Hospital mortality and optimality of nursing workload: A study on the predictive validity of the RAFAELA Nursing Intensity and Staffing system.

Background Patient classification systems have been developed to manage workloads by estimating the need for nursing resources through the identification and quantification of individual patients’ care needs. There is in use a diverse variety of patient classification systems. Most of them lack validity and reliability testing and evidence of the relationship to nursing outcomes. Objective Predictive validity of the RAFAELA system was tested by examining whether hospital mortality can be predicted by the optimality of nursing workload. Methods In this cross-sectional retrospective observational study, monthly mortality statistics and reports of daily registrations from the RAFAELA system were gathered from 34 inpatient units of two acute care hospitals in 2012 and 2013 ( n = 732). The association of hospital mortality with the chosen predictors (hospital, average daily patient to nurse ratio, average daily nursing workload and average daily workload optimality) was examined by negative binomial regression analyses. Results Compared to the incidence rate of death in the months of overstaffing when average daily nursing workload was below the optimal level, the incidence rate was nearly fivefold when average daily nursing workload was at the optimal level (IRR 4.79, 95% CI 1.57–14.67, p = 0.006) and 13-fold in the months of understaffing when average daily nursing workload was above the optimal level (IRR 12.97, 95% CI 2.86–58.88, p = 0.001). Conclusions Hospital mortality can be predicted by the RAFAELA system. This study rendered additional confirmation for the predictive validity of this patient classification system. In future, larger studies with a wider variety of nurse sensitive outcomes and multiple risk adjustments are needed. Future research should also focus on other important criteria for an adequate nursing workforce management tool such as simplicity, efficiency and acceptability. [ABSTRACT FROM AUTHOR]