Developing a Model of the Restorative Approaches Veteran Family Service
Abstract: The negative impact of mental health illness on families is well known. Although the high incidence of mental health problems, especially PTSD, amongst military Veterans is recognised there is little systematic support for their families. TGP are a Welsh Charity which supports vulnerable and marginalised children, young people and families in Wales. TGP are closely associated with use of a Restorative Approach: a philosophy and practice drawn from Restorative Justice that is based on the shared set of democratic values such as honesty, trust, fairness, inclusion and collaboration. A Restorative Approach has been adopted in varied service settings including education and welfare, with encouraging signs of improved relationships and reduction in problems such as aggression and bullying. Recent research exploring use of a Restorative Approach in family support systems has found signs of better, more effective service provision and higher levels of family communication and reduced family conflict. TGP are seeking to provide a family support service for the families of Veterans with PTSD. To do so they are working with Veterans NHS Wales, a NHS service providing support for Welsh veterans with PTSD. The ‘Restorative Approach for Veteran Families Service’ seeks to work with families and Veterans alongside the VNHSW service with the intent of improving family relationships and dynamics and so benefitting the mental health of all family members including the Veteran. This research is evaluating the project over the first three years of development and implementation with the aim of identifying and addressing project facilitators and barriers and thereby producing the best model of service implementation.
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.