ArticlePharmacoepidemiology and drug safety2019
Using natural language processing of clinical text to enhance identification of opioid-related overdoses in electronic health records data.
Article in Pharmacoepidemiology and drug safety, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers, 3 of them syntheses that pooled it.
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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.
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Who cites it
27 citing papers in PubMed, 3 syntheses or guidelines pooled it, 33 citations in OpenAlex.
- Improving Clinical Documentation with Artificial Intelligence: A Systematic Review.Perspectives in health information management · 2024Pooled it
- Extracting social determinants of health from electronic health records using natural language processing: a systematic review.Journal of the American Medical Informatics Association : JAMIA · 2021Pooled it
- Can antiepileptic efficacy and epilepsy variables be studied from electronic health records? A review of current approaches.Seizure · 2021Pooled it
- Natural Language Processing Identification of Nonprescribed Fentanyl Use in Electronic Health Records: Algorithm Development and Validation Study.Journal of medical Internet research · 2026Article
- Artificial Intelligence for Opioid Safety Surveillance from Clinical Text: A Clinically Focused Review.Journal of clinical medicine · 2026Review
- Natural Language Processing for Substance Use Disorder Information Extraction: A Systematic Literature Review.Current addiction reports · 2026Review
- Identifying emergency department patients at high risk for opioid overdose using natural language processing and machine learning.Journal of substance use and addiction treatment · 2025Article
- Sensitivity and Specificity of Natural Language Processing Systems for Identification of Hospitalized People Who Use Drugs.Open forum infectious diseases · 2025Article
- Leveraging Natural Language Processing and Machine Learning Methods for Adverse Drug Event Detection in Electronic Health/Medical Records: A Scoping Review.Drug safety · 2025Article
- Detecting Opioid Use Disorder in Health Claims Data With Positive Unlabeled Learning.IEEE journal of biomedical and health informatics · 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
- Identifying Opioid Overdose and Opioid Use Disorder and Related Information from Clinical Narratives Using Large Language Models.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2025Article
- Question-answering system extracts information on injection drug use from clinical notes.Communications medicine · 2024Article
- Health Equity in Clinical Research Informatics.Yearbook of medical informatics · 2023Review
- A deep learning method to detect opioid prescription and opioid use disorder from electronic health records.International journal of medical informatics · 2023Article
- Natural Language Processing and Machine Learning to Identify People Who Inject Drugs in Electronic Health Records.Open forum infectious diseases · 2022Article
- Development and Validation of Machine Models Using Natural Language Processing to Classify Substances Involved in Overdose Deaths.JAMA network open · 2022Article
- Using Natural Language Processing of Clinical Notes to Predict Outcomes of Opioid Treatment Program.Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference · 2022Article
- Comparative Analysis of Patient Distress in Opioid Treatment Programs using Natural Language Processing.Biomedical engineering systems and technologies, international joint conference, BIOSTEC ... revised selected papers. BIOSTEC (Conference) · 2022Article
- Classifying Characteristics of Opioid Use Disorder From Hospital Discharge Summaries Using Natural Language Processing.Frontiers in public health · 2022Article
Corrections and comments
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Authors and funding
8 authors at 4 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeTo enhance automated methods for accurately identifying opioid-related overdoses and classifying types of overdose using electronic health record (EHR) databases.
methodsWe developed a natural language processing (NLP) software application to code clinical text documentation of overdose, including identification of intention for self-harm, substances involved, substance abuse, and error in medication usage. Using datasets balanced with cases of suspected overdose and records of individuals at elevated risk for overdose, we developed and validated the application using Kaiser Permanente Northwest data, then tested portability of the application using Kaiser Permanente Washington data. Datasets were chart-reviewed to provide a gold standard for comparison and evaluation of the automated method.
resultsThe method performed well in identifying overdose (sensitivity = 0.80, specificity = 0.93), intentional overdose (sensitivity = 0.81, specificity = 0.98), and involvement of opioids (excluding heroin, sensitivity = 0.72, specificity = 0.96) and heroin (sensitivity = 0.84, specificity = 1.0). The method performed poorly at identifying adverse drug reactions and overdose due to patient error and fairly at identifying substance abuse in opioid-related unintentional overdose (sensitivity = 0.67, specificity = 0.96). Evaluation using validation datasets yielded significant reductions, in specificity and negative predictive values only, for many classifications mentioned above. However, these measures remained above 0.80, thus, performance observed during development was largely maintained during validation. Similar results were obtained when evaluating portability, although there was a significant reduction in sensitivity for unintentional overdose that was attributed to missing text clinical notes in the database.
conclusionsMethods that process text clinical notes show promise for improving accuracy and fidelity at identifying and classifying overdoses according to type using EHR data.
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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.