Using Dominance Analysis to Identify the Most Important Dimensions of Safety Culture for Predicting Patient Safety

Studies have demonstrated associations between safety culture and patient safety based on the perceptions of healthcare professionals, but limited attention has been given to the perceptions of nurses. Moreover, most studies have used regression modeling, an approach that limits researchers’ ability to identify the most important predictors of patient safety due to intercorrelations among predictors in the model. Therefore, the purpose of this study was to examine the effects of seven dimensions of safety culture on nurse-rated patient safety and identify the relative importance of these dimensions for predicting patient safety. This correlational study used data from the Agency for Healthcare Research and Quality’s 2018 Hospital Survey on Patient Safety Culture. Data from 13,031 nurses working in surgical areas of 443 hospitals in the United States were examined using logistic regression and dominance analysis. Staffing adequacy was the strongest predictor of patient safety, followed by hospital management support for patient safety and organizational learning/continuous improvement. However, dominance analysis showed that hospital management support for patient safety was the most important predictor rather than staffing adequacy. Nurse managers and hospital administrators should role model a culture of safety and demonstrate their valuing of patient safety by providing sufficient resources, listening to and valuing staff suggestions regarding patient safety, and providing feedback about organizational changes to improve patient safety.

Missed nursing care, non-nursing tasks, staffing adequacy, and job satisfaction among nurses in a teaching hospital in Egypt

Background Missed nursing care (MNC) has been linked to patient harm in a growing body of literature. However, this issue is still not adequately investigated in developing countries. The aim of the study is to measure the extent of missed nursing care, to identify its types, and to determine factors contributing to missed nursing care. Methods A cross-sectional design was used. The study was conducted among 50 units at 1762-beds teaching Hospital in Alexandria that employs 1211 nurses in inpatient areas. A sample of 553 nurses were interviewed using the MISSCARE and the N4CAST survey. The MISSCARE survey measured the amount of missed nursing care (MNC) that was experienced on the last worked shift by each nurse. The N4CAST survey was used to collect data about level of non-nursing work carried out by nurses and the nurses’ job satisfaction. Results The overall mean score for the missed nursing care was 2.26 ± 0.96 out of 5, with highest mean score attributed to “Planning” and lowest mean score attributed to “Assessment and Vital Signs” (2.64 and 1.96, respectively). Missed nursing care was significantly associated with number of patients admitted and cared for in the last shift and perceived staffing adequacy. Almost all non-nursing care tasks and most of satisfaction elements showed negative weak correlation with overall missed nursing care. Conclusion Missed Nursing Care is common in study hospital which may endanger patient safety. MNC Missed Nursing Care is positively associated with nursing adequacy. There is no association between MNC and neither nurses’ job satisfaction nor non-nursing tasks. Nursing leaders should monitor missed nursing care and the environmental and staffing conditions associated with it in order to design strategies to reduce such phenomena.

Predicting healthcare-associated infections, length of stay, and mortality with the nursing intensity of care index

Objectives: The objectives of this study were (1) to develop and validate a simulation model to estimate daily probabilities of healthcare-associated infections (HAIs), length of stay (LOS), and mortality using time varying patient- and unit-level factors including staffing adequacy and (2) to examine whether HAI incidence varies with staffing adequacy. Setting: The study was conducted at 2 tertiary- and quaternary-care hospitals, a pediatric acute care hospital, and a community hospital within a single New York City healthcare network. Patients: All patients discharged from 2012 through 2016 (N = 562,435). Methods: We developed a non-Markovian simulation to estimate daily conditional probabilities of bloodstream, urinary tract, surgical site, and Clostridioides difficile infection, pneumonia, length of stay, and mortality. Staffing adequacy was modeled based on total nurse staffing (care supply) and the Nursing Intensity of Care Index (care demand). We compared model performance with logistic regression, and we generated case studies to illustrate daily changes in infection risk. We also described infection incidence by unit-level staffing and patient care demand on the day of infection. Results: Most model estimates fell within 95% confidence intervals of actual outcomes. The predictive power of the simulation model exceeded that of logistic regression (area under the curve [AUC], 0.852 and 0.816, respectively). HAI incidence was greatest when staffing was lowest and nursing care intensity was highest. Conclusions: This model has potential clinical utility for identifying modifiable conditions in real time, such as low staffing coupled with high care demand.