@article{ref1,
title="Identification of opioid use disorder using electronic health records: beyond diagnostic codes",
journal="Drug and alcohol dependence",
year="2023",
author="Poulsen, Melissa N. and Nordberg, Cara M. and Troiani, Vanessa and Berrettini, Wade and Asdell, Patrick B. and Schwartz, Brian S.",
volume="251",
number="",
pages="e110950-e110950",
abstract="BACKGROUND: We used structured and unstructured electronic health record (EHR) data to develop and validate an approach to identify moderate/severe opioid use disorder (OUD) that includes individuals without prescription opioid use or chronic pain, an underrepresented population. <br><br>METHODS: Using electronic diagnosis grouper text from EHRs of ~1 million patients (2012-2020), we created indicators of OUD-with &quot;tiers&quot; indicating OUD likelihood-combined with OUD medication (MOUD) orders. We developed six sub-algorithms with varying criteria (multiple vs single MOUD orders, multiple vs single tier 1 indicators, tier 2 indicators, tier 3 and 4 indicators). Positive predictive values (PPVs) were calculated based on chart review to determine OUD status and severity. We compared demographic and clinical characteristics of cases identified by the sub-algorithms. <br><br>RESULTS: In total, 14,852 patients met criteria for one of the sub-algorithms. Five sub-algorithms had PPVs ≥0.90 for any severity OUD; four had PPVs ≥0.90 for moderate/severe OUD. Demographic and clinical characteristics differed substantially between groups. Of identified OUD cases, 31.3% had no past opioid analgesic orders, 79.7% lacked evidence of chronic prescription opioid use, and 43.5% lacked a chronic pain diagnosis. <br><br>DISCUSSION: Incorporating unstructured data with MOUD orders yielded an approach that adequately identified moderate/severe OUD, identified unique demographic and clinical sub-groups, and included individuals without prescription opioid use or chronic pain, whose OUD may stem from illicit opioids. <br><br>FINDINGS show that incorporating unstructured data strengthens EHR algorithms for identifying OUD and suggests approaches limited to populations with prescription opioid use or chronic pain exclude many individuals with OUD.<p /> <p>Language: en</p>",
language="en",
issn="0376-8716",
doi="10.1016/j.drugalcdep.2023.110950",
url="http://dx.doi.org/10.1016/j.drugalcdep.2023.110950"
}