MANUAL FOR THE WELL-BEING INVENTORY (WBI): A multidimensional tool for assessing key components of well-being
Abstract: This manual provides information on the development and validation of the Well-Being Inventory (WBI), scoring guidelines, and normative information that can be used to contextualize findings based on the WBI. The WBI is a multidimensional instrument that was designed to assess status, functioning, and satisfaction with four key life domains of vocation, finances, health, and social relationships. The WBI is the product of a multi-phase psychometric endeavor that was funded jointly by the National Center for Posttraumatic Stress Disorder, US Department of Veterans Affairs, Health Services Research & Development, and the Veteran Metrics Initiative, which is managed by the Henry M. Jackson Foundation for the Advancement of Military Medicine, Inc. Although the WBI was developed and validated for use with the military veteran population, it may also have utility for administration within other adult populations. The current chapter presents the rationale for developing the WBI, provides information on the constructs for the four key domains, and describes potential uses of the WBI.
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.