Veteran families with complex needs: a qualitative study of the veterans’ support system
Abstract: Background: Families with complex needs face significant challenges accessing and navigating health and social services. For veteran families, these challenges are exacerbated by interactions between military and civilian systems of care, and the density of the veterans’ non-profit sector. This qualitative study was designed to gather rich, detailed information on complex needs in veteran families; and explore service providers’ and families’ experiences of accessing and navigating the veterans’ support system. Methods: The study comprised participant background questionnaires (n = 34), focus groups with frontline service providers (n = 18), and one-on-one interviews with veteran families (n = 16) in Australia. The semi-structured focus groups and interviews were designed to gather rich, detailed information on four study topics: (i) health and wellbeing needs in veteran families; (ii) service-access barriers and facilitators; (iii) unmet needs and gaps in service provision; and (iv) practical solutions for improving service delivery. The study recruited participants who could best address the focus on veteran families with complex needs. The questionnaire data was used to describe relevant characteristics of the participant sample. The focus groups and interviews were audio-recorded, transcribed, and a reflexive thematic analysis was conducted to identify patterns of shared meaning in the qualitative data. Results: Both service providers and families found the veterans’ support system difficult to access and navigate. System fragmentation was perceived to impede care coordination, and delay access to holistic care for veteran families with complex needs. The medico-legal aspects of compensation and rehabilitation processes were perceived to harm veteran identity, and undermine health and wellbeing outcomes. Recovery-oriented practice was viewed as a way to promote veteran independence and self-management. Participants expressed a strong preference for family-centred care that was informed by an understanding of military lifestyle and culture. Conclusion: The health and wellbeing needs of veteran families intensify during the transition from full-time military service to civilian environments, and service- or reintegration-related difficulties may emerge (or persist) for a significant period of time thereafter. Veteran families with complex needs are unduly burdened by care coordination demands. There is a pressing need for high-quality implementation studies that evaluate initiatives for integrating fragmented systems of 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.