Challenges and recommendations for improving access to evidence-based COPD management among rural Veterans: rural primary care provider perspectives
Abstract: Chronic obstructive pulmonary disease (COPD), a leading cause of disability and death in the U.S., disproportionately affects rural residents.1 Rural counties experience more COPD-related exacerbations, hospitalizations, and deaths than urban counties.1,2 Furthermore, isolated rural veterans have a higher risk of mortality following hospitalization for an acute exacerbation compared to urban veterans.3 Rural–urban disparities in COPD outcomes are multifactorial in origin with contributions from factors such as occupational exposures, tobacco use, and socioeconomic status.4,5 Lack of access to resources required to deliver evidence-based COPD management, such as outpatient pulmonary rehabilitation, likely also affects rural disparities in COPD.6 To address this, we aimed to assess barriers to, facilitators of, and recommendations for improving evidence-based COPD management in rural clinics.
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.