Internet‐based family training with telephone coaching to promote mental health treatment initiation among veterans with posttraumatic stress disorder: A pilot study
Abstract: Posttraumatic stress disorder (PTSD) is common among military veterans, yet many affected veterans do not seek treatment. Family members of these veterans often experience compromised well-being and a desire for the veteran to receive mental health care. The Veterans Affairs (VA)–Community Reinforcement and Family Training (VA-CRAFT) for PTSD is an internet-based intervention intended to teach veterans’ family members skills to encourage veterans to initiate mental health care. This study assessed the feasibility, acceptability, and potential efficacy of VA-CRAFT with telephone coaching in a sample of 12 spouses and intimate partners of veterans with PTSD. Participants completed the intervention over 12 weeks and were assessed pre- and posttreatment. For feasibility, 75.0% (n = 9) of participants completed the intervention and reported few difficulties and ease of use. Supporting acceptability, all nine completers had mostly favorable impressions of the intervention and perceived it as helpful. Finally, six (50.0%) participants got the PTSD-affected veteran to engage in mental health care; however, aside from potentially increasing treatment talk frequency, outcome expectancy, and self-efficacy, ds = 0.60–1.08, no apparent improvements were observed for any well-being outcomes, ds = 0.01–0.40. Although the findings are promising, given the study limitations, future research is required to evaluate this approach in a full-scale randomized controlled trial.
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.