Veterans with poor PTSD treatment adherence: Exploring their loved ones' experience of PTSD and understanding of PTSD treatment
Abstract: Trauma-focused psychotherapies such as cognitive processing therapy (CPT) and prolonged exposure (PE) are some of the most effective treatments available for posttraumatic stress disorder (PTSD). These treatments have been widely disseminated and promoted throughout the VA Health care System. However, adherence to and completion of these protocols among veterans is often poor, resulting in diminished impact. “Support persons” (SPs) such as relatives and close friends may provide a source of emotional or practical support in treatment, but little is known about how SPs are involved in or exposed to treatment principles and activities. The primary goal of the current research was to examine the experience of SPs of veterans who had poor adherence to treatment. We were interested in SPs’ knowledge about the treatment, their level of involvement in treatment activities or sessions, and their potential interest in more participation or education. Qualitative analyses were used to examine data collected from interviews with 19 SPs of veterans who had an unsuccessful course of CPT or PE. Results indicated generally very low levels of knowledge and treatment participation. However, among most SPs there was substantial interest in the possibility of more treatment involvement, particularly in order to receive guidance from the clinician about how to respond to the veteran’s symptoms. We suggest that it is possible and desirable to incorporate loved ones more formally into such protocols.
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.