Ertas for Medical Coding

The Challenge

Medical coding — the process of translating clinical documentation into standardized diagnosis and procedure codes — is the backbone of healthcare revenue cycle management. Accurate coding determines reimbursement rates, compliance with payer requirements, and regulatory reporting. The ICD-10-CM system alone contains over 72,000 diagnosis codes, and selecting the correct code requires understanding clinical context, specificity requirements, coding guidelines, and payer-specific rules. A single digit difference between codes can mean the difference between a clean claim and a denial.

The medical coding workforce faces a chronic shortage, with the American Health Information Management Association reporting tens of thousands of unfilled coding positions. This shortage creates backlogs that delay revenue, increases error rates as overworked coders rush through charts, and drives up costs as organizations rely on expensive contract coders. Generic AI tools cannot address this gap because medical coding requires simultaneously understanding clinical language, applying complex coding guidelines, and navigating the specificity hierarchy of classification systems — a combination of domain knowledge that general models do not possess.

The Solution

Ertas enables healthcare organizations to fine-tune AI models on their own coded clinical documentation, creating a coding assistant that suggests appropriate codes based on the specific patterns, specialties, and payer requirements that the organization encounters. With Ertas Studio, revenue cycle teams train models on pairs of clinical notes and their verified code assignments, teaching the model the relationship between clinical language and code selection at the level of specificity their coders achieve.

The fine-tuned model serves as a coding assistant that analyzes clinical documentation and suggests primary and secondary codes with supporting rationale — citing the specific clinical findings that justify each code selection. Deployed on-premise through Ollama or Ertas Cloud, the model processes charts in real time as coders work, presenting suggestions that coders review and approve. This human-in-the-loop workflow maintains coding accuracy while dramatically increasing coder throughput. Ertas Vault ensures all PHI used in training and inference is encrypted and access-controlled, with audit trails that satisfy HIPAA requirements.

Key Features

Example Workflow

A 500-bed hospital system processes 2,000 inpatient and 8,000 outpatient encounters weekly. The coding team exports 100,000 historically coded encounters — clinical notes paired with verified ICD-10 and CPT code assignments — and uploads them to Ertas Vault after automated PHI scrubbing. Using Ertas Studio, they fine-tune a model specialized for their top 15 service lines, which account for 80% of coding volume. The model is deployed on-premise and integrated with their coding workflow through a browser extension. As coders open each chart, the AI suggests primary and secondary codes with confidence scores and supporting clinical evidence highlighted in the documentation. Coders review suggestions, accepting or correcting them with a single click. Coding throughput increases by 35%, the denial rate due to coding errors drops from 8% to 3%, and the organization fills fewer contract coder positions, saving over $600,000 annually.