  Background: Electronic health records (EHRs) are a data source for opioid
research. Opioid use disorder is known to be under-coded as a diagnosis, yet
problematic opioid use can be documented in clinical notes.
  Objectives: Our goals were 1) to identify problematic opioid use from a full
range of clinical notes; and 2) to compare the characteristics of patients
identified as having problematic opioid use, exclusively documented in clinical
notes, 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 two
Veteran Affairs service regions to identify patients with problematic opioid
use. We also used a set of ICD diagnostic codes to identify patients with
opioid use disorder from the same cohort. We compared the demographic and
clinical characteristics of patients identified only through NLP, to those of
patients identified through ICD codes.
  Results: NLP exclusively identified 57,331 patients; 6,997 patients had
positive ICD code identifications. Patients exclusively identified through NLP
were more likely to be women. Those identified through ICD codes were more
likely to be male, younger, have concurrent benzodiazepine prescriptions, more
comorbidities, more care encounters, and less likely to be married. Patients in
the NLP and ICD groups had substantially elevated comorbidity levels compared
to patients not documented as experiencing problematic opioid use.
  Conclusions: NLP is a feasible approach for identifying problematic opioid
use not otherwise recorded by ICD codes. Clinicians may be reluctant to code
for opioid use disorder. It is therefore incumbent on the healthcare team to
search for documentation of opioid concerns within clinical notes.
