Understanding how Canadian health care providers have learned to identify co-occurring PTSD symptoms and dementia in Veterans
Abstract: Introduction: Co-occurring PTSD and dementia in Veterans can be difficult to distinguish from dementia-related responsive behaviours, which may result in inappropriate care management. Improved identification of PTSD and dementia is necessary to inform more appropriate and effective care for Veterans. Aim/Question: The purpose of this study was to understand how Canadian healthcare providers have learned to identify the co-occurrence of PTSD symptoms in Veterans with dementia. Methods: Eight semi-structured interviews employing the Critical Incident Technique were conducted with key informant healthcare providers who treat Veterans from across Canada. Framework analysis was used to code, sort and develop themes. Results: Observed differences in Veterans with PTSD and dementia cued healthcare providers to seek our more information, leading to a new understanding of past trauma underlying the symptoms they observed. Healthcare providers then altered their usual care approaches to utilize trust-based and validation-oriented strategies resulting in more effective care management. Discussion: Improvement in the identification of co-occurring PTSD and dementia in Veterans requires specialized education and training for healthcare providers. Implications for Practice: Recognizing the complex needs of older Veterans with co-occurring PTSD and dementia is necessary for healthcare providers to implement more effective care for this population.
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.