title: The prevalence of problem opioid use in patients receiving chronic opioid therapy: computer-assisted review of electronic health record clinical notes.
abstract: 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. An additional 4.1% of COT patients had an International Classification of Disease, version 9 (ICD-9) diagnosis without NLP-identified problem opioid use. Agreement between the NLP methods and ICD-9 coding was moderate (kappa = 0.61). Over one-third of the NLP-positive patients did not have an ICD-9 diagnostic code for opioid abuse or dependence. We used structured EHR data to identify 14 risk indicators for problem opioid use. Forty-seven percent of the COT patients had 3 or more risk indicators. The prevalence of problem opioid use was 9.6% among patients with 3 to 4 risk indicators, 26.6% among those with 5 to 6 risk indicators, and 55.04% among those with 7 or more risk indicators. 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.
authors.affiliation: US Medical Affairs, Pfizer Inc., New York, NY, USA Group Health Research Institute, Seattle, WA, USA.
authors.forename: Roy E
authors.initials: RE
authors.lastname: Palmer
authors.forename: David S
authors.initials: DS
authors.lastname: Carrell
authors.forename: David
authors.initials: D
authors.lastname: Cronkite
authors.forename: Kathleen
authors.initials: K
authors.lastname: Saunders
authors.forename: David E
authors.initials: DE
authors.lastname: Gross
authors.forename: Elizabeth
authors.initials: E
authors.lastname: Masters
authors.forename: Sean
authors.initials: S
authors.lastname: Donevan
authors.forename: Timothy R
authors.initials: TR
authors.lastname: Hylan
authors.forename: Michael
authors.initials: M
authors.lastname: Von Kroff
mesh_terms: D000293:Adolescent; D000328:Adult; D000368:Aged; D000701:Analgesics, Opioid / Q000009:adverse effects*; D057286:Electronic Health Records / Q000639:trends*; D005260:Female; D006801:Humans; D038801:International Classification of Diseases / Q000639:trends; D008297:Male; D008875:Middle Aged; D009323:Natural Language Processing; D009293:Opioid-Related Disorders / Q000175:diagnosis* / Q000453:epidemiology*; D015995:Prevalence; D055815:Young Adult
chemical_list: D000701:Analgesics, Opioid