The social prescribing of psychosocial interventions in the treatment of addictions and substance use disorders with military veterans: a reclamation of identity and belonging
Abstract: Social prescribing is a way of connecting individuals to a source of support within the community to help improve their health and well-being. Social prescribing programmes are being widely promoted within the United Kingdom (UK) and United States as non-pharmaceutical interventions for those living with addiction and substance misuse needs. These needs have been exasperated by the recent COVID-19 pandemic and global economic crisis, with emerging research indicating short-term and long-term detrimental effects on physical and mental health due to substance misuse and addictions. Psychosocial interventions utilize psychological or social factors rather than an overreliance on biological interventions to treat the health impacts of mental illnesses such as addictions and substance use disorder. In this paper, I will discuss the associated determinants of addictions and substance for the military veteran population, as well as how the social prescribing of psychosocial interventions could be used to reaffirm participant’s identity and enhance their sense of belonging for military veterans, using a real-world example in Wales, UK.
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.