Qualitative findings from a Housing First evaluation project for homeless Veterans in Canada
Abstract: This two-year study implemented a Housing First approach among homelessness services for Veterans in four cities across Canada (Victoria, Calgary, London, and Toronto). This approach included peer support and harm reduction resources for Veterans. To obtain a detailed evaluation of personal experiences and opinions, focus groups were held with Veterans, housing staff, and stakeholders at three time points during the study: July-September 2012, May-June 2013, and January 2014. Harm reduction and peer support were regarded as positive aspects of this new approach to housing and homelessness. It was suggested that greater mental health support, support from peers with military experience, and issues regarding roommates should be considered in future implementations of housing services for Veterans. It was also noted that to support personal stabilization, permanent housing is preferred over transitional or temporary housing. Future housing programs serving Veterans experiencing homelessness should consider the addition of harm reduction and peer support to further enhance services and help maintain housing stability.
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.