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A US healthcare revenue-cycle (RCM) operation
Evidence-Linked Medical Coding Coders Can Trust
A production coder workspace plus an explainable, human-gated AI coding assist where every suggested code traces back to the chart.
The proof
The results, kept honest.
01
The challenge.
Chart coding was manual, slow and costly, with throughput capped at a few dozen charts per coder per day and quality dependent on individual judgment. Leadership wanted AI leverage without surrendering auditability: coders and auditors had to be able to see why every code was assigned. Generic LLM coding was a non-starter, because an unexplained code is unbillable and unauditable.
02
The approach.
A production coder workspace with role-based assign/code/review/audit, real-time collaboration and full audit logging.
An autonomous-coding pipeline: OCR → clinical NLP entity extraction → ICD-10/CPT mapping grounded on clinical ontologies (UMLS/SNOMED) → an LLM coder with confidence scoring, hallucination detection and evidence-linking, so every code points back to the exact chart text.
A three-model pipeline (pre-validation → coding → an independent reviewer model) with a human accept/edit gate on every output.
03
What shipped.
A deployed coder workflow application (in production), plus a working autonomous-coding POC running with a launch design partner — including the confidence/evidence-linking layer and the independent-reviewer loop.
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One builder, across the board.
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