Coding Fairness: Detecting Demographic-Related Coding Discrepancies in ICD Code Assignments

Coded clinical data are crucial in biomedical informatics research. While it is well known that electronic medical records often contain coding errors, numerous studies rely on International Classification of Diseases (ICD) codes for phenotyping in cohort assembly, statistical analysis, and AI modeling. Although fairness hasbecome an important focus in AI research, the potential biases embedded in coded clinical data have received less attention. In this study, we employed a race- and sex-agnostic AI phenotyping model to assess coding fairness across 203 ICD code blocks within the Veterans Health Administration Clinical Data Warehouse. Our findings revealed variability in coding consistency across demographic subgroups, including sex, race, and ethnicity. Notably, over 50% of the code blocks exhibitedstatisticallysignificant differences in discrepancies between AI-generated and ICD-based phenotypesacross these demographic groups. These results suggest the need to recognize and address demographic-related coding discrepancies to ensure coding fairness.