Assessment of Probable Opioid Use Disorder Using Electronic Health Record Documentation.

IMPORTANCE

Electronic health records are a potentially valuable source of information for identifying patients with opioid use disorder (OUD).

OBJECTIVE

To evaluate whether proxy measures from electronic health record data can be used reliably to identify patients with probable OUD based on Diagnostic and Statistical Manual of Mental Disorders (Fifth Edition) (DSM-5) criteria.

DESIGN, SETTING, AND PARTICIPANTS: This retrospective cross-sectional study analyzed individuals within the Geisinger health system who were prescribed opioids between December 31, 2000, and May 31, 2017, using a mixed-methods approach. The cohort was identified from 16 253 patients enrolled in a contract-based, Geisinger-specific medication monitoring program (GMMP) for opioid use, including patients who maintained or violated contract terms, as well as a demographically matched control group of 16 253 patients who were prescribed opioids but not enrolled in the GMMP. Substance use diagnoses and psychiatric comorbidities were assessed using automated electronic health record summaries. A manual medical record review procedure using DSM-5 criteria for OUD was completed for a subset of patients. The analysis was conducted beginning from June 5, 2017, until May 29, 2020.

MAIN OUTCOMES AND MEASURES

The primary outcome was the prevalence of OUD as defined by proxy measures for DSM-5 criteria for OUD as well as the prevalence of comorbidities among patients prescribed opioids within an integrated health system.

RESULTS

Among the 16 253 patients enrolled in the GMMP (9309 women [57%]; mean [SD] age, 52 [14] years), OUD diagnoses as defined by diagnostic codes were present at a much lower rate than expected (291 [2%]), indicating the necessity for alternative diagnostic strategies. The DSM-5 criteria for OUD can be assessed using manual medical record review; a manual review of 200 patients in the GMMP and 200 control patients identifed a larger percentage of patients with probable moderate to severe OUD (GMMP, 145 of 200 [73%]; and control, 27 of 200 [14%]) compared with the prevalence of OUD assessed using diagnostic codes.

CONCLUSIONS AND RELEVANCE

These results suggest that patients with OUD may be identified using information available in the electronic health record, even when diagnostic codes do not reflect this diagnosis. Furthermore, the study demonstrates the utility of coding for DSM-5 criteria from medical records to generate a quantitative DSM-5 score that is associated with OUD severity.