Kushas Khadka@kushas_khadka204

US hospitals and clinics are losing $125B every year due to delayed and inaccurate medical coding. While 8 out of 10 citizens receive medical bills with at least one error. Current solutions for both citizens and hospitals do not seem to be sufficient: even today, more than 50 percent of hospitals rely on the manual labor of medical coders. Although the large foundation models do not perform according to industry standards, we now propose a new framework, which combines multi-agent architecture with a RAG pipeline. At present there are two main functionalities: 1. Extrapolate via Llama 3.2 the content of the codes of a medical receipt (e.g., an itemised bill) 2. Assign ICD-10 codes to a clinical note, explaining them and offering alternatives. The solution outperforms some of the solutions currently on the market.

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