Preliminary Psychometrics and Potential Big Data Uses of the U.S. Army Family Global Assessment Tool
Abstract: The purpose of the present study is to explore the psychometric properties of the U.S. Army’s Family Global Assessment Tool (GAT), which assesses the psychosocial fitness of Army families. With data from 1,692 Army spouses, we examined the structure, reliability and validity of the GAT, using confirmatory factor analysis (CFA) and two validity studies. Fifty-three items and 9 factors were retained following CFA. This model provided a good fit, and scales demonstrated strong internal consistency. Bivariate correlations and results from a theoretically driven model provide preliminary evidence of validity. Findings support the usefulness of the GAT for measuring psychosocial fitness of Army spouses.
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.