AI medical coding copilot.

The problem

Medical coding is buckling under a 20–30% shortage of certified coders, growing backlogs, and rising audit pressure. Nearly 15–20% of claims are initially denied, with coding inaccuracies and thin documentation among the top causes — directly hurting provider cash flow.

The underlying work is manual and slow: coders gather records scattered across EHRs, lab and radiology systems, and scanned PDFs, then read lengthy documentation to identify diagnoses, apply ICD-10 codes against shifting payer rules, and check compliance by hand. Legacy computer-assisted coding tools process documents independently and rely on static rules engines, missing the cross-document clinical picture.

The solution

The AI medical coding copilot helps coders review multi-document clinical cases and generate diagnosis code recommendations with evidence and reasoning. Coders upload several patient documents and receive primary and secondary ICD-10-CM recommendations, each with a confidence score, supporting clinical findings, and a short rationale linked back to the original records.

The differentiators are multi-document clinical understanding — analyzing physician notes, discharge summaries, pathology, radiology, and labs together — plus explainable outputs and human-in-the-loop validation. An admin layer lets teams switch models and upload updated ICD guidelines so reasoning stays aligned with current coding knowledge.

How it works

The copilot uses LLMs plus RAG, running on OpenAI GPT-4.1 / GPT-4o integrated through secure Supabase Edge Functions. On upload, the backend extracts and cleans text, retrieves relevant ICD-10-CM guideline chunks from a pgvector store via semantic similarity, and passes them with the clinical findings into the model to generate codes, rationale, and confidence.

The system prompt enforces grounding — use only information supported by uploaded documents, prioritize confirmed over suspected diagnoses, return "Insufficient clinical evidence available" rather than force a code, and ignore instructions embedded in documents. Coders then accept, edit, or reject each suggestion, and those corrections feed evaluation and continuous improvement.

Who it's for

This is a B2B SaaS product for hospitals, health systems, physician groups, ambulatory surgery centers, and healthcare RCM firms, priced on coding volume. The primary end users — the ICP who work in the tool daily — are medical coders, alongside RCM teams and compliance auditors.

Buyers are CIOs, HIM Directors, CFOs, and Revenue Cycle leaders, with physicians and clinical documentation teams acting as influencers. Healthcare BPOs and medical coding service providers form a secondary user base.

Why it matters

The global medical coding market was estimated at roughly $39.85B in 2024, projected toward $71B by 2030 at about 10% CAGR, with a calculated North American TAM near $6B annually. Generative AI adoption, rising operational costs, and payer scrutiny are pushing hospitals and RCM vendors to modernize legacy workflows.

Across 30 multi-document evaluation cases the system reached 90% exact ICD-match accuracy, a 10% hallucination rate, and 90% rationale-quality pass, with guideline retrieval relevance (66.7%) flagged for improvement. The rollout is a phased pilot — internal, then controlled, then broader — targeting a 50% reduction in coding review time. As a capstone MVP it is not yet HIPAA-certified; formal compliance is a production prerequisite.

At a glance

Project

AI medical coding copilot

Built by

Gaurav Bhagat

One-liner

This project is an AI-assisted medical coding copilot that helps coders review multi-document clinical cases and generate diagnosis code recommendations with evidence and reasoning.