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.
Center for Information and Systems, School of Information Sciences, University of Tampere, Tampere, Finland; Kuopio University Hospital, Kuopio, Finland. Electronic address: jaana.junttila@kuh.fi.
Jaana K
JK
Junttila
Kuopio University Hospital, Kuopio, Finland.
Aija
A
Koivu
Nursing Science, Faculty of Health Sciences, Buskerud and Vestfold University College, Drammen, Norway.
Lisbeth
L
Fagerström
Kuopio University Hospital, Kuopio, Finland; University of Eastern Finland, Kuopio, Finland.
Kaisa
K
Haatainen
Center for Information and Systems, School of Information Sciences, University of Tampere, Tampere, Finland.
Pirkko
P
Nykänen
D003430:Cross-Sectional Studies; D005387:Finland; D017052:Hospital Mortality; D006801:Humans; D009741:Nursing Staff, Hospital; D010561:Personnel Staffing and Scheduling; D012189:Retrospective Studies; D016526:Workload
Hospital mortality; Nursing intensity; Nursing workload; Patient classification system; Quantitative research; Scale development