The Veteran Friendly Practice Accreditation Programme: a Mixed-Methods Evaluation
Abstract: The Royal College of General Practitioners (RCGP) Veteran Friendly Practice Accreditation Programme launched in 2019, aiming to allow practices to better identify, treat, and refer veterans, where appropriate, to dedicated NHS services. Aim: To evaluate the effectiveness of the accreditation programme, focusing on benefits for the veteran, the practice, and the delivery of the programme itself. The study evaluated the views of veteran-friendly accredited GP practices across England. A mixed-methods study was undertaken, which collected data via an online survey from 232 accredited primary healthcare (PHC) staff and 15 semi-structured interviews with PHC veteran leads. Interviews were analysed using modified grounded theory. The study found 99% (n = 228) of responders would recommend the programme, 78% (n = 180) reported improved awareness, and 84% (n = 193) a better understanding of veterans' needs. Seventy-two per cent (n = 166) identified benefits for veterans who were engaging more with PHC but participants felt more time was needed, largely owing to the COVID-19 pandemic, to fully assess the impact of the programme on help-seeking behaviour. Challenges included identifying veterans already registered, promoting the accreditation process, and ensuring all PHC staff were kept up to date with veteran issues. The programme has increased signposting to veteran-specific services and resulted in greater understanding of the NHS priority referral criteria for veterans. Recording of veteran status has improved and there was evidence of a better medical record coding system in PHC practices. These findings add to the limited empirical evidence exploring veteran engagement in PHC, and demonstrate how accreditation results in better treatment and identification of veterans.
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.