Nursing care for the military veteran and their family
Abstract: Armed Forces personnel risk their lives in defence of their country, whilst their families are exposed to regular changes in location and spending long periods apart. Many veterans have witnessed dreadful incidents, including the death of friends and civilians of all ages and have a high prevalence of common mental health (MH) disorders (Finnegan & Randles, 2022). They are embedded in the fabric of society; for example, in the United Kingdom, there are an estimated 2 M veterans, and in the USA, 25 M. These figures can be multiplied by four to indicate the number of the wider Armed Forces Community (AFC) of serving personnel, veterans and their families.
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.