An evaluation and critical analysis of the impact of the Aged Veterans Fund
Abstract: There has been research on the numbers and needs of an ageing society yet, relatively little is known about the specific needs of older veterans, and the effectiveness of services specifically developed to meet these needs. In 2016 and 2017, the Armed Forces Covenant Fund Trust funded invested £30 million to the Aged Veterans Fund (AVF) programme. This consisted of 19 portfolio projects to support health, wellbeing, and social care needs for older veterans (born before 1st January 1950) and their families. This report explores the impact of the AVF, with the intent of informing service providers, stakeholders and policy makers, of the lessons learned and the next steps required for the support of older veterans. A retrospective evaluation focused on both the impact of the processes adopted by the programmes, and the outcomes achieved, was commissioned. Qualitative analysis was performed on 78 eligible source documents, from which 10 recurrent themes were identified.
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.