How a US RCM Operation Made Every Code Traceable With AI
Full traceability
Not disclosed
Not disclosed
Manual chart coding was slow and costly, limited to a few dozen charts per coder per day, and quality depended on individual judgment. The team needed AI assistance but required full auditability, because coders and auditors must see why each code was assigned. Generic LLM tools were unsuitable since an unexplained code is unbillable and unauditable.
TrustEvals delivered a production coder workflow application with role-based assign-code-review-audit functionality, plus an autonomous coding pipeline with three-model validation and human gatekeeping. Every suggested code traces to source chart text with confidence scoring and independent reviewer validation.
The autonomous pipeline runs OCR, then clinical NLP entity extraction, then ICD-10/CPT mapping via UMLS/SNOMED ontologies, then an LLM coder with hallucination detection. A three-stage flow of pre-validation, coding, and an independent reviewer model applies human accept/edit gates before anything is finalized. The production workspace adds real-time collaboration and audit logging.
Best fit for regulated coding or claims operations that need AI throughput but require every AI output to be explainable and audit-traceable.
Every code traceable to source
Three-model validation with human gates
Live with a launch design partner
Unmukt Raizada
A US revenue-cycle operation worked with the experts to build a coder workspace and an autonomous coding pipeline. Rather than a generic LLM, the pipeline runs OCR, clinical NLP, and ontology-backed ICD-10/CPT mapping through three-model validation with human accept-edit gates, so every suggested code links back to the exact source chart text with a confidence score.
The approach combined document processing, clinical natural-language processing, and decision-support scoring. It uses OCR, NLP entity extraction, UMLS/SNOMED ontologies for ICD-10/CPT mapping, an LLM coder with hallucination detection, and an independent reviewer model. No specific foundation model or vendor is named in the record.
Every AI-suggested code is evidence-linked to source chart text with confidence scoring, keeping outputs billable and auditable. Three-model validation with human gates guards against hallucinated codes, and the coder workspace and pipeline reached proof-of-concept, live with a launch design partner.
The record does not specify a timeline. At the time of the case the coder workspace and autonomous pipeline had reached proof-of-concept and were live with a launch design partner.
It is best suited to regulated coding or claims operations that need AI throughput but require every AI output to be explainable and audit-traceable.
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