Utilization and outcomes of a Whole Health model of care by Veterans with psychiatric illness
Abstract: The Veterans Health Administration (VHA) is implementing a model of healthcare known as Whole Health (WH). The goal of WH is to shift from a disease-oriented system to one that emphasizes health promotion and disease prevention as well as promotes the use of Complementary and Integrative Health. The aim of this project was to investigate utilization and outcomes of WH programming among Veterans with psychiatric disorders. This was a retrospective study. Subjects were 7138 Veterans who had a mental health diagnosis. Descriptive data, as well as within- and between-subjects analyses were conducted to investigate utilization and outcomes.Setting: The study was conducted at a large VHA medical center. Whole health interventions considered in this study were mindfulness training, health education, wellbeing, and nutrition classes as well as health coaching. Other whole health interventions, such as acupuncture and message were not evaluated. Utilization of mental health treatment services, medication prescriptions, pain scores and suicide-related behaviors were the primary outcome measures.Results: There were 305 Veterans with at least one WH appointment. The existence of several specific psychiatric disorders was associated with WH utilization. The mean number of appointments completed per individual was 6.9 for all WH programming. Finally, WH participation was associated with decreased risk of suicide as well as reduced use of mental health services. There were no significant changes to other outcome variables. WH programming shows promise to reduce suicide risk and the need for mental health services among this population. Additionally, WH services were underutilized and there was a lack of treatment engagement.
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.