mock endpoints to demonstrate the workflow): 1.Supervisor Agent: Controls the fixed sequence and enforces system safety. 2.Document Processing Agent: Simulates the extraction of structured clinical data. 3.Criteria Evaluation Agent: Evaluates data against mocked healthcare policies for whitelist determination. 4.Data Entry Agent: Demonstrates how approved cases would be autonomously submitted. The system features smart routing: "Mode A" for end-to-end automation of clean cases, and "Mode B" for human-in-the-loop escalation where a Full Recommendation Package is sent to a UM Specialist. Note to Judges: Our complete LangGraph architecture works flawlessly in our local testing environment (as showcased in our demo video). During the final cloud deployment on Railway, we encountered a strict pip dependency conflict in the cloud environment. While the frontend UI is active, the cloud backend may experience startup limits. We highly encourage you to review our Demo Video, Pitch Deck, and GitHub repository to see the robust implementation of our orchestration pipeline!
19 May 2026
