Compensation and Pension Exams for Military Sexual Trauma–Related Posttraumatic Stress Disorder: Examiner Perspectives, Clinical Impacts on Veterans, and Strategies
Abstract: It is estimated that in one in three women veterans experience military sexual trauma (MST), which is strongly associated with posttraumatic stress disorder (PTSD). A 2018 report indicated the Veterans Benefits Administration (VBA) processed approximately 12,000 disability claims annually for PTSD related to MST, most of which are filed by women. Part of the VBA adjudication process involves reviewing information from a Compensation and Pension (C&P) exam, a forensic diagnostic evaluation that helps determine the relationship among military service, diagnoses, and current psychosocial functioning. The quality and outcome of these exams may affect veteran well-being and use of Veterans Health Administration (VHA) mental health care, but no work has looked at examiner perspectives of MST C&P exams and their potential clinical impacts on veteran claimants. Thirteen clinicians (“examiners”) who conduct MST C&P exams through VHA were interviewed. Data were analyzed using rapid qualitative methods. Examiners described MST exams as more clinically and diagnostically complex than non-MST PTSD exams. Examiners noted that assessing “markers” of MST (indication that MST occurred) could make veterans feel disbelieved; others raised concerns related to malingered PTSD symptoms. Examiners identified unique challenges for veterans who underreport MST (e.g., men and lesbian, gay, bisexual, transgender, and queer [LGBTQ+] veterans), and saw evaluations as a conduit to psychotherapy referrals and utilization of VHA mental health care. Last, examiners used strategies to convey respect and minimize retraumatization, including a standardized process and validating the difficulty of the process. Examiners’ responses offer insight into a process entered by thousands of veterans annually with PTSD. Strengthening the MST C&P process is a unique opportunity to enhance trust in the VBA claims process and increase likelihood of using VHA mental health care, especially for women veterans.
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.