Understaffing in Nursing: Literature Review

Table of Contents

Inadequate nurse staffing is a problem that has plagued the healthcare industry for a long time. Understaffing is associated with adverse health outcomes such as reduction in the quality of health services, high incidence of medical errors, and poor patient satisfaction (Profit et al., 2014). As a result, it is necessary to look into methods of enhancing nursing staff levels in healthcare settings. The purpose of this literature review is to compare existing information concerning the issue of understaffing in nursing. This paper compares the research questions, sample populations, and limitations of studies addressing the problem of inadequate nursing staff. Recommendations from the literature to address the research problem are also provided.

A Comparison of Research Questions

Kim and Mehrorta (2015) aimed at investigating the prevailing staffing levels and schedules to provide detailed solutions to the shortfalls of the second stage of scheduling. McHugh and Ma (2014) determined the impact of wages and work environments on nurse burnout in a hospital setting. Conversely, Twigg, Myers, Duffield, Pugh, Gelder, and Roche (2016) questioned how adding nursing assistants to teams in acute care wards would affect patient outcomes.

Similarly, McHugh et al. (2016) examined how enhanced nurse staffing and good working conditions influenced the survival rates of patients with cardiac arrest while Cho, Kim, Yeon, You, and Lee (2015) tested the impact of nursing shortage on missed nursing outcomes by contrasting the incidence of missed nursing care in hospitals with high staffing rates versus hospitals with low staffing levels. A study conducted by Profit et al. (2014) also sought to evaluate and illustrate the relationship between burnout rates in nurses working in neonatal intensive care units and patient safety culture.

On the contrary, Fagerstrom, Lonning, and Andersen (2014) probed the effectiveness of the RAFAELA staffing system in maintaining high staffing rates. Even with good salaries and adequate staffing levels, nurses need to feel appreciated and supported, especially when they join the practice field for the first time. Hence, Edwards, Hawker, Carrier, and Rees (2015) examined the strategies of implementing the transition of student nurses into qualified nurses and the effectiveness of these tactics on retaining newly-employed nurses.

A Comparison of Sample Populations

The studies reviewed varied in terms of settings and participants. Kim and Mehrorta (2015) developed a simulation model based on a virtual clinical model and applied it to a clinical setting. However, no specific sample populations were provided. McHugh and Ma (2014), Twigg et al. (2016), and Profit et al. (2014) carried out their research in clinical settings involving 100,000, 256,302, and 2,073 respondents respectively.

Edwards et al. (2015) and Cho et al. (2015) also conducted their investigations in clinical settings. However, Edwards et al. (2015) examined 11,160 files belonging to adult patients, whereas 232 nurses participated in the study by Cho et al. (2015). Fagerstrom et al. (2014) and McHugh et al. (2016) conducted systematic reviews of the literature by appraising 30 and 40 research articles respectively.

A Comparison of the Limitations of the Study

Kim and Mehrorta (2015) admitted that the integrated staffing and scheduling model used was relevant only for a single unit, which limited the generalizability of the model to complex systems. The model also presumed that nurses had similar skill sets, which was not true in practice settings. McHugh and Ma (2014) and McHugh et al. (2016) acknowledged that the cross-sectional designs constrained the capacity to infer causality.

Additionally, McHugh and Ma (2014) reported that the survey and wage data preceded the economic recession that started at the close of 2007. Consequently, there was a likelihood that the financial depression could have interfered with perceptions of the undesirable features of the work settings. Another shortcoming was that the outcomes of the study were nurse-specific, whereas the wage data were not, which could have affected the magnitude of the observed gains (McHugh & Ma, 2014).

Twigg et al. (2016) admitted that the pre-test and post-test method of analysis could have obscured the accuracy of the ward type data because patients received care in different wards in a single admission. Additionally, the dates when adverse events occurred were not recorded. Another limitation of the study was large variations in the deployment of nursing assistants in the hospital (Twigg et al., 2016).

Profit et al. (2014) only used data from volunteer regional, community, and intermediate NICUs, which affected the accuracy and generalizability of the outcomes. Edwards et al. (2015) limited their search to English hence excluding other relevant studies in different languages. Fagerstrom et al. (2014) reported that one limitation of the RAFAELA system was the inability to maximize the nurse proficiency variable. The self-assessment of missed nursing outcomes could have affected the accuracy of the findings by Cho et al. (2015).


Despite the shortcomings of the studies, the literature review showed that poor wages, understaffing, burnout, and unfriendly work environments lowered nurse satisfaction and increased turnover rates, which were associated with poor patient outcomes. On the other hand, improving the working environment by creating better wages and employing extra help led to high satisfaction levels in nurses and quality patient outcomes. Additionally, providing support during the transition of nurses from student status into newly-qualified nurses enhanced job satisfaction thus reducing nurse turnover rates and improving patient outcomes.

The use of computational models also facilitated the prediction of staffing and scheduling problems, which could help managers to foresee staffing issues and address them promptly. Therefore, healthcare settings should strive to improve their work environments using the recommended strategies to minimize nurse burnout, improve job satisfaction, reduce turnover rates, and enhance patient outcomes. Future studies should look into additional strategies for simulating staffing and work scheduling problems in complex healthcare systems.


Cho, S. H., Kim, Y. S., Yeon, K. N., You, S. J., & Lee, I. D. (2015). Effects of increasing nurse staffing on missed nursing care. International Nursing Review, 62(2), 267-274.

Edwards, D., Hawker, C., Carrier, J., & Rees. C. (2015). A systematic review of the effectiveness of strategies and interventions to improve the transition from student to newly qualified nurse. International Journal of Nursing Studies, 52(7), 1254-1268.

Fagerstrom, L., Lonning, K., & Andersen, M. H. (2014). The RAFAELA system: A workforce planning tool for nurse staffing and human resource management. Nursing Management, 21(2), 30-36.

Kim, K., & Mehrotra, S. (2015). A two-stage stochastic integer programming approach to integrated staffing and scheduling with application to nurse management. Operations Research, 63(6), 1431-1451.

McHugh, M. D., & Ma, C. (2014). Wage, work environment, and staffing: effects on nurse outcomes. Policy, Politics, & Nursing Practice, 15(3-4), 72-80.

McHugh, M. D., Rochman, M. F., Sloane, D. M., Berg, R. A., Mancini, M. E., Nadkarni, V. M., … Aiken, L. H. (2016). Better nurse staffing and nurse work environments associated with increased survival of in-hospital cardiac arrest patients. Medical Care, 54(1), 74-80.

Profit, J., Sharek, P. J., Amspoker, A. B., Kowalkowski, M. A., Nisbet, C. C., Thomas, E. J., … Sexton, J. B. (2014). Burnout in the NICU setting and its relation to safety culture. BMJ Quality & Safety, 23(10), 806-813.

Twigg, D. E., Myers, H., Duffield, C., Pugh, J. D., Gelder, L., & Roche, M. (2016). The impact of adding assistants in nursing to acute care hospital ward nurse staffing on adverse patient outcomes: An analysis of administrative health data. International Journal of Nursing Studies, 63(1), 189-200.

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