Treating Veterans and Military Families: Evidence Based Practices and Training Needs Among Community Clinicians
Abstract: Little is known about the capacity of community providers to provide military informed evidence based services for posttraumatic stress disorder (PTSD). We conducted a regional, web-based survey of 352 community mental health care providers that sought to identify clinical practices, training needs, and predictors of evidence based treatment (EBT) use for PTSD. Overall, 49 % of providers indicated they seldom or never use a validated PTSD screening instrument. Familiarity with EBTs, specifically prolonged exposure (PE; v2 (4) = 14.68, p\.01) and cognitive processing therapy (CPT; v2 (4) = 4.55, p\.05), differed by provider type. Of providers who received training in PE or CPT (N = 121), 75 % reported using treatment in their practice, which was associated with having received clinical supervision (v2 (1) = 20.16, p\.001). Widely disseminated trainings in empirically supported PTSD assessment and treatment, and implementation of case supervision in community settings are needed.
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.