Faith-based organizations’ support of Veteran congregants at risk for mental health conditions and suicide: a qualitative study of clergy experiences in los angeles county
Abstract: Faith-based organizations (FBOs) are often “gatekeepers” to mental health care for congregants at risk of mental illness and suicide, especially U.S. military Veterans, but data to inform better collaboration are needed. We conducted focus groups with clergy in Los Angeles County to understand the mental health support FBOs provide and barriers to collaboration with the mental healthcare system. Clergy detailed strategies used to support the mental health of Veteran congregants. Barriers included stigma, limits in clergy training, and incomplete knowledge about community and VA mental health resources. Results suggest strategies to improve collaboration between FBOs and the mental healthcare system in Los Angeles County.
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.