Nurse staffing and Veteran outcomes in the Veterans Health Administration's community living centers
Abstract: The demand for nursing care is rising in the long-term care setting. Nurse staffing is a crucial measure linked to health care quality measure outcomes. To assess for associations between nursing hours per patient day (NHPPD) and outcome measures in the Veterans Health Administration Community Living Centers. A retrospective data review of NHPPD and quality measures for 134 community living centers was conducted. Linear regression was used to assess for linear associations between average total NHPPD and 6 quality measures. A significant linear association was found between average total NHPPD and falls with major injury (P = .02) and help with activities of daily living (P = .01). No associations were found between nurse staffing and 4 other quality measures. This study adds to the body of literature regarding the impact of nurse staffing on quality measures.
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.