They did not know how to talk to us and it seems that they didn't care: narratives from bereaved family members of black Veterans
Abstract: Racial disparities in the quality of health care services, including end of life (EOL) care, are well-documented. While several explanations for these inequities have been proposed, few studies have examined the underlying mechanisms. This paper presents the results of the qualitative phase of a concurrent mixed-methods study (QUANT + QUAL) that sought to identify explanations for observed racial differences in quality of EOL care ratings using the Department of Veterans Affairs Bereaved Family Survey (BFS). The objective of the qualitative phase of the study was to understand the specific experiences that contributed to an unfavorable overall EOL quality rating on the BFS among family members of Black Veterans. We used inductive thematic analysis to code BFS open-ended items associated with 165 Black Veterans whose family member rated the overall quality of care received by the Veteran in the last month of life as “poor” or “fair.” Four major themes emerged from the BFS narratives, including (1) Positive Aspects of Care, (2) Unmet Care Needs, (3) Lack of Empathy, Dignity, and Respect, and (4) Poor Communication. Additionally, some family members offered recommendations for care improvements. Our discussion includes integrated results from both our qualitative and previously reported quantitative findings that may serve as a foundation for future evidence-based interventions to improve the equitable delivery of high-quality EOL care.
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.