MediClear — AI Agents That Fight Denials

Doctors spend nearly 28 hours a week fighting insurance companies over paperwork instead of treating patients, and Stanford's own research earlier this year found that even the best AI system today completes only about 36% of these administrative tasks. Insurance claim denials are a massive, largely unsolved problem in healthcare — one estimated to cost the industry over a trillion dollars a year in administrative waste — and the people hurt most are patients who don't have the time, medical knowledge, or legal language to appeal a denial on their own. MediClear is a four-agent AI system built to close that gap. It takes a raw denial letter and transforms it into a complete, submission-ready appeal in seconds. The system runs four coordinated agents: an Orchestrator that reads and classifies the denial, an Appeal Agent that drafts a clinically-grounded appeal letter citing relevant medical guidelines and ICD-10 diagnosis codes, an Auth Agent that generates prior-authorization paperwork, and a Track Agent that monitors the claim's status and follow-up deadlines going forward. Inference runs through Fireworks AI's gpt-oss-20b model, hosted on AMD EPYC and GPU infrastructure — verified directly via rocminfo output confirming real AMD hardware access, not just a wrapper claiming it. The backend is built with FastAPI and deployed on Render; the frontend is built in React and deployed on Vercel. The entire stack is fully public, open source, and live — anyone can test it right now without any local setup. We built MediClear as second-year CSE students, under a tight hackathon deadline, because I wanted to build something that gives ordinary patients an advocate when they don't have anyone else fighting for them.