A Comparison of Veterans with Problematic Opioid Use Identified through Natural Language Processing of Clinical Notes versus Using Diagnostic Codes
Document Type
Journal Article
Publication Date
4-6-2024
Journal
Healthcare (Basel, Switzerland)
Volume
12
Issue
7
DOI
10.3390/healthcare12070799
Keywords
Veterans; care use; comorbidities; demographic data; machine learning; natural language processing; opioid misuse documentation
Abstract
Opioid use disorder is known to be under-coded as a diagnosis, yet problematic opioid use can be documented in clinical notes, which are included in electronic health records. We sought to identify problematic opioid use from a full range of clinical notes and compare the demographic and clinical characteristics of patients identified as having problematic opioid use exclusively in clinical notes to patients documented through ICD opioid use disorder diagnostic codes. We developed and applied a natural language processing (NLP) tool that combines rule-based pattern analysis and a trained support vector machine to the clinical notes of a patient cohort (n = 222,371) from two Veteran Affairs service regions to identify patients with problematic opioid use. We also used a set of ICD diagnostic codes to identify patients with opioid use disorder from the same cohort. The NLP tool achieved 96.6% specificity, 90.4% precision/PPV, 88.4% sensitivity/recall, and 94.4% accuracy on unseen test data. NLP exclusively identified 57,331 patients; 6997 patients had positive ICD code identifications. Patients exclusively identified through NLP were more likely to be women. Those identified through ICD codes were more likely to be male, younger, have concurrent benzodiazepine prescriptions, more comorbidities, and more care encounters, and were less likely to be married. Patients in both these groups had substantially elevated comorbidity levels compared with patients not documented through either method as experiencing problematic opioid use. Clinicians may be reluctant to code for opioid use disorder. It is therefore incumbent on the healthcare team to search for documentation of opioid concerns within clinical notes.
APA Citation
Workman, Terri Elizabeth; Kupersmith, Joel; Ma, Phillip; Spevak, Christopher; Sandbrink, Friedhelm; Cheng, Yan; and Zeng-Treitler, Qing, "A Comparison of Veterans with Problematic Opioid Use Identified through Natural Language Processing of Clinical Notes versus Using Diagnostic Codes" (2024). GW Authored Works. Paper 4757.
https://hsrc.himmelfarb.gwu.edu/gwhpubs/4757
Department
Clinical Research and Leadership