ArticleJMIR medical informatics2019
Identifying Opioid Use Disorder in the Emergency Department: Multi-System Electronic Health Record-Based Computable Phenotype Derivation and Validation Study.
Article in JMIR medical informatics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 papers, 2 of them syntheses that pooled it.
What it found
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
33 citing papers in PubMed, 2 syntheses or guidelines 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
- 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
- Adoption of Emergency Department-Initiated Buprenorphine for Patients With Opioid Use Disorder: Secondary Analysis of a Cluster Randomized Trial.JAMA network open · 2023Trial
- User centered clinical decision support to implement initiation of buprenorphine for opioid use disorder in the emergency department: EMBED pragmatic cluster randomized controlled trial.BMJ (Clinical research ed.) · 2022Trial
- Implementation of an opioid use disorder (OUD) machine-learning phenotype in real-time for the ADAPT clinical trial.PLOS digital health · 2026Article
- Characterizing emergency clinician engagement with social drivers of health data among patients with opioid use disorder.Journal of the American Medical Informatics Association : JAMIA · 2026Article
- Computable Structured Phenotype Versus Large Language Model Identification of Opioid Use Disorder Using Electronic Health Record Data.Annals of emergency medicine · 2026Article
- Comparing computable structured phenotype- versus large language model-identification of opioid use disorder using electronic health record data.medRxiv : the preprint server for health sciences · 2026Article
- Article
- Characterizing emergency clinician engagement with social drivers of health data among patients with opioid use disorder.medRxiv : the preprint server for health sciences · 2025Article
- 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
- A Measurement Science Framework to Optimize CDS for Opioid Use Disorder Treatment in the ED.Applied clinical informatics · 2025Article
- Equivalence of electronic health record data for measuring hypertension prevalence: a retrospective comparison to BRFSS with data from two Indiana health systems, 2021.BMC public health · 2025Article
- Article
- Effect of emergency department opioid prescribing on health outcomes.CMAJ : Canadian Medical Association journal = journal de l'Association medicale canadienne · 2025Article
- Development and validation of computable social phenotypes for health-related social needs.JAMIA open · 2025Article
- A Clinical Decision Support System for Addressing Health-Related Social Needs in Emergency Department: Defining End User Needs and Preferences.Applied clinical informatics · 2024Article
- Fentanyl-positive urine drug screens in the emergency department: Association with intentional opioid misuse and racial disparities.Drug and alcohol dependence reports · 2024Article
- Variability in Practice of Buprenorphine Treatment by Emergency Department Operational Characteristics.The western journal of emergency medicine · 2024Article
- Enhanced phenotypes for identifying opioid overdose in emergency department visit electronic health record data.JAMIA open · 2023Article
Corrections and comments
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Authors and funding
15 authors.
Funding
Abstract
backgroundDeploying accurate computable phenotypes in pragmatic trials requires a trade-off between precise and clinically sensical variable selection. In particular, evaluating the medical encounter to assess a pattern leading to clinically significant impairment or distress indicative of disease is a difficult modeling challenge for the emergency department.
objectiveThis study aimed to derive and validate an electronic health record-based computable phenotype to identify emergency department patients with opioid use disorder using physician chart review as a reference standard.
methodsA two-algorithm computable phenotype was developed and evaluated using structured clinical data across 13 emergency departments in two large health care systems. Algorithm 1 combined clinician and billing codes. Algorithm 2 used chief complaint structured data suggestive of opioid use disorder. To evaluate the algorithms in both internal and external validation phases, two emergency medicine physicians, with a third acting as adjudicator, reviewed a pragmatic sample of 231 charts: 125 internal validation (75 positive and 50 negative), 106 external validation (56 positive and 50 negative).
resultsCohen kappa, measuring agreement between reviewers, for the internal and external validation cohorts was 0.95 and 0.93, respectively. In the internal validation phase, Algorithm 1 had a positive predictive value (PPV) of 0.96 (95% CI 0.863-0.995) and a negative predictive value (NPV) of 0.98 (95% CI 0.893-0.999), and Algorithm 2 had a PPV of 0.8 (95% CI 0.593-0.932) and an NPV of 1.0 (one-sided 97.5% CI 0.863-1). In the external validation phase, the phenotype had a PPV of 0.95 (95% CI 0.851-0.989) and an NPV of 0.92 (95% CI 0.807-0.978).
conclusionsThis phenotype detected emergency department patients with opioid use disorder with high predictive values and reliability. Its algorithms were transportable across health care systems and have potential value for both clinical and research purposes.
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Registered trials
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.