Facilitators and Barriers to Verifying Penicillin Allergies in a Veteran Nursing Home Population
Abstract: Background: Unconfirmed penicillin allergies are common and may contribute to adverse outcomes, especially in frail older patients. Evidence-based clinical pathways for evaluating penicillin allergies have been effectively and safely applied in selected settings, but not in nursing home populations. Objective: To identify potential facilitators and barriers to implementing a strategy to verify penicillin allergies in Veterans Health Administration nursing homes, known as Community Living Centers (CLCs). Methods: We conducted semi structured interviews with staff, patients, and family members at 1 CLC to assess their understanding of penicillin allergies and receptiveness to verifying the allergy. We also asked staff about the proposed allergy assessment strategy, including willingness to delabel by history and feasibility of performing oral challenges or skin testing on their unit. Results: From 24 interviews (11 front-line staff, 4 leadership, 3 patients, 6 family members), we identified several facilitators or barriers. Staff recognized the importance of allergy verification and were willing to support and assist in implementing verification strategies. The CLC residents were willing to have their allergy status verified. However, some family members expressed reluctance to verifying their relative's allergy status owing to safety concerns. Front-line staff also expressed concern over having the necessary resources, including time and expertise, to implement the strategy. Staff suggested involving clinical pharmacists and educating staff, patients, and family members as ways to overcome these barriers. Conclusions: Concerns about safety and staff resources are important potential barriers to implementing verification strategies. Involvement of pharmacists and education of both staff and patients and family members will be important components of any successful intervention.
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.