Moving Women Veterans' Health Research Forward: a Special Supplement
Abstract: Women’s increased rate of participation in the US military is reshaping the Veteran population. With the growing number of women Veterans, it is imperative to understand the unique facets of military women’s and women Veterans’ health and health care experiences to ensure that they receive the highest-quality patient-centered care throughout the life course. This special issue of the Journal of General Internal Medicine (JGIM), sponsored by VA Health Services Research & Development (HSR&D) Service and the VA Women’s Health Research Network, highlights innovations and new findings related to women Veterans’ health and health care, including the diverse needs and experiences of women Veterans and active-duty and Reserve/National Guard servicewomen. We received an extraordinary number of submissions, reflecting the richness and depth of the field. After careful review and reflection, we accepted papers clustered around the five themes described below.
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.