When a Veterans’ Treatment Court Fails: Lessons Learned from a Qualitative Evaluation
Abstract: More than 500 veterans treatment courts (VTCs) provide thousands of eligible veterans across the nation alternative means of resolving criminal charges through a therapeutic, judicially supervised programs. The majority of those VTCs mandate that veteran participants work with a volunteer veteran mentor throughout their tenure in VTC programs. Mentoring has been heralded as a critical and valuable component of VTCs, and it is believed that mentoring discourages substance abuse and promotes adherence to substance abuse interventions. But very little is known about how mentoring actually works. Scant research documents how mentors interact with mentees, what their responsibilities are, or what impact they have on veterans’ progress through rigorous VTC protocols. Through interview data collected following the death of a veteran mentee in a northeastern Study VTC, this research provides in-depth analysis of how mentors and mentees understand their responsibilities with respect to illicit substance use and violations of VTCs’ sobriety requirements. This article provides background data on VTCs and veterans who participate in them, then explores interview and documentary data as part of a case study of a policy failure in the Study VTC. The article concludes with recommendations that could improve mentor/mentee relationships and VTC participants’ access to treatment.
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.