Supplementary file 1_ICD-11 and diabetes: opportunities for precision public health through enhanced classification.docx

Third, its postcoordination mechanism allows multidimensional documentation of complications, drug-induced diabetes, and unstable disease states, generating structured, machine-readable data with potential for real-world evidence generation and health system performance assessment. However, early implementation experiences across multiple countries reveal substantial challenges—including heterogeneous adoption timelines, coding inconsistencies, and workforce training gaps—that may constrain the realization of these benefits in practice.

We also discuss the implications of these advances for health systems, including value-based reimbursement models and the production of actionable evidence for policy makers.</p>Conclusion<p>ICD-11 repositions diabetes classification toward a framework that offers substantially enhanced capacity for etiological and granular representation of the disease. By generating standardized, granular population-level data, it offers a potential infrastructure for precision public health.

Yet realizing this potential will depend on health system capacity, workforce training, and sustained investment in implementation—particularly in resource-limited settings, where the risk of widening disparities is most acute. Future refinements should integrate genomic descriptors and dynamic update mechanisms to keep pace with rapid advances in diabetes research, with continued attention to health equity and global applicability.</p>