title: Enhancing Risk Assessment in Patients Receiving Chronic Opioid Analgesic Therapy Using Natural Language Processing.
abstract: OBJECTIVE
  Clinical guidelines for the use of opioids in chronic noncancer pain recommend assessing risk for aberrant drug-related behaviors prior to initiating opioid therapy. Despite recent dramatic increases in prescription opioid misuse and abuse, use of screening tools by clinicians continues to be underutilized. This research evaluated natural language processing (NLP) together with other data extraction techniques for risk assessment of patients considered for opioid therapy as a means of predicting opioid abuse.
  
  
  METHODS
  Using a retrospective cohort of 3,668 chronic noncancer pain patients with at least one opioid agreement between January 1, 2007, and December 31, 2012, we examined the availability of electronic health record structured and unstructured data to populate the Opioid Risk Tool (ORT) and other selected outcomes. Clinician-documented opioid agreement violations in the clinical notes were determined using NLP techniques followed by manual review of the notes.
  
  
  RESULTS
  Confirmed through manual review, the NLP algorithm had 96.1% sensitivity, 92.8% specificity, and 92.6% positive predictive value in identifying opioid agreement violation. At the time of most recent opioid agreement, automated ORT identified 42.8% of patients as at low risk, 28.2% as at moderate risk, and 29.0% as at high risk for opioid abuse. During a year following the agreement, 22.5% of patients had opioid agreement violations. Patients classified as high risk were three times more likely to violate opioid agreements compared with those with low/moderate risk.
  
  
  CONCLUSIONS
  Our findings suggest that NLP techniques have potential utility to support clinicians in screening chronic noncancer pain patients considered for long-term opioid therapy.
authors.affiliation: Essentia Institute of Rural Health, Duluth, Minnesota.
authors.forename: Irina V
authors.initials: IV
authors.lastname: Haller
authors.affiliation: Essentia Institute of Rural Health, Duluth, Minnesota.
authors.forename: Colleen M
authors.initials: CM
authors.lastname: Renier
authors.affiliation: Essentia Institute of Rural Health, Duluth, Minnesota.
authors.forename: Mitch
authors.initials: M
authors.lastname: Juusola
authors.affiliation: Essentia Institute of Rural Health, Duluth, Minnesota.
authors.forename: Paul
authors.initials: P
authors.lastname: Hitz
authors.affiliation: North American Medical Affairs.
authors.forename: William
authors.initials: W
authors.lastname: Steffen
authors.affiliation: North American Medical Affairs.
authors.forename: Michael J
authors.initials: MJ
authors.lastname: Asmus
authors.affiliation: North American Medical Affairs.
authors.forename: Terri
authors.initials: T
authors.lastname: Craig
authors.affiliation: Statistics, Global Innovative Pharma.
authors.forename: Jack
authors.initials: J
authors.lastname: Mardekian
authors.affiliation: Outcomes and Evidence, Global Health and Value, Pfizer Inc., New York, New York, USA.
authors.forename: Elizabeth T
authors.initials: ET
authors.lastname: Masters
authors.affiliation: Essentia Institute of Rural Health, Duluth, Minnesota.
authors.forename: Thomas E
authors.initials: TE
authors.lastname: Elliott
mesh_terms: D000293:Adolescent; D000328:Adult; D000368:Aged; D059350:Chronic Pain / Q000188:drug therapy; D015331:Cohort Studies; D057286:Electronic Health Records; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged; D009323:Natural Language Processing; D009293:Opioid-Related Disorders / Q000517:prevention & control*; D012189:Retrospective Studies; D018570:Risk Assessment; D012307:Risk Factors; D015813:Substance Abuse Detection / Q000379:methods*; D055815:Young Adult
keywords: Chronic Pain; Electronic Health Records; Natural Language Processing; Opioid Analgesics; Risk Assessment