Joining Forces: The Status of Military and Veteran Health Care in Nursing Curricula
Abstract: According to the Department of Veterans Affairs, there are approximately 23 million veterans living in the United States. In 2012, the Joining Forces initiative highlighted the need to enhance nursing education for the military and veteran population. With the drawdown of 2 long, large-scale conflicts, a young cohort of veterans presented new challenges in health care. Although not necessarily a traditional vulnerable population, given their emergent health care needs, they are vulnerable. Purnell's Model for Cultural Competence provided a framework for this exploratory descriptive study. A national on-line survey of 123 nursing programs that pledged to support Joining Forces responded as to how they addressed the initiatives, curricular content, and facilitators and barriers to the process. The findings suggest that some schools/colleges of nursing have exceeded the initiative goals, some who have implemented little, whereas most are in the process. Respondents shared approaches used to enhance courses and curricula. Faculty who were veterans were a strength to program enhancement. The majority felt that incorporating this content was important, although lack of time and a content-laden curriculum were common barriers. Nurse educators have an ethical obligation to teach culturally sensitive care. Making the pledge was only the first step.
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.