Registered nurse effect on long length of stay in the heart failure hospitalizations of African Americans - Tremaine B. Williams; Pearman Parker; Milan Bimali; Maryam Y. Garza; Alisha Crump; Taiquitha Robins; Emel Seker; Ava Storey; Allison Purvis; Mya Tolbert; Anthony Drake; Taren Massey Swindle; et al. (PLoS ONE, 2025)
African Americans experience approximately 2.5 times more heart failure hospitalizations than Caucasians and the complexity of heart failure requires registered nurses to work in collaboration with other types of healthcare professionals. The purpose of this study was to identify care team configurations associated with long lengths of hospital stay in African Americans with heart failure hospitalizations and the related effect of the presence of registered nurses on their length of hospital stay. This study analyzed electronic health record data on the heart failure hospitalizations of 2,274 African American patients. Binomial logistic regression identified the association between specific care team configurations and length of stay among subgroups of African American patients. Of the significant team configurations, a Kruskal-Wallis H test and linear regression further assessed the team composition and the specific change in days associated with a one-unit change in the number of registered nurses on a patient’s care team. Six team configurations were associated with a long length of stay among all African Americans regardless of age, sex, rurality, heart failure severity, and overall health severity. The configurations only differed significantly in the proportion of registered nurses with respect to other care team roles. An increase in one additional registered nurse on a care delivery team was associated with an increase in length of stay of 8.4 hours (i.e., 504 minutes). Identifying the full range of social and technical care delivery tasks performed by RNs, and controlling for their effect on length of stay, may be a key strategy for reducing length of stay and explaining why these six configurations and RNs are associated with long LOS. The identification of these models can be used to support decision-making that optimizes the availability of patient access to high-quality care (e.g., clinical staffing and supplies).