Codes the chart says are there, surfaced with the evidence.
The ICD-10 Extractor reads clinical documentation and surfaces ICD-10 conditions with the chart evidence and the coding logic, ready for your coders and clinicians to confirm.
How it operates
Three steps from chart to confirmed code.
Reads the record.
Pulls from the longitudinal patient record assembled by the data layer, including notes, problem lists, encounters, labs, and meds.
Applies coding logic.
Uses the ICD-10 library and the relevant policy logic from the Context Layer to surface supportable conditions.
Hands off to your team.
Each suspect comes with chart evidence, the coding rule applied, and the rationale, ready for coder confirmation.
Where it plugs in
A standalone Ai Worker. And a building block.
The ICD-10 Extractor is a standalone Ai Worker and a building block. It powers HCC retrospective work, RCM coding support, and any workflow where structured ICD-10 evidence matters. Built on Gwen, governed by the Context Layer, grounded in the data layer. Every suspect carries source documentation, the coding rule applied, and the rationale, so your coders can confirm or adjust.
HCC retrospective and prospective programs
RCM coding support and claim preparation
Clinical documentation improvement queries
Quality measure abstraction
Risk stratification and population analytics
How it deploys
First confirmed suspects within the first deployment cycle.
The ICD-10 Extractor can run as a standalone Ai Worker on a defined population or as a component inside a broader workflow like HCC retrospective or RCM coding support. Deployment typically follows the discovery, integration, pilot, and scale path that runs across the Operating System. First confirmed suspects usually arrive within the first deployment cycle.