Comparing effectiveness of residential versus intensive virtual treatment for Veterans with posttraumatic stress disorder

Abstract: OBJECTIVE: During the COVID-19 pandemic, restrictions imposed on residential treatment programs necessitated rapid implementation of virtual treatment delivery. Posttraumatic stress disorder (PTSD) Residential Rehabilitation Treatment Programs (P-RRTP) are a key mental health treatment for Veterans with PTSD who require more intensive interventions than outpatient care. During the pandemic, the W. G. (Bill) Hefner VA Healthcare System developed and implemented a Virtual Intensive Outpatient Program for PTSD (VIOPP) to meet the needs of the Veteran population. The purpose of this analysis was to compare the effectiveness of VIOPP to P-RRTP. METHOD: Analyses included N = 370 Veterans, n = 193 who completed P-RRTP between January 2018 to April 2020 and n = 177 who completed VIOPP between June 2020 and November 2022 and provided pre- and posttreatment scores. Pre- and posttreatment scores of the PTSD Checklist for DSM-5 (PCL-5) were available for all patients. Pre- and posttreatment depressive symptom scores from the Nine-item Patient Health Questionnaire (PHQ-9) were available for n = 254 Veterans. Paired and independent samples t tests evaluated differences in change scores overall and by treatment modality (residential vs. virtual). RESULTS: Results indicated a significant decrease in PCL-5 scores regardless of treatment modality, p < .001. Despite beginning VIOPP with significantly higher PCL-5 scores than P-RRTP, there were no significant differences in PCL-5 change scores between virtual (M = -16.94) and residential treatment (M = -17.10), p = .910. PHQ-9 scores also decreased significantly for both treatment groups. CONCLUSION: These analyses suggest that intensive virtual treatment has similar effectiveness to residential treatment for PTSD. This supports the development of intensive virtual interventions as viable alternatives to residential treatments and a valuable component within the continuum of PTSD care.

Read the full article
Report a problem with this article

Related articles

  • More for Researchers

    Identifying opioid relapse during COVID-19 using natural language processing of nationwide Veterans Health Administration electronic medical record data

    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.