Study protocol: a hybrid effectiveness-implementation trial of Moral Reconation Therapy in the US Veterans Health Administration
Abstract: Background: Moral Reconation Therapy (MRT) is a cognitive-behavioral intervention aimed at reducing risk for criminal recidivism by restructuring antisocial attitudes and cognitions (i.e., “criminogenic thinking”). MRT has empirical support for reducing risk for criminal recidivism among civilian offenders. Recently, a version of MRT was developed for military veterans; however, no randomized controlled trials (RCT) have been conducted with the veteran-specific protocol, and the effectiveness and implementation potential of MRT outside of correctional settings has not been established. Methods: Using a Hybrid Type 1 RCT design, this study will test the effectiveness of MRT to reduce risk for criminal recidivism and improve health-related outcomes among justice-involved veterans entering mental health residential treatment at three US Veterans Health Administration (VHA) Medical Centers. Upon admission to the treatment program, justice-involved veterans will complete a baseline assessment, be randomized to usual care (UC) or UC + MRT, and be followed 6 and 12 months post-baseline. A process evaluation will also be conducted to identify barriers and facilitators to implementation of MRT in residential treatment. Discussion: The primary aim of this study is to evaluate the effectiveness of MRT with justice-involved veterans. If MRT proves effective in this trial, the findings can provide large healthcare systems that serve veterans with an evidence-based intervention for addressing criminogenic thinking among justice-involved adults, as well as guidance on how to facilitate future implementation of MRT in non-correctional settings.
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.