Are Communities Ready? Assessing Providers’ Practices, Attitudes, and Knowledge About Military Personnel
Abstract: One potential barrier to helping returning military personnel and their families is a lack of community providers skilled to help these groups. Although capacity and competence have expanded within the Department of Defense (DoD) and the Veterans Health Administration (VHA), it is unknown if community agencies have the interest, capacity, and competence to help service members, veterans, and their families postdeployment. This study used an online survey to examine the knowledge, common practices, attitudes, and training needs of community mental health providers, in order to determine if needs are adequately addressed. Assessment and treatment practices with veterans and service members varied greatly in community practices. Additional training opportunities are needed, particularly for helping military personnel with traumatic brain injuries and providing evidence-based practice. Furthermore, clinicians in the community need to systematically assess new clients for military service.
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.