Predicting patient nurse-level intensity for a subsequent shift in the intensive care unit: A single-centre prospective observational study

Abstract

BackgroundA dynamic optimized nurse staffing model for the Intensive Care Unit (ICU), requires a tool for monitoring nurse-level intensity with validated cut-offs to identify patients requiring 1:1, 2:1 or 3:1 patient-to-nurse ratios. ObjectivesWe aimed to determine the Nursing Activities Score (NAS) cut-off values which can best distinguish between high, average and lower nurse-level intensity as unanimously perceived by care providers, and to evaluate whether these NAS cut-offs allow to predict nurse-level intensity in the next shift or the same shift the next day. DesignA prospective observational study. Setting9 ICUs in a Belgian tertiary care center. ParticipantsAll 3295 patients admitted between March 20, 2013, and September 12, 2013 were included. NAS was quantified at the end of each shift using automatically derived and manually entered care information. Additionally, 412 nurses, 24 nurse managers and 37 physicians rated perceived nurse-level intensity. MethodsWe first assessed concordance between nurses’, nurse managers’ and physicians’ perceptions of lower (3:1 patient-to-nurse ratio), average (2:1 patient-to-nurse ratio) and high (1:1 patient-to-nurse ratio) nurse-level intensity. Next, receiver operating characteristic (ROC) analysis was applied to determine the NAS cut-offs that best distinguish between different levels of perceived intensity for cases with concordant opinions. Last, logistic regression analysis was applied to estimate the ability of these NAS cut-offs to predict low and high perceived intensity during the next shift and during the same shift the next day. ResultsNurses’, nurse managers’ and physicians’ perceptions were concordant in 57.1% (n = 4693) of cases, mostly concerning lower or average intensity. Optimal NAS cut-offs for lower and high intensity patients equaled 52.7% and 69.8%, respectively. The lower intensity NAS cut-off showed 74.0% accuracy to predict lower intensity in the next shift and 75.9% accuracy to predict lower intensity for the same shift the next day. The high intensity NAS cut-off showed 67.9% accuracy to predict high intensity in the next shift and 72.0% accuracy to predict high intensity for the same shift the next day. ConclusionsNAS cut-offs could contribute considerably in predicting patient nurse-level intensity, and thus patient-to-nurse staffing ratios, in the next shift or day. Identification or prediction of high intensity, nevertheless, appears most complex and requires further study. Future studies need to account for the many confounding variables which complicate nurse staffing planning.