Veteran-centered Barriers to VA Mental Healthcare Services use
Abstract: Some veterans face multiple barriers to VA mental healthcare service use. However, there is limited understanding of how veterans’ experiences and meaning systems shape their perceptions of barriers to VA mental health service use. In 2015, a participatory, mixed-methods project was initiated to elicit veteran-centered barriers to using mental healthcare services among a diverse sample of US rural and urban veterans. We sought to identify veteran-centric barriers to mental healthcare to increase initial engagement and continuation with VA mental healthcare services. Cultural Domain Analysis, incorporated in a mixed methods approach, generated a cognitive map of veterans’ barriers to care. The method involved: 1) free lists of barriers categorized through participant pile sorting; 2) multi-dimensional scaling and cluster analysis for item clusters in spatial dimensions; and 3) participant review, explanation, and interpretation for dimensions of the cultural domain. Item relations were synthesized within and across domain dimensions to contextualize mental health help-seeking behavior. Participants determined five dimensions of barriers to VA mental healthcare services: concern about what others think; financial, personal, and physical obstacles; confidence in the VA healthcare system; navigating VA benefits and healthcare services; and privacy, security, and abuse of services. These findings demonstrate the value of participatory methods in eliciting meaningful cultural insight into barriers of mental health utilization informed by military veteran culture. They also reinforce the importance of collaborations between the VA and Department of Defense to address the role of military institutional norms and stigmatizing attitudes in veterans’ mental health-seeking behaviors.
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.