An AI Method for Assessing Coding Consistency in a Large Dataset

Objective We developed a method to assess the consistency of the assignment of ICD codes, using coding performed at a United States health system at the time of the transition from ICD-9CM to ICD-10CM. Methods Using clusters of equivalent codes derived from the US Centers for Disease Control General Equivalence Mapping (GEM) tables, ICD assignments occurring during the ICD-9CM to ICD-10CM transition were evaluated in EHR data from the US Veterans Administration Central Data Warehouse, using a deep learning model based on 860 covariates. The model was then used to detect abrupt changes across the transition; additionally changes at each VA station were examined. Results Many of the 687 most-used code clusters had ICD-10CM assignments differing greatly from that predicted by the GEM from the codes used in ICD-9CM. Notably, the observed transition patterns varied widely across care locations. Conclusion Machine learning can model variability across time and across location, enabling an assessment of coding consistency. Expert review is not scalable, deep learning model applied to a large dataset of EHR records provides an approximation of ground truth.