Lives in Transition: returning to civilian life with a physical injury or condition. Final report.
Executive Summary: This report presents the final findings of a project funded by Forces in Mind Trust (FiMT) called Understanding the transition to civilian life for ex-service personnel with physical conditions as a direct result of service or acquired whilst in service. Running from April 2019to October 2021, this project was the #rst substantive qualitative longitudinal research (QLR) to explore how service leavers experience the transition to civilian life when they have left the Armed Forces with a physical injury or condition. Despite the prevalence of physical conditions and injuries as a factor in leaving service, there is limited research that provides a holistic view of the experiences of this cohort. Our project was therefore structured to provide an exploration of the various stages of people’s journeys from injury/condition within service through to accessing civilian systems, support and employment.
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.