How to Improve the Financial Well-Being of Military Members and their Families: A Data-Driven Approach
Abstract: America's military strength is its all-volunteer force. This volunteer force guarantees the will and commitment of military members to defend the US Constitution. However, the stability and readiness of the US volunteer force are jeopardized by individual and familial financial stressors, such as mounting debt, high interest rates, fees, and penalties. These stressors can lead to divorce, alcohol abuse, and family maltreatment: factors which undermine force readiness and stability. Consequently, Congress should mandate, and the Department of Defense (DoD) must implement, a data-driven longitudinal study using new credit bureau and Thrift Savings Plan (TSP) data to measure and monitor the financial well-being of military members and their families. This study should also evaluate the effectiveness of the DoD Financial Readiness Program to identify improved strategies for enhancing the financial well-being of military personnel and their families, thereby increasing military readiness. A five-factor model, encompassing credit access, income, spending, debts, and assets, may be beneficial for measuring and monitoring financial wellness. Employer-sponsored financial wellness programs are commonplace today and offer employers a positive return on investment. In fact, if comparable financial wellness programs were adopted, the US Government could save over $52 billion annually while simultaneously increasing military readiness and personnel retention.
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.