An Evaluation of seAp's Military Advocacy Service: Early Findings Report
Abstract: seAps Military Advocacy Service (mAs) came about in recognition of a gap in specialist provision for military veterans and their families. It is a service which recognises the complexity of its clients’ needs, and seeks to provide more intense, specialist support than is available elsewhere. mAs aims to be a more open and flexible service, capable of addressing a wide range of issues. It offers a practical and resilience building model of support, designed to empower individuals who engage with the service to find solutions and deal with their life issues, whatever they may be, in order to help them get their lives back on course. mAs endeavour to ‘walk alongside’ all clients, assisting them in navigating the myriad of agencies and services available to them. seAp strongly believe in the power of its peerdelivered military advocacy model to transform people’s lives, and wish for it to become a statutory service which would be available to all veterans in England. This report is of a service evaluation of mAs.
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.