Veterans' experiences of and preferences for patient-centered, measurement-based PTSD care

Abstract: Background: Up to 50% of veterans drop out of trauma-focused evidence-based psychotherapies (TF-EBP) without completing treatment or recovering; evidence suggests this is in part because their posttraumatic stress disorder (PTSD) care is insufficiently patient-centered. There is also evidence that measurement-based care (MBC) for mental health should be personalized to the patient, yet this is not common practice in VA PTSD care. Objectives: To explore veterans' experiences and preferences for aligning measurement-based PTSD care with their own treatment goals. Method: Qualitative interviews were conducted with veterans (n=15) with PTSD who had received at least 2 sessions of a TF-EBP. MEASURES: Survey on the administration of outcomes questionnaires and demographics and an interview about their most recent TF-EBP episode. Results: Half of veterans had symptom-focused goals and half did not; all had at least one treatment goal that was not symptom-focused. They typically met their goals about functioning and coping skills but not their symptom reduction goals. We found veterans overall were receptive to MBC but preferred patient-reported outcomes measures about functioning, wellbeing, coping skills, and understanding their trauma more than the commonly used PTSD symptom scale (the PCL-5). Conclusions: Many veterans in this sample disliked the PCL-5 because it reinforced their maladaptive cognitions. Such veterans might be more receptive to MBC if offered patient-report outcomes measures that better align with their functional and wellbeing goals. For many goal/outcome areas, psychometrically sound measures exist and require better implementation in PTSD care. For some areas, scale development is needed.

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.