Final Report - Project Nova: A Pilot Study to Support Veterans in the Criminal Justice System
Abstract: Project Nova was implemented to consider and address the needs of veteran offenders following a referral after arrest by the Police. The pilot, delivered in Norfolk and Suffolk, lasted for 12 months from July 2014 to July 2015. Over this 12-month period, 145 veterans were referred to the project. However, over half of this number were not traceable because of insufficient contact details. 34 veteran offenders fully engaged with the pilot project during the evaluation period. The data from these participants was collected by the Project Nova operational team, anonymised and shared with Anglia Ruskin University for further consideration and analysis. Validated questionnaires were circulated to the participants at baseline, measuring social adjustment, well-being, alcohol misuse and Post-Traumatic Stress Disorder (PTSD). Initial findings indicated difficulties with all the issues measured for many of the veterans. Just under a third of these participants (n=10) took part in semi-structured interviews. These interviews required ethical approval which was sought prior to conducting interviews with participants from the Project Nova team, a Police Officer from the Norfolk and Suffolk Constabulary, and the 10 offenders. The interviews were conducted by the same researcher to ensure continuity.
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.