Building Community Capacity to Care for Military and Veteran Families: The Star Behavioral Health Providers Program
Abstract: Service members, veterans, and their families frequently have difficulties finding trained behavioral health providers who have knowledge of military culture and issues specific to the military population. This paper documents the design, implementation, effectiveness, and proximal outcomes of the Star Behavioral Health Providers training program (SBHP). We created SBHP as a dissemination effort in response to elevated levels of mental health problems among community-dwelling military and veteran families (CDMVF), limitations in provider capacity in the Departments of Defense (DoD) and Veterans Affairs (VA), and uneven preparation of civilian providers to serve military and veteran families. The goals of the initiative were to: Improve the preparation of community-based professionals to work with CDMVF. Increase providers’ use of evidence-informed information and practices. Strengthen the behavioral health infrastructure for treating CDMVF. The program provides military-specific training to community-based behavioral health providers and provides a mechanism for those seeking such services to find trained providers. Evaluation data, though limited by the lack of comparison or control groups, provided correlational evidence consistent with each program goal.
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.