Assessment for the U.S. Army Comprehensive Soldier Fitness program: The Global Assessment Tool
Abstract: Psychology and the U.S. military have a long history of collaboration. The U.S. Army Comprehensive Soldier Fitness (CSF) program aims to measure the psychosocial strengths and assets of soldiers as well as their problems, to identify those in need of basic training in a given domain as well as those who would benefit from advanced training, and then to provide that training. The goals of the CSF program include the promotion of well-being as well as the prevention of problems. Assessment is the linchpin of the CSF program, and the Global Assessment Tool (GAT) is a self-report survey that measures psychosocial fitness in emotional, social, family, and spiritual domains. We review the history of psychological assessment in the military and the lessons taught by this history. Then we describe the process by which the GAT was developed and evaluated. We conclude with a discussion of pending next steps in the development and use of the GAT.
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.