Opioid use and opioid use disorder in mono and dual-system users of veteran affairs medical centers

Exploring the Utilization of Synthetic Data in Unsupervised Clustering for Opioid Misuse Analysis

Privacy and security restrictions on medical data pose challenges to collaborative research, making synthetic data an increasingly attractive solution. Recent advancements in Generative AI technologies, like GAN models, have improved synthetic data generation. This study investigates the use of synthetic data in clustering models for opioid misuse analysis, generating a dataset that replicates real-world data from 2017 to 2019, including demographics and diagnosis codes. By maintaining patient privacy, we enable comprehensive analysis without compromising security. We developed unsupervised clustering models to identify opioid misuse patterns and assessed the effectiveness of synthetic data across four scenarios: training on real dataset and testing on real dataset (TRTR), training on real dataset and testing on synthetic dataset (TRTS), TSTR, and TSTS. Results demonstrate that synthetic data can replicate real data distributions and clustering characteristics as a training set, offering significant potential for collaborative model development and optimization without exposing privacy or security risks.

A deep learning analysis for dual healthcare system users and risk of opioid use disorder

The opioid crisis has disproportionately affected U.S. veterans, leading the Veterans Health Administration to implement opioid prescribing guidelines. Veterans who receive care from both VA and non-VA providers-known as dual-system users-have an increased risk of Opioid Use Disorder (OUD). The interaction between dual-system use and demographic and clinical factors, however, has not been previously explored. We conducted a retrospective study of 856,299 patient instances from the Washington DC and Baltimore VA Medical Centers (2012-2019), using a deep neural network (DNN) and explainable Artificial Intelligence to examine the impact of dual-system use on OUD and how demographic and clinical factors interact with it. Of the cohort, 146,688(17%) had OUD, determined through Natural Language Processing of clinical notes and ICD-9/10 diagnoses. The DNN model, with a 78% area under the curve, confirmed that dual-system use is a risk factor for OUD, along with prior opioid use or other substance use. Interestingly, a history of other drug use interacted negatively with dual-system use regarding OUD risk. In contrast, older age was associated with a lower risk of OUD but interacted positively with dual-system use. These findings suggest that within the dual-system users, patients with certain risk profiles warrant special attention.

Guideline concordant opioid therapy in Veterans receiving VA and community care

Guideline concordant opioid therapy is a key part of the concerted effort to address the opioid crisis in the United States. The study aimed to compare the rates of guideline concordant care between veterans who solely used VA services (mono users) and veterans who used both VA services and community care (dual-system users). We used electronic health record data from the Washington DC and Baltimore VA Medical Centers from 2015 to 2019. We provided descriptive statistics as well as generalized estimating equations models to find associations between mono vs. dual-system users and each guideline outcome, controlling for demographic factors and comorbid conditions. The study found that overall rates of guideline concordant care were high in both mono and dual-system users with over 90% adherence rates for the majority of recommendations. However, there were variations in adherence to specific guidelines, with urine drug screening at initiation being the least commonly followed recommendation (8.9% of mono-user opioid initiators and 11.2% of dual-user initiators). This study also found that there was no consistent pattern of higher guideline adherence in mono vs. dual-system users but did show that through the course of this study (2015-2019) overall rates of guideline concordance increased. Future research will explore additional guideline recommendations and potential coordination issues among dual-system users.

A Comparison of Veterans with Problematic Opioid Use Identified through Natural Language Processing of Clinical Notes versus Using Diagnostic Codes

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.