Strategic pathways to International Classification of Diseases, 11th Revision, adoption in France and the United States
Bibliographic record
Abstract
The International Classification of Diseases, 11th Revision (ICD-11), developed by the World Health Organization, represents a transformative update to global health data classification systems. Building on the foundation of ICD-10, it introduces innovative features such as multilingual coding, advanced interoperability, postcoordination, and improved specificity, enabling better alignment with modern healthcare and digital information systems. This commentary explores the adoption pathways for ICD-11 in France and the United States, 2 countries with complex healthcare infrastructures and distinct implementation strategies. France's phased roadmap, led by the National Health Information Agency, prioritizes system readiness, workforce training, and pilot testing to ensure smooth integration with hospital information systems. In contrast, the United States, guided by the National Committee on Vital and Health Statistics, focuses on regulatory alignment, funding models, and system modifications to support a seamless transition. The manuscript underscores the critical role of academic research in refining ICD-11's applications, assessing its impact on healthcare quality, and optimizing tools for implementation. Drawing lessons from early adopters globally, we advocate for a coordinated, resource-driven approach to achieve full ICD-11 adoption within 5 years. This transition is poised to enhance health data accuracy, support clinical research, and improve population health outcomes worldwide.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.026 | 0.073 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.028 | 0.023 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".