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Hospital Mortality and Optimal Nursing Workload


The problem of understaffing is one of the most recognized issues in the contemporary public sector, which demands a solution (Silva-Santos et al., 2018). The application of the analytical approach to theoretical and evidence-based academic works in this context appears to be a beneficial approach (Burns & Grove, 2011). The following PICO question establishes the research problem: “in a hospital with an imbalance in nurse-patient ratios, can hiring more nurses per room compare with adding LPN personnel increase on quality of care, reduce nursing stress and medical errors, and increase patient safety?” Accordingly, the scope of this research critique is to dwell upon the analytical discussion of quantitative research on the topic in order to observe its principal characteristics.

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Background of the Study

The article by Junttila, Koivu, Fagerström, Haatainen, and Nykänen (2016) is chosen as the object of analysis for this paper. Apparently, the clinical problem, which led to the study, is the issue of understaffing in general. To be more specific, the authors state that there are various patient classification systems that were developed to manage nursing workload more effectively. However, the majority of these systems lack “validity and reliability testing and evidence of the relationship to nursing outcomes” (Junttila et al., 2016, p. 46). This gap in knowledge was the initial impetus of the authors for conducting their research.

The authors mention several areas of concern, which are directly related to the insufficient efficiency of nursing workload’s management: “the safety and quality of patient care, job satisfaction and health of nurses, shortage and turnover of qualified nursing staff and overall cost-effectiveness of health services” (Junttila et al., 2016, p. 47). Based on their observation of the wide variety of literature on the topic, the authors conclude that the negative impact of understaffing on patient and nurse outcomes is immense.

The purpose of the study was to test the RAFAELA Nursing Intensity and Staffing system in order to understand whether “hospital mortality can be predicted by the optimality of nursing workload” (Junttila et al., 2016, p. 46). The authors do not explicitly provide research questions, mostly due to the quantitative nature of their study. However, it could be suggested that the research question for this study could be formulated as follows: is the RAFAELA system efficient enough for managing and optimizing nursing workloads? Accordingly, the purpose of the study as well as the formulated research question directly correlates with the overall problem of the study.

Method of the Study

As it was previously mentioned, the study is of a quantitative nature. In particular, the authors used the cross-sectional retrospective observational approach (Junttila et al., 2016). It is mentioned that “no data concerning individual patients or nurses and no personal sensitive health-related data were available for the researchers” (Junttila et al., 2016, p. 52). Therefore, there were no risks nor benefits for the participants, and informed consent was not needed. Accordingly, the authors mention that in Finland “permission for the use of the statistical administrative data by each organization concerned” is the only requirement of the healthcare organization (Junttila et al., 2016, p. 52). Further, it is essential to discuss the particular aspects of the research process.

The data for this study was collected by gathering “monthly mortality statistics and reports of daily registrations” from the RAFAELA system from 34 inpatient units of two acute care hospitals in 2012 and 2013 in Finland (Junttila et al., 2016, p. 46).

The authors do not explicitly provide a rationale for the selection of this particular data collection method. However, it is possible to suggest that it was chosen because it allows determining the relationships between different variables on the basis of a large sample. The sequence of data collection events includes primarily the gathering of monthly reports from two hospitals and reviewing their compatibility with two inclusion criteria. Firstly, the RAFAELA system should be in use; and secondly, the optimal level of nursing workload for the unit should be determined (Junttila et al., 2016).

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The data management and analysis methods used in the study could be described as follows. The authors developed and elaborated on several factors that can predict mortality in hospitals: the hospital type, “average daily patient to nurse

ratio, average daily nursing workload, and average daily workload optimality” (Junttila et al., 2016, p. 46). Further, the correlation between these predictors and hospital mortality “was examined by negative binomial regression analyses” (Junttila et al., 2016, p. 46). The rigor of the process was assured primarily by the use of SPSS 21 for Windows, which is a commonly used software tool for statistical analysis, which ensures the accuracy of the results. The use of this software also ensures that the probability of researcher bias is minimal for this study.

Results of the Study

The authors interpret the findings of their research by concluding that hospital mortality can be predicted by the RAFAELA system, and they develop their argument in a coherent manner (Junttila et al., 2016). It could be suggested that the results reflect the reality appropriately and reliably. The primary limitation of this study is that it is small-scale, and also only one risk adjustment measure was chosen. Regarding implications for the nursing practice, the confirmation of the predictive validity of the RAFAELA system in the context of hospital mortality is the most important implication. For future research, the authors suggest that the RAFAELA systems should be examined in terms of meeting other important criteria for an adequate nursing workforce management tool.

Ethical Considerations Associated with the Conduct of the Study

It is also highly important to briefly discuss ethical considerations that are relevant to the study. As it was previously mentioned, the study was conducted on the basis of monthly statistical reports that did not contain any personal patient information. Therefore, the issue of patient privacy was not within the scope of the research. Also, as it was also stated by the authors, in Finland there is no need for specific approval for performing such studies. In general, ethical consideration did not play a significant role in this study.


It is possible to restate the importance of the initial thesis statement, formulated in the form of the PICO question. This thesis statement served as the guideline for the development of this paper. The congruity of this statement and the conducted research critique is determined by the critical importance of the chosen academic article for providing evidence-based knowledge for the formulated PICO question. Most importantly, it should be noted that the research finding, which is the most useful in nursing practice, is the predictability of hospital mortality with the RAFAELA system.

The study which was the object of the analysis of this paper is credible and critical to a significant extent since it is based on the employment of peer-reviewed sources and statistical data. The applicability of the article’s results to the nursing practice is high. Evidence-based knowledge was also obtained from this article. It could be summarized as follows: there are various predictors and driving factors that influence hospital mortality, and they could be effectively managed in the context of the nursing workload with the use of patient classification systems, especially the RAFAELA system.


Burns, N., & Grove, S. (2011). Understanding nursing research (5th ed.). St. Louis, MO: Elsevier.

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Junttila, J. K., Koivu, A., Fagerström, L., Haatainen, K., & Nykänen, P. (2016). Hospital mortality and optimality of nursing workload: A study on the predictive validity of the RAFAELA Nursing Intensity and Staffing system. International Journal of Nursing Studies, 60, 46-53. Web.

Silva-Santos, H., Araújo-dos-Santos, T., Alves, A. S., Silva, M. N. D., Costa, H. O. G., & Melo, C. M. M. D. (2018). Error-producing conditions in nursing staff work. Revista Brasileira de Enfermagem, 71(4), 1858-1864.

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