Providing recovery support to wounded, injured, and sick UK military personnel throughout the COVID-19 pandemic
Abstract: Health precautions implemented by the United Kingdom (UK) government to limit the spread of the Coronavirus Disease 2019 (COVID-19) led to the closure of many well-being support services in 2020. This created a need to re-think how impactful recovery support courses can be provided. One such service was that of the five-day Multi Activity Course (MAC) which was redesigned in accordance with national health guidelines to allow continued access for Wounded, Injured and Sick (WIS) military personnel to the service; the positive impacts of which are well established. This study investigated the influence of the newly developed Reduced numbers MAC (R-MAC) on the WIS participants lives during and for 12 months after attending. The R-MAC led to comparable impacts for participants well-being, at a time in which people’s mental well-being was often being adversely affected. The positive mental well-being of the 261 participants improved by 33% throughout the course and remained 14% higher for the 37 participants who provided data six months after attending. Key facets of the experience that were most impactful for the participants were (i) shared experience with other veterans, (ii) discussing issues in a safe environment while receiving support from the staff and (iii) developing knowledge around self-help/personal development. Adapting to the challenging circumstances and developing the R-MAC mitigated against the already adverse impact of the COVID-19 pandemic for the WIS participants.
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.