Preference Consistency: Veteran and Non-Veteran Nursing Home Resident Self-Reported Preferences for Everyday Living
Understanding patient preferences is core to person-centered care. The consistency of everyday preference reporting was assessed comparing responses of Veteran (VA) and non-VA nursing home (NH) residents on the Preferences for Everyday Living Inventory (PELI) at baseline and 5 to 7 days later. Non-VA NH residents demonstrated higher perfect agreement than VA residents (66% vs. 56%, respectively) and higher acceptable agreement (95% vs. 88%, respectively). Multiple regression analyses examined significant predictors of reliability using demographics, cognitive functional variables, and interviewer ratings. In the VA group, higher perfect agreement was associated with residents who were less likely to have hearing deficits, better cognition, and better interviewer ratings related to energy, attention, and comprehension. In the non-VA group, higher perfect agreement was associated with residents who were younger and more independent with walking. Overall, higher agreement was associated with being female, non-VA, and having better cognition. Implications for future research and clinical practice are highlighted.
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.