The latest in risk adjustment technology for health plans

The Health Language Risk Adjustment Solution leverages AI, machine learning, optical character recognition, and specialized, clinically intelligent natural language processing (cNLP) to support clinical validation.

Designed to seamlessly capture the value of hidden text captured in unstructured text within a patient record, the Coder Workbench workflow application tool abstracts valuable diagnoses, clinical indicators, and metadata from within a patient record, enabling coders the ability to accurately and comprehensively code a medical chart.

The Coder Workbench was designed by coders for coders. The intuitive user interface organizes the medical chart in a way that helps coders increase review speed, provides team visibility across projects, and delivers meaningful analytics that improves workflow processes.

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Key Benefits of the Coder Workbench for Health Plans


Improved accuracy and outcomes

Clinically intelligent NLP (cNLP) identifies and extracts clinical insights from unstructured data with unprecedented accuracy – finding relevant information that other NLP engines miss.


Increased efficiency and production

Clinically accurate tools reduce review times, increase the volume of chart audits, and expedite quality assurance initiatives while maintaining 95% or higher accuracy rates.


User-friendly UI for coder satisfaction

Methodically highlight clinical conditions or diagnoses, identify the related clinical indicators, and analyze metadata into a user-friendly format to support coder workflows.

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Key features of the Coder Workbench

Smart chart organization

The user interface creates a table of contents within the medical chart for coders to navigate sections efficiently.

Built-in coding assistant

Auto-populate and code refinement tools ensure complete and accurate ICD-10 and HCC code capture for maximum coder review speed and accuracy.

Embedded coding guidelines

Medicare Advantage code-specific guidelines and logic are integrated to ensure sufficient evidence for code capture.

AI-assisted clinical NLP

Advanced technology trained to understand and extract clinical indicators captured in unstructured text within the medical record.

Proprietary clinical terminologies

Semantically enriched terminology libraries ensure superior ICD-10 code refinement.

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