Leveraging an Innovation Model to Facilitate ICD-11 Implementation.

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.