Pain
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To estimate the prevalence of problem opioid use, we used natural language processing (NLP) techniques to identify clinical notes containing text indicating problem opioid use from over 8 million electronic health records (EHRs) of 22,142 adult patients receiving chronic opioid therapy (COT) within Group Health clinics from 2006 to 2012. Computer-assisted manual review of NLP-identified clinical notes was then used to identify patients with problem opioid use (overuse, misuse, or abuse) according to the study criteria. These methods identified 9.4% of patients receiving COT as having problem opioid use documented during the study period. ⋯ Higher rates of problem opioid use were observed among young COT patients, patients who sustained opioid use for more than 4 quarters, and patients who received higher opioid doses. Methods used in this study provide a promising approach to efficiently identify clinically recognized problem opioid use documented in EHRs of large patient populations. Computer-assisted manual review of EHR clinical notes found a rate of problem opioid use of 9.4% among 22,142 COT patients over 7 years.