Describing the feasibility of using case management in a specialist forensic substance misuse intervention for UK veterans: a case study
Abstract: Veterans with mental health problems are a high-risk group for substance misuse difficulties and are over-represented in forensic settings. Yet, there are few substance misuse services available for this population. Evidence suggests that case management can provide effective interventions for veterans with substance misuse problems. However, there is little research to show its effectiveness in the UK. The present study reported on the implementation and preliminary outcomes of the Veterans Forensic Substance Misuses Service (VFSMS), piloted within a prison setting, to demonstrate the feasibility of the service. The VFSMS operated in four stages: Assessing needs, developing case management plans, providing bespoke support and developing discharge plans. Case studies were used to demonstrate this process, with measures of alcohol use and recovery showing outcomes for each case. Findings from three case studies suggested that case management was a feasible approach, with a range of interventions being used, including substance misuse and mental health services, plus housing and employment services. Outcome measures suggested that alcohol and substance misuse recovery improved following the VFSMS intervention. While the scope of the findings is limited, they suggested that case management is a feasible substance misuse intervention, with preliminary findings showing improvements in substance misuse outcomes.
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.