Restore and Rebuild (R & R) - a feasibility pilot study of a co-designed intervention for moral injury-related mental health difficulties
Abstract: Background: Moral injury can significantly negatively impact mental health, but currently no validated treatment for moral injury-related mental health difficulties exists in a UK context. This study aimed to examine whether the Restore and Rebuild (R&R) treatment was feasible to deliver, acceptable and well tolerated by UK military veterans with moral injury related mental health difficulties. Method: The R&R treatment was delivered to 20 patients who reported distress related to exposure to a morally injurious event(s) during military service. R&R is a 20-session psychotherapy with key themes of processing the event, self compassion, connecting with others and core values. Treatment was delivered online, weekly, one-to-one by a single therapist. Qualitative interviews with patients and the therapist who delivered R&R were conducted to explore acceptability and analysed using thematic analysis. Results: Following treatment, patients experienced a significant reduction in symptoms of post-traumatic stress disorder, depression, alcohol misuse and moral injury related distress. R&R was found to be well tolerated by patients and improved their perceived wellbeing. Conclusions: These results provide preliminary evidence that veterans struggling with moral injury related mental ill health can benefit from R&R treatment.
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.