ArticlePharmacoepidemiology and drug safety2023
Development and validation of an electronic health records-based opioid use disorder algorithm by expert clinical adjudication among patients with prescribed opioids.
Article in Pharmacoepidemiology and drug safety, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 2 of them syntheses that pooled it.
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Who cites it
13 citing papers in PubMed, 2 syntheses or guidelines pooled it, 17 citations in OpenAlex.
- Predictive Models for Identifying Adult Patients at High Risk of Developing Opioid-Related Harms: a Systematic Review.Drug safety · 2025Pooled it
- Sensitivity and specificity of International Classification of Diseases algorithms (ICD-9 and ICD-10) used to identify opioid-related overdose cases: A systematic review and an example of estimation using Bayesian latent class models in the absence of gold standards.Canadian journal of public health = Revue canadienne de sante publique · 2024Pooled it
- Natural Language Processing Identification of Nonprescribed Fentanyl Use in Electronic Health Records: Algorithm Development and Validation Study.Journal of medical Internet research · 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 evaluation of machine learning models for the detection of emergency department patients with opioid misuse from clinical notes.JAMIA open · 2026Article
- Estimating community-level prevalence of opioid use disorder: Extrapolating from Medicaid claims data and other publicly available data sources in Ohio, USA.Addiction (Abingdon, England) · 2026Article
- 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
- Diagnosis and coding of opioid misuse: a systematic scoping review and implementation framework.Pain medicine (Malden, Mass.) · 2025Article
- Abuse-deterrent formulations and opioid-related harms in North Carolina, 2010-2018.American journal of epidemiology · 2025Article
- Performance of International Classification of Disease-10 codes in detecting emergency department patients with opioid misuse.Addiction (Abingdon, England) · 2024Article
- Prenatal opioid use as a predictor of postpartum suicide attempts among reproductive-age women enrolled in Oregon Medicaid.BMC women's health · 2024Article
- American clusters: using machine learning to understand health and health care disparities in the United States.Health affairs scholar · 2024Article
- Association of Opioid Dose Reduction With Opioid Overdose and Opioid Use Disorder Among Patients Receiving High-Dose, Long-term Opioid Therapy in North Carolina.JAMA network open · 2022Article
Corrections and comments
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Authors and funding
15 authors at 5 institutions in 1 country.
Funding
Abstract
backgroundIn the US, over 200 lives are lost from opioid overdoses each day. Accurate and prompt diagnosis of opioid use disorders (OUD) may help prevent overdose deaths. However, international classification of disease (ICD) codes for OUD are known to underestimate prevalence, and their specificity and sensitivity are unknown. We developed and validated algorithms to identify OUD in electronic health records (EHR) and examined the validity of OUD ICD codes.
methodsThrough four iterations, we developed EHR-based OUD identification algorithms among patients who were prescribed opioids from 2014 to 2017. The algorithms and OUD ICD codes were validated against 169 independent "gold standard" EHR chart reviews conducted by an expert adjudication panel across four healthcare systems. After using 2014-2020 EHR for validating iteration 1, the experts were advised to use 2014-2017 EHR thereafter.
resultsOf the 169 EHR charts, 81 (48%) were reviewed by more than one expert and exhibited 85% expert agreement. The experts identified 54 OUD cases. The experts endorsed all 11 OUD criteria from the Diagnostic and Statistical Manual of Mental Disorders-5, including craving (72%), tolerance (65%), withdrawal (56%), and recurrent use in physically hazardous conditions (50%). The OUD ICD codes had 10% sensitivity and 99% specificity, underscoring large underestimation. In comparison our algorithm identified OUD with 23% sensitivity and 98% specificity. CONCLUSIONS AND RELEVANCE: This is the first study to estimate the validity of OUD ICD codes and develop validated EHR-based OUD identification algorithms. This work will inform future research on early intervention and prevention of OUD.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.