Behavioral Health Provider Burnout and Mental Health Care in the Veterans Health Administration
Abstract: Although many studies assess predictors of provider burnout, few analyses provide high-quality, consistent evidence on the impact of provider burnout on patient outcomes exist, particularly among behavioral health providers (BHPs). Objective: To assess the impact of burnout among psychiatrists, psychologists, and social workers on access-related quality measures in the Veterans Health Administration (VHA). Design: This study used burnout in VA All Employee Survey (AES) and Mental Health Provider Survey (MHPS) data to predict metrics assessed by the Strategic Analytics for Improvement and Learning Value, Mental Health Domain (MH-SAIL), VHA’s quality monitoring system. The study used prior year (2014–2018) facility-level burnout proportion among BHPs to predict subsequent year (2015–2019) facility-level MH-SAIL domain scores. Analyses used multiple regression models, adjusting for facility characteristics, including BHP staffing and productivity. Participants: Psychologists, psychiatrists, and social workers who responded to the AES and MHPS at 127 VHA facilities. Main Measures: Four compositive outcomes included two objective measures (population coverage, continuity of care), one subjective measure (experience of care), and one composite measure of the former three measures (mental health domain quality). Key Results: Adjusted analyses showed prior year burnout generally had no impact on population coverage, continuity of care, and patient experiences of care but had a negative impact on provider experiences of care consistently across 5 years (p < 0.001). Pooled across years, a 5% higher facility-level burnout in AES and MHPS had a 0.05 and 0.09 standard deviation worse facility experiences of care from the prior year, respectively. Conclusions: Burnout had a significant negative impact on provider-reported experiential outcome measures. This analysis showed that burnout had a negative effect on subjective but not on objective quality measures of Veteran access to care, which could inform future policies and interventions regarding provider burnout.
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.