The Transition Mapping Study -Understanding the Transition Process for Service Personnel Returning to Civilian Life
Abstract: Since the Armed Forces Covenant was published in 2000, there has been increased interest in the process of helping Service people transition from the Armed Forces into civilian life. In 2006 this process was formalised when the Ministry of Defence published its Strategy for Veterans. The Forces in Mind Trust (FiMT), which has been set up to improve the transition process, commissioned The Futures Company to review existing research, to understand how the transition process currently works, how it is viewed by stakeholders and by recent Service leavers, to develop a model that quantified the costs of poor transition to the UK as a whole, and to make recommendations on how to reduce the number of poor transitions. The research focussed on fulltime personnel, not reservists. Transitioning from military service into civilian life is inevitable for almost all Armed Forces personnel; only a tiny proportion proceeds through to a full career. Most transitions are successful. One of the reasons for this is because of the resources invested in them by the Armed Forces. At the same time, a “good transition” is undefined. For the purposes of this project we developed with help from stakeholders the following definition: “A good transition is one that enables ex-Service personnel to be sufficiently resilient to adapt successfully to civilian life, both now and in the future. This resilience includes financial, psychological, and emotional resilience, and encompasses the ex-Service person and their immediate families.”
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.