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. ### Competing Interest Statement The authors have declared no competing interest. ### Funding Statement This work was supported by VA HSRD grant 1I21HX003278-01A1, and by AHRQ grant R01 HS28450-01A1. ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: The IRBs of the Veterans Administration Health Services Research Division and the George Washington University School of Medicine and Health Sciences have determined this research is exempt from review, as it involves deidentified data. I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes The data is available through the US Veterans Administration.