By Kacie Geretz, Director of Growth Enablement
June 11, 2025
Key Takeaway: With ICD-11 already active globally but no confirmed U.S. implementation date, healthcare organizations face a strategic dilemma: invest early in preparation or risk operational disruption when timelines are announced. This article explores ICD-11's major differences from ICD-10, including 55,000+ codes versus 14,400, and reveals how AI technologies can help navigate this transition uncertainty while maintaining current efficiency.
How should healthcare provider organizations prepare for a coding system transition when no implementation timeline exists? With ICD-11 already active globally but the U.S. still operating without a confirmed adoption date, healthcare administrators face a unique strategic challenge: balancing preparation costs against the risk of being caught unprepared when implementation is eventually announced.
To navigate this preparation challenge effectively, it’s important to understand both what ICD-11 represents and why the U.S. timeline uncertainty creates such complex planning decisions.
ICD-11 is the World Health Organization's (WHO) latest globally effective revision to the International Classification of Diseases system, featuring over 55,000 codes and an enhanced digital design. The WHO periodically updates the ICD system to align with the latest medical knowledge and clinical practices, and ICD-11 represents the organization's most comprehensive revision to date.
While ICD-11 became effective globally on January 1, 2022, implementation varies significantly across different countries and healthcare systems (1). More than half of the 120 WHO member countries are currently implementing ICD-11, but no definitive implementation date has been established for ICD-11 adoption in the United States (2). While some projections suggest potential implementation between 2025 and 2027, industry experts believe the timeline may extend 10 to 15 years before full implementation occurs (2).
This uncertainty creates significant planning challenges for healthcare administrators. Organizations must balance the cost of early preparation against the risk of being unprepared when implementation is eventually announced. Early preparation requires significant investment in training, system updates, and workflow modifications, while delayed preparation risks operational disruption and compliance challenges.
Given the timeline uncertainty surrounding ICD-11, understanding the scope of changes becomes critical for making informed preparation decisions. The differences between ICD-10 and ICD-11 will determine your training requirements, technology investments, and operational adjustments when implementation is eventually announced.
Expanded Code Structure and Volume: ICD-11 encompasses 28 chapters with over 55,000 codes compared to ICD-10's 22 chapters and 14,400 codes (3). This expansion provides substantially greater diagnostic specificity but requires more extensive training resources for your coding teams.
Enhanced Clinical Terminology: ICD-11 demonstrates significantly improved alignment with current clinical terminology, including expanded codes for social determinants of health. For your organization, this means more precise capture of patient conditions, potentially improving quality reporting and risk adjustment accuracy.
Digital-Native Design: ICD-11 is designed as a fully digital system with online coding tools, multilingual support, and advanced search capabilities that can interpret over 1.6 million medical terms (4). This architectural difference may require significant updates to your existing coding workflows and technology infrastructure.
The challenges outlined above, from expanded code volumes to infrastructure requirements to training complexity, represent significant operational hurdles that your organization will eventually face. AI technologies offer strategic advantages for managing these transition challenges while maintaining current ICD-10 efficiency, providing a flexible approach that adapts to whatever timeline ultimately emerges.
Automated Documentation Analysis and Code Translation: AI systems using natural language processing may help analyze clinical documentation to suggest relevant ICD-11 codes from the expanded 55,000+ code structure or assist with crosswalk mapping between ICD-10 and ICD-11 systems. While complete automation may not be possible, these capabilities could significantly reduce the resource requirements typically associated with major coding system transitions.
Enhanced Training and Real-Time Coding Support: AI-powered platforms may provide personalized learning modules that simulate ICD-11 coding scenarios, helping coding teams practice with the system's digital-native features and complex postcoordination requirements. Such support systems may help accelerate competency development while reducing coding errors during critical transition phases.
Predictive Analytics and Quality Monitoring: AI technologies could help organizations optimize risk adjustment coding by analyzing patient populations and assisting with ICD-11's granular codes to maintain accurate reimbursement levels. These systems may also provide denial prevention capabilities by analyzing historical claims data to identify potential coding errors before submission. This type of predictive oversight could help maintain revenue integrity while building the quality infrastructure needed for eventual ICD-11 compliance.
No official implementation date has been established for ICD-11 in the United States. While some projections suggest potential implementation between 2025 and 2027, industry experts believe the timeline may extend 10 to 15 years. For your organization, this means focusing on maintaining ICD-10 efficiency while building flexible preparation strategies that can scale when timelines are announced.
ICD-11's 55,000+ codes compared to ICD-10's 14,400 codes will require significant training investment and may impact productivity during transition periods. However, the enhanced clinical specificity may improve quality reporting and risk adjustment accuracy. The key is choosing preparation strategies that provide value for current operations while building future readiness.
AI systems can assist with code mapping, training acceleration, and quality assurance during transition periods. While complete automation isn't possible due to structural differences between systems, AI can significantly reduce resource requirements and operational disruption typically associated with major coding system transitions.
The primary risk is operational disruption when implementation timelines are finally announced. Organizations that begin preparation early can spread costs over longer periods, while those who wait may face compressed timelines requiring significant resource allocation. The key is choosing preparation strategies that enhance current ICD-10 operations while building ICD-11 readiness.
(12 February 2025). ICD-11 Implementation. World Health Organization. Retrieved May 22, 2025, from https://www.who.int/standards/classifications/frequently-asked-questions/icd-11-implementation
Stanfill, M. (30 May 2024). US Timeline for ICD-11 Implementation. Libman Education. Retrieved May 22, 2025, from https://libmaneducation.com/us-timeline-for-ICD-11-implementation/
(14 February 2025). What Is ICD-11? AAPC. Retrieved May 22, 2025, from https://www.aapc.com/resources/what-is-icd-11
(14 February 2025). WHO Releases 2025 Update to the International Classification of Diseases (ICD-11). WHO. Retrieved May 22, 2025, from https://www.who.int/news/item/14-02-2025-who-releases-2025-update-to-the-international-classification-of-diseases-(icd-11)
Kacie Geretz, RHIA, CPMA, CPC, CCA is the Director of Growth Enablement at Nym, where she aligns Nym’s product roadmap with the evolving needs of health system partners and serves as the externally-facing expert on Nym’s autonomous medical coding engine. A graduate of The Ohio State University’s Health Information Management program, Kacie brings deep expertise across the revenue cycle—having led revenue integrity programs, built managed care contracting and credentialing infrastructure, and driven denials and A/R process improvement initiatives. She is passionate about advancing healthcare automation and regularly shares insights on coding innovation and RCM transformation.
Powering automation in revenue cycle management.
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