Augmenting Ongoing Depression Care With a Mutual Peer Support Intervention Versus Self-Help Materials Alone: A Randomized Trial
Abstract: Objective: Various models of peer support may be implemented in mental health settings. This randomized trial assessed the effectiveness of a telephone-delivered mutual peer support intervention. Methods: A total of 443 patients receiving ongoing depression treatment from the U.S. Department of Veterans Affairs were enrolled in either enhanced usual care (N=243) or the peer support intervention (N=200). Intent-to-treat analyses assessed outcomes at six months postenrollment, excluding 56 patients who experienced an unplanned telephone platform shutdown. Results: At baseline, patients had substantial depressive symptoms, functional limitations, and low quality of life. Both groups showed significant clinical improvements at six months, with no significant differences by group. Conclusions: Telephone-delivered mutual peer support for patients with depression did not improve outcomes beyond those observed with enhanced usual care. Other peer support models, with more “professionalized” peers delivering a structured curriculum, may be more effective.
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.