ArticleDrug and alcohol dependence2023
Identification of opioid use disorder using electronic health records: Beyond diagnostic codes.
Article in Drug and alcohol dependence, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 1 of them a synthesis that pooled it.
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Who cites it
15 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Evidence-based framework for identifying opioid use disorder in administrative data: A systematic review and methodological development study.Pain medicine (Malden, Mass.) · 2026Pooled it
- Comparison of statewide substance use disorder prevalence estimates from survey data vs electronic health record data.Drug and alcohol dependence · 2026Article
- Implementation of an opioid use disorder (OUD) machine-learning phenotype in real-time for the ADAPT clinical trial.PLOS digital health · 2026Article
- Development and validation of a near-comprehensive RxNorm valueset of opioid medications.JAMIA open · 2026Article
- Opioid Use Disorder and Treatment Uptake Among Patients With Rheumatic Conditions in the All of Us Research Program: A Descriptive Analysis.Arthritis care & research · 2026Article
- Article
- Implementation of an Opioid Use Disorder (OUD) Machine-Learning Phenotype in Real-Time for the ADAPT Project.medRxiv : the preprint server for health sciences · 2025Article
- Predicting treatment retention in medication for opioid use disorder: a machine learning approach using NLP and LLM-derived clinical features.Journal of the American Medical Informatics Association : JAMIA · 2025Article
- Opioid prescription patterns in long-term disease-free cancer survivors: a retrospective analysis at a single NCI-designated comprehensive cancer center.Supportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer · 2025Observational
- Associations of community socioeconomic factors and opioid use disorder across an urban-to-rural spectrum in Pennsylvania: an electronic health record-based case-control study.Social psychiatry and psychiatric epidemiology · 2025Article
- Implementation of screening and assessment tools for diagnosing opioid use disorder: A systematic review.Drug and alcohol dependence reports · 2025Review
- Improving diagnosis-based quality measures: an application of machine learning to the prediction of substance use disorder among outpatients.BMJ open quality · 2025Article
- A Review of Leveraging Artificial Intelligence to Predict Persistent Postoperative Opioid Use and Opioid Use Disorder and its Ethical Considerations.Current pain and headache reports · 2025Review
- Opioid analgesics for chronic noncancer pain in patients prescribed opioid agonist therapy or with opioid use disorder: A systematic review.Canadian journal of pain = Revue canadienne de la douleur · 2025Review
- Developing a Framework to Infer Opioid Use Disorder Severity From Clinical Notes to Inform Natural Language Processing Methods: Characterization Study.JMIR mental health · 2024Article
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
backgroundWe 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.
methodsUsing electronic diagnosis grouper text from EHRs of ~1 million patients (2012-2020), we created indicators of OUD-with "tiers" 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.
resultsIn 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. 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. 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.
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