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Leveraging an Innovation Model to Facilitate ICD-11 Implementation.

Kathy L Giannangelo, Michael B Pine, Christopher P Tompkins

Perspectives in Health Information Management /

|March 26, 2025

Summary

This summary is machine-generated.

The proposed C-CLEAR model enhances the International Classification of Diseases, 11th Revision (ICD-11) implementation in the US. This innovation facilitates complex clinical data capture and reporting using standardized ICD-11 codes.

Keywords:

ICD-10 ICD-10-CM ICD-11 International Classification of Diseases episode of care health information exchange interoperability morbidity ontology value-based healthcare

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Area of Science:

Health Informatics
Medical Classification Systems

Background:

The International Classification of Diseases, 11th Revision (ICD-11) represents a significant upgrade from previous versions, leveraging informatics for enhanced functionality.
Current implementation strategies for ICD-11 in the US may face challenges related to complexity and data capture.

Purpose of the Study:

To propose an innovative model, C-CLEAR, to facilitate the adoption and effective utilization of ICD-11 within the United States.
To enhance the capability of electronic coding tools for capturing rich clinical data.

Main Methods:

Introduction of ICD-11 Comprehensive Clinical Linearization, Evolution and Response (C-CLEAR).
Development of syntactical rules for combining ICD-11 stem and extension codes.
Integration of C-CLEAR into electronic coding tools for natural language processing.

Main Results:

The C-CLEAR model enables the transmission of complex clinical concepts via standardized ICD-11 code clusters.
Supports detailed clinical data capture, including condition acuity, severity, and pharmacological treatments.
Demonstrates potential for accelerating ICD-11 implementation with minimal disruption.

Conclusions:

The C-CLEAR model offers a promising approach to streamline ICD-11 implementation in the US.
This model can maximize net benefits by improving data richness and reporting efficiency.
Further vetting, testing, and stakeholder input are necessary for successful adoption.