Development of an intervention for moral injury-related mental health difficulties in UK military veterans: a feasibility pilot study protocol
Abstract: Experiencing potentially morally injurious events (PMIEs) has been found to be significantly associated with poor mental health outcomes in military personnel/veterans. Currently, no manualised treatment for moral injury-related mental health difficulties for UK veterans exists. This article describes the design, methods and expected data collection of the Restore & Rebuild (R&R) protocol, which aims to develop procedures to treat moral injury related mental ill health informed by a codesign approach. Methods: The study consists of three main stages. First, a systematic review will be conducted to understand the best treatments for the symptoms central to moral injury-related mental ill health (stage 1). Then the R&R manual will be co-designed with the support of UK veteran participants with lived experience of PMIEs as well as key stakeholders who have experience of supporting moral injury affected individuals (stage 2). The final stage of this study is to conduct a pilot study to explore the feasibility and acceptability of the R&R manual (stage 3). Qualitative data will be analysed using thematic analysis. This study was approved by the King's College London's Research Ethics Committee (HR-20/21-20850). The findings will be disseminated in several ways, including publication in academic journals, a free training event and presentation at conferences. By providing information on veteran, stakeholder and clinician experiences, we anticipate that the findings will not only inform the development of an acceptable evidence-based approach for treating moral injury-related mental health problems, but they may also help to inform broader approaches to providing care to trauma exposed military veterans.
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.