Veterans' experiences of moral injury, treatment and recommendations for future support
Abstract: Moral injury (MI) significantly impacts the lives of many UK military veterans however, there is a lack of manualised treatment to address the needs of this population. To develop future treatments that are acceptable and well tolerated, veterans should be consulted on their experiences of existing psychological treatments and suggestions for future treatments. 10 UK military veterans were interviewed about their experiences of receiving treatment for psychological difficulties after MI, and beliefs about core components of future treatments. Thematic analysis of these interviews were conducted. 2 superordinate themes were identified: experiences of previous mental health treatment and perceptions of the proposed treatments. Reflections on cognitive behavioural therapy were mixed, with some describing that it did not ameliorate their guilt or shame. In future treatments, focusing on values, using written letters and including therapy sessions with close companions were considered beneficial. Veterans reported that a strong rapport with therapist was key for MI treatment. Findings provide a useful account of how current post-trauma treatments may be experienced by patients with MI. Although limited by sample size, the results highlight therapeutic approaches that may be helpful in future and provide important considerations for therapists treating MI.
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.