Vocational Rehabilitation for Veterans With Felony Histories and Mental Illness: 12-Month Outcomes
Abstract: Lack of employment is an important barrier to successful reintegration encountered by those released from prison with mental illnesses and/or substance use disorders. This study compares 3 different vocational reintegration modalities for a veteran population: (a) basic services; (b) self-study using the About Face Vocational Manual; and (c) the About Face Vocational Program, a standardized group program focused on the About Face Vocational Manual. One-hundred eleven veterans with a history of at least one felony conviction and a mental illness and/or substance use disorder were recruited from a large urban Veterans Affairs (VA) medical center. Veterans were assigned to 1 of the 3 conditions and followed for 12 months. At the end of the 1-year follow-up period, veterans in the group condition had superior competitive and stable employment rates, as well as faster times to employment compared with both the basic and self-study conditions. The self-study condition was generally indistinguishable from the basic services condition. Overall, new employment during the last 6 months of the follow-up period was relatively low. The findings support the use of standardized group vocational reintegration programs such as the About Face Vocational Program. Limitations and implications are discussed.
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.