PTSD disability examinations in the Department of Veterans Affairs: A comparison of telehealth and in-person exams
Abstract: It is estimated that the VA will have rendered decisions on roughly 1.4 million disability claims in 2021. A substantial percentage of these are for mental health conditions, specifically posttraumatic stress disorder (PTSD). Prior to the COVID-19 pandemic, almost all Compensation and Pension (C&P) examinations for PTSD were completed in-person; since March 2020, most have been conducted using telehealth. However, the content and quality of such exams, relative to those conducted in-person, have not been studied. The present study compared Initial PTSD examinations by telehealth to those completed in-person. Overall, 105 reports (51 in-person and 54 telehealth) were randomly selected from all Initial PTSD C&P exams completed within VA Connecticut between 2019 and 2020 (1 year preceding the pandemic and the first year of the pandemic). Raters were masked to all information indicating examiner, mode, and date of exam. Exam content was recorded, and exam quality was rated using three metrics that demonstrated adequate reliability and sensitivity in a prior study. There were no statistically significant differences between in-person and tele-exams on any relevant report content variables, report quality metrics, or VA disability rating outcomes. Results support the validity of the use of telehealth for conducting psychological exams for PTSD disability claims within the VA. Implications for the use of telehealth technology in improving operational breadth and reducing barriers to examination and care, both in the VA and beyond, are discussed.
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.