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Background: Electronic health records (EHRs) are a data source for opioidresearch. Opioid use disorder is known to be under-coded as a diagnosis, yetproblematic opioid use can be documented in clinical notes.  Objectives: Our goals were 1) to identify problematic opioid use from a fullrange of clinical notes; and 2) to compare the characteristics of patientsidentified as having problematic opioid use, exclusively documented in clinicalnotes, to those having documented ICD opioid use disorder diagnostic codes.  Materials and Methods: We developed and applied a natural language processing(NLP) tool to the clinical notes of a patient cohort (n=222,371) from twoVeteran Affairs service regions to identify patients with problematic opioiduse. We also used a set of ICD diagnostic codes to identify patients withopioid use disorder from the same cohort. We compared the demographic andclinical characteristics of patients identified only through NLP, to those ofpatients identified through ICD codes.  Results: NLP exclusively identified 57,331 patients; 6,997 patients hadpositive ICD code identifications. Patients exclusively identified through NLPwere more likely to be women. Those identified through ICD codes were morelikely to be male, younger, have concurrent benzodiazepine prescriptions, morecomorbidities, more care encounters, and less likely to be married. Patients inthe NLP and ICD groups had substantially elevated comorbidity levels comparedto patients not documented as experiencing problematic opioid use.  Conclusions: NLP is a feasible approach for identifying problematic opioiduse not otherwise recorded by ICD codes. Clinicians may be reluctant to codefor opioid use disorder. It is therefore incumbent on the healthcare team tosearch for documentation of opioid concerns within clinical notes.

A Comparison of Veterans with Problematic Opioid Use Identified through Natural Language Processing of Clinical Notes versus Using Diagnostic Codes