Assessment of Probable Opioid Use Disorder Using Electronic Health Record Documentation

Abstract

Key Points Question Are medication monitoring programs within a hospital associated with more accurate identification of patients with opioid use disorder through the use of proxy Diagnostic and Statistical Manual of Mental Disorders (Fifth Edition) ( DSM-5) criteria for opioid use disorder extracted from electronic health records? Findings This cross-sectional study demonstrated that DSM-5 criteria for opioid use disorder can be extracted through review of electronic health records and that patients who are part of a drug monitoring program had a higher mean prevalence of opiod use disorder and a higher mean number of psychiatric comorbidities associated with opioid use disorder. Meaning Proxy measures that rely on multiple sources of data, including prescription drug history and notes in the electronic health record, may help identify patients with opioid use disorder who have not received a diagnosis.

Two Data-Driven Approaches to Identifying the Spectrum of Problematic Opioid Use: A Pilot Study within a Chronic Pain Cohort

“…We reviewed records using a keyword template developed from keywords in the Diagnostic and Statistical Manual of Mental Disorders, 5 th Ed. (DSM V) criteria for OUD [34], the Addiction Behaviors Checklist [35], and previous studies describing problematic opioid use detection in EHRs [15,19,20]. Periodic interim analyses assessed word performance, and we trimmed duplicate words (i.e.…”

“…Several existing methods have utilized EHR data for OUD prediction [14][15][16][17][18][19][20]. Some models identify OUD cohorts using ICD codes [16][17], but this approach likely underrepresents problematic opioid use [13].…”

“…For example, one study used machine learning methods to produce a continuous measure of OUD risk in Medicare data purchased through the Centers for Medicare and Medicaid (CMS) [22]. These studies provide indispensable insight into the continuum of problematic opioid use, but come at the expense of poor reproducibility in most EHR systems [13,[20][21][22].…”

Genetics and prescription opioid use (GaPO): study design for consenting a cohort from an existing biobank to identify clinical and genetic factors influencing prescription opioid use and abuse

“…As part of the consent process, patients give permission to access their entire health record and give permission to link non-health record data (such as insurance claims, external prescription databases, etc.). Following enrollment, the study plan comprises several elements, including estimating an OUD risk score based on chart review criteria [ 11 ], extracting brain imaging using the clinical imaging database pipeline, genomic analyses, and assessment of phenotypic stability based on EHR review. More detailed analytic plans are described in the Data Analysis section, below.…”

“…The same study reports that depression, anxiety, illicit drug use, post-traumatic stress disorder, alcohol dependence, being under 65 years old, and patient-reported assessments of poor health are all associated with increased risk for OUD. In subsequent analyses of the Geisinger population, we find that patients treated with opioids for chronic non-progressive pain, who are enrolled in a contract-based medication management program, are much more likely to have characteristics of OUD determined via chart review, as well as comorbid conditions, such as depression and anxiety [ 11 ].…”