Pathways to Care, Mental Health and Wellbeing Transition Study
This Pathways to Care Report is the second of eight reports and two papers that comprise the Transition and Wellbeing Research Programme (the Programme). The Programme is the most comprehensive study undertaken in Australia on the impact of military service on the mental, physical and social health of Transitioned and 2015 Regular Australian Defence Force (ADF) members and their families (the study populations).
This report complements the first report, Mental Health Prevalence, which explored the prevalence of 12-month and lifetime mental disorder in the Transitioned ADF and compared self-reported symptoms in Transitioned ADF with 2015 Regular ADF members.
Pathways to Care investigates how Transitioned and 2015 Regular ADF access, use and value mental health services. This includes the proportion who received care, the type of care received, reasons for seeking care, pathways into care, satisfaction with services, funding of services and their attitudes and beliefs about mental health and seeking 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.