Skills training in affective and interpersonal regulation narrative therapy delivered via synchronous telehealth: A case study of a rural woman veteran with complex posttraumatic stress disorder
Abstract: The International Classification of Diseases–11th Revision (ICD-11) includes the diagnosis of complex posttraumatic stress disorder (CPTSD). Clinical practice guidelines support the use of phased care for individuals with CPTSD. This case study illustrates the use of synchronous telehealth to deliver phased treatment to a rural woman veteran with CPTSD. Mrs. A experienced sexual, physical, and emotional abuse throughout her life, perpetrated by family members, intimate partners, and military authority figures. She sought treatment for posttraumatic nightmares and body image issues; she also had pain related to fibromyalgia and chronic migraine headaches. Mrs. A participated in 19 sessions of Skills Training in Affective and Interpersonal Regulation (STAIR) Narrative therapy via synchronous telehealth. Trauma and eating disorder symptoms were assessed before and after treatment and the patient demonstrated clinically significant improvement on measures of these disorders. Patient-provider working alliance and quality of life were assessed post-treatment. Synchronous telehealth use drastically increased with the onset of COVID-19; however, little information on treating CPTSD via synchronous video teleconferencing is available. This case study illustrates an evidence-based, phased therapy for CPTSD while highlighting the feasibility and value of in-home delivery of psychotherapy for CPTSD via synchronous telehealth.
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.