title: Automated prediction of risk for problem opioid use in a primary care setting.
abstract: Identification of patients at increased risk for problem opioid use is recommended by chronic opioid therapy (COT) guidelines, but clinical assessment of risks often does not occur on a timely basis. This research assessed whether structured electronic health record (EHR) data could accurately predict subsequent problem opioid use. This research was conducted among 2,752 chronic noncancer pain patients initiating COT (≥70 days' supply of an opioid in a calendar quarter) during 2008 to 2010. Patients were followed through the end of 2012 or until disenrollment from the health plan, whichever was earlier. Baseline risk indicators were derived from structured EHR data for a 2-year period prior to COT initiation. Problem opioid use after COT initiation was assessed by reviewing clinician-documented problem opioid use in EHR clinical notes identified using natural language processing techniques followed by computer-assisted manual review of natural language processing-positive clinical notes. Multivariate analyses in learning and validation samples assessed prediction of subsequent problem opioid use. The area under the receiver operating characteristic curve (c-statistic) for problem opioid use was .739 (95% confidence interval = .688, .790) in the validation sample. A measure of problem opioid use derived from a simple weighted count of risk indicators was found to be comparably predictive of the natural language processing measure of problem opioid use, with 60% sensitivity and 72% specificity for a weighted count of ≥4 risk indicators.
  
  
  CONCLUSIONS
  An automated surveillance method utilizing baseline risk indicators from structured EHR data was moderately accurate in identifying COT patients who had subsequent problem opioid use.
authors: North America Medical Affairs, Global Innovative Pharma, Pfizer Inc, New York, New York.
authors: Timothy R
authors: TR
authors: Hylan
authors: Group Health Research Institute, Seattle, Washington. Electronic address: vonkorff.m@ghc.org.
authors: Michael
authors: M
authors: Von Korff
authors: Group Health Research Institute, Seattle, Washington.
authors: Kathleen
authors: K
authors: Saunders
authors: Outcomes & Evidence, Global Health & Value, Pfizer Inc, New York, New York.
authors: Elizabeth
authors: E
authors: Masters
authors: North America Medical Affairs, Global Innovative Pharma, Pfizer Inc, New York, New York.
authors: Roy E
authors: RE
authors: Palmer
authors: Group Health Research Institute, Seattle, Washington.
authors: David
authors: D
authors: Carrell
authors: Group Health Research Institute, Seattle, Washington.
authors: David
authors: D
authors: Cronkite
authors: Statistics, Global Innovative Pharma, Pfizer Inc, New York, New York.
authors: Jack
authors: J
authors: Mardekian
authors: North America Medical Affairs, Global Innovative Pharma, Pfizer Inc, New York, New York.
authors: David
authors: D
authors: Gross
mesh_terms: D000293:Adolescent; D000328:Adult; D000368:Aged; D000701:Analgesics, Opioid / Q000009:adverse effects* / Q000627:therapeutic use; D019540:Area Under Curve; D059350:Chronic Pain / Q000188:drug therapy / Q000453:epidemiology; D057286:Electronic Health Records; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged; D015999:Multivariate Analysis; D009323:Natural Language Processing; D009293:Opioid-Related Disorders / Q000175:diagnosis* / Q000453:epidemiology; D010363:Pattern Recognition, Automated / Q000379:methods*; D011320:Primary Health Care / Q000379:methods*; D012372:ROC Curve; D012306:Risk; D018570:Risk Assessment / Q000379:methods*; D012307:Risk Factors; D012680:Sensitivity and Specificity; D055815:Young Adult
chemical_list: D000701:Analgesics, Opioid
keywords: Chronic opioid therapy; opioid surveillance; primary care; risk for problem opioid use