SynthesisJournal of the American Medical Informatics Association : JAMIA2017
Automatable algorithms to identify nonmedical opioid use using electronic data: a systematic review.
Synthesis in Journal of the American Medical Informatics Association : JAMIA, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers, 3 of them syntheses that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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
25 citing papers in PubMed, 3 syntheses or guidelines pooled it, 46 citations in OpenAlex.
- The validity of electronic health data for measuring smoking status: a systematic review and meta-analysis.BMC medical informatics and decision making · 2024Pooled it
- A systematic review of natural language processing and text mining of symptoms from electronic patient-authored text data.International journal of medical informatics · 2019Pooled it
- Natural language processing of symptoms documented in free-text narratives of electronic health records: a systematic review.Journal of the American Medical Informatics Association : JAMIA · 2019Pooled it
- Predicting overdose among individuals prescribed opioids using routinely collected healthcare utilization data.PloS one · 2020Trial
- Developing and Validating a Machine Learning Algorithm to Predict the Risk of Incident Opioid Use Disorder Among OneFlorida+ Patients: Prognostic Modeling Study.Journal of medical Internet research · 2026Article
- A Machine Learning Application to Classify Patients at Differing Levels of Risk of Opioid Use Disorder: Clinician-Based Validation Study.JMIR medical informatics · 2024Article
- Developing a Framework to Infer Opioid Use Disorder Severity From Clinical Notes to Inform Natural Language Processing Methods: Characterization Study.JMIR mental health · 2024Article
- Identifying patients with opioid use disorder using International Classification of Diseases (ICD) codes: Challenges and opportunities.Addiction (Abingdon, England) · 2024Observational
- Identification of opioid use disorder using electronic health records: Beyond diagnostic codes.Drug and alcohol dependence · 2023Article
- Algorithms to Identify Nonmedical Opioid Use.Current pain and headache reports · 2023Review
- Identifying High-Risk Comorbidities Associated with Opioid Use Patterns Using Electronic Health Record Prescription Data.Complex psychiatry · 2022Article
- Classifying Characteristics of Opioid Use Disorder From Hospital Discharge Summaries Using Natural Language Processing.Frontiers in public health · 2022Article
- A narrative review on the validity of electronic health record-based research in epidemiology.BMC medical research methodology · 2021Review
- External validation of an opioid misuse machine learning classifier in hospitalized adult patients.Addiction science & clinical practice · 2021Observational
- A spatiotemporal analysis of opioid poisoning mortality in Ohio from 2010 to 2016.Scientific reports · 2021Article
- Article
- Assessment of Probable Opioid Use Disorder Using Electronic Health Record Documentation.JAMA network open · 2020Article
- Publicly available machine learning models for identifying opioid misuse from the clinical notes of hospitalized patients.BMC medical informatics and decision making · 2020Observational
- Strengths and weaknesses of existing data sources to support research to address the opioids crisis.Preventive medicine reports · 2020Review
- Measuring problem prescription opioid use among patients receiving long-term opioid analgesic treatment: development and evaluation of an algorithm for use in EHR and claims data.Journal of drug assessment · 2020Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveImproved methods to identify nonmedical opioid use can help direct health care resources to individuals who need them. Automated algorithms that use large databases of electronic health care claims or records for surveillance are a potential means to achieve this goal. In this systematic review, we reviewed the utility, attempts at validation, and application of such algorithms to detect nonmedical opioid use. MATERIALS AND
methodsWe searched PubMed and Embase for articles describing automatable algorithms that used electronic health care claims or records to identify patients or prescribers with likely nonmedical opioid use. We assessed algorithm development, validation, and performance characteristics and the settings where they were applied. Study variability precluded a meta-analysis.
resultsOf 15 included algorithms, 10 targeted patients, 2 targeted providers, 2 targeted both, and 1 identified medications with high abuse potential. Most patient-focused algorithms (67%) used prescription drug claims and/or medical claims, with diagnosis codes of substance abuse and/or dependence as the reference standard. Eleven algorithms were developed via regression modeling. Four used natural language processing, data mining, audit analysis, or factor analysis. DISCUSSION: Automated algorithms can facilitate population-level surveillance. However, there is no true gold standard for determining nonmedical opioid use. Users must recognize the implications of identifying false positives and, conversely, false negatives. Few algorithms have been applied in real-world settings.
conclusionAutomated algorithms may facilitate identification of patients and/or providers most likely to need more intensive screening and/or intervention for nonmedical opioid use. Additional implementation research in real-world settings would clarify their utility.
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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.