Knowledge Assessment of Military Personnel, Veterans, and Family Taking Dietary Supplements
Abstract: Dietary supplements (DS) pose many side effects and multiple interactions with perioperative medications, which may increase surgical morbidity and mortality. Descriptive empirical data are essentially nonexistent related to DS consumption and patient knowledge of DS. The aims of this study were to investigate the prevalence of use, type of supplements used, and the knowledge base among military beneficiaries and veterans consuming DS during the preoperative period. This descriptive cross-sectional study solicited data from 2,623 volunteer, preoperative patients at 6 different military medical centers throughout the United States. Of the 2,623 participants, 847 (32.3%) reported taking at least 1 DS. Relevant to the surgical population, 154 (18.1%) of participants reported consuming DS that are associated with an increased risk of bleeding. Importantly, we found that 89.7% of patients taking DS were not aware of any potential side effects, and 97.1% lacked knowledge regarding any potential medication interactions between the supplement consumed and their prescribed medications. This vast knowledge gap could have deleterious effects on surgical outcomes. An increase in DS research is needed, and patient education should be incorporated routinely during preoperative assessments provided by military and Veterans Affairs healthcare facilities.
Abstract: Novel and automated means of opioid use and relapse risk detection are needed. Unstructured electronic medical record data, including written progress notes, can be mined for clinically relevant information, including the presence of substance use and relapse-critical markers of risk and recovery from opioid use disorder (OUD). In this study, we used natural language processing (NLP) to automate the extraction of opioid relapses, and the timing of these occurrences, from veteran patients' electronic medical record. We then demonstrated the utility of our NLP tool via analysis of pre-/post-COVID-19 opioid relapse trends among veterans with OUD. For this demonstration, we analyzed data from 107,606 veterans OUD enrolled in Veterans Health Administration, comparing a pandemic-exposed cohort (n = 53,803; January 2019-March 2021) to a matched prepandemic cohort (n = 53,803; October 2017-December 2019). The recall of our NLP tool was 75% and our precision was 94%, demonstrating moderate sensitivity and excellent specificity. Using the NLP tool, we found that the odds of opioid relapse postpandemic onset were proportionally higher compared to prepandemic trends, despite patients having fewer mental health encounters from which to derive instances of relapse postpandemic onset. In this research application of the tool, and as hypothesized, we found that opioid relapse risk was elevated postpandemic. The application of NLP Methods: to identify and monitor relapse risk holds promise for future surveillance, risk prevention, and clinical outcome research.