Veterans Universal Passport: a pilot of a health and social care record for UK ex-Service personnel
Abstract: The transfer of care between different health and social care systems are often associated with poor outcomes and disengagement. Indeed, following the transition from military to civilian life, ex-service personnel report difficulties in navigating civilian health and social care services. Personal healthcare records are associated with a number of benefits, including improved continuity of care and patient empowerment. As such, this pilot project aimed to assess the benefits of the Veterans Universal Passport (VUP) in supporting UK ex-service personnel accessing NHS services. In-depth semi-structured interviews were carried out with eight participants (three ex-service personnel, two carers, three health and social care professionals) who had used the VUP. Interviews explored the benefits, challenges and unmet needs associated with the VUP. A thematic analysis was used to identify themes within this framework. Participants felt that the VUP improved continuity of care and promoted a feeling of control over care. The military-specific nature of the VUP promoted a sense of identity and provided a ‘support scaffold’ for navigating the complexities of the civilian healthcare system. Challenges included awareness among health and social care professionals, and engagement of users. All participants suggested development into a digital application. Findings suggest that the VUP had a positive impact on veterans’ access to civilian health and social care services, highlighting that it provided a much-needed structure to their journey through treatment. Considering the parallels with other health and social care transitions, translation for other populations may be beneficial.
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.