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NUR 621 Wk 8 Benchmark – Staffing Matrix and Reflection

NUR 621 Wk 8 Benchmark – Staffing Matrix and Reflection

Staffing Matrix and Reflection

All patients deserve quality, safe, and timely care. Providing such care requires health care providers to be physically and mentally prepared and adequately resourced. Unit managers should also be proficient in staffing and ensure that the number of staff in each facility at a particular time matches patients’ needs. A medical-surgical unit provides critical care. Patient acuity is also high, implying the need for a staffing model that accounts for acuity levels. Staffing matrices enable unit managers to allocate staff according to patient numbers and needs. The purpose of this paper is to describe the importance of a staffing matrix, units of measurement used when determining full-time equivalents (FTEs), staff adjustment, and variance considerations.

Importance of a Staffing Matrix in the 30-Bed Medical-Surgical Unit

Patient needs are always inconsistent since the type and severity of illness vary from one patient to the other. Health care providers’ skills and experience also vary according to specialties and length of service. In any case, the primary role of staffing is to match patients’ needs with the staff’s expertise (Dixon et al., 2018). Accordingly, a staffing matrix provides a valuable tool for ensuring staffing levels can effectively address patient needs as patient flow fluctuates. In this case, a staffing matrix is crucial in the medical-surgical unit to ensure that nurse competencies and patient needs match. Such congruence enables health care providers to optimize patient outcomes since they will use their expertise and time appropriately. Furthermore, a staffing matrix enables health care professionals to provide a high proportion of care hours since duties’ allocation is not necessary based on patient numbers. For instance, in acuity-based staffing, nurse managers consider the intensity of nursing care required (Long, 2020). A similar approach when developing a staffing matrix will ensure that patients in the medical-surgical unit receive maximum attention.

Matrix Description: FTEs, Measurement Units, and Financial Management Principles

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Health care providers should always look forward to providing optimal care. The staffing matrix (Appendix I) illustrates the number of health care providers expected per shift based on the patient census. Such care providers include registered nurses (RNs) for primary care, nursing assistants (NAs) for a supportive role, and a health unit coordinator to monitor and coordinate functions. Health care providers should always be available in a medical-surgical unit hence the 12-hour shift. Overall, health care providers are expected to work for 72 hours per pay period every two weeks. A full-time work week is 80 hours; thus, 72/80 making 0.9 FTEs.

Units of Work Measurement

Unit managers use different units of work measurements to develop staffing matrices. The guiding principle is that the staff available in a facility at a particular time should address patient needs without significant challenges (Liu et al., 2019). Based on the same principle, the first unit of work measurement considered was the nurse: patient ratio. For a critical care unit like a medical-surgical facility, the ideal nurse: patient ratio is 1:4. Given this, the number of patients that a nurse serves should never exceed four patients. Therefore, approximately nine health care providers would be required when the patient census is 30. The minimum required for 24 patients is six health care providers (Appendix I). Besides the nurse: patient ratio, the patient census was also considered when developing the matrix. As illustrated in Appendix I, the number of patients declines gradually within the week. The assumption is that the severity of care needed reduces as the patients’ volume declines. Matching this decline would prompt a proportional decline in the number of care providers.

NUR 621 Wk 8 Benchmark – Staffing Matrix and Reflection

Financial Management Principles

Staffing affects an organization’s finances since remuneration varies according to work done and the number of staff engaged at a particular time. It is always crucial to use organizational resources efficiently to enhance outcomes (Penner, 2017). In this regard, efficiency was considered when developing the staffing matrix; it is crucial to avoid wastage of human resources by allocating staff where it is not required. No unit should be oversupplied with staff to avoid draining organizational resources. The other financial management principle considered when developing the matrix is acuity. Long (2020) defined patient acuity as the magnitude of care needed based on the severity of a patient’s condition. As earlier indicated, it is assumed that the acuity for 30 patients is higher than 24 patients. Therefore, 30 patients require lesser staff (Appendix I).

Staff Adjustment Based on Patient Census

The medical-surgical unit has a 30-bed capacity, implying that it can only hold a maximum of 30 patients. Therefore, the only reasonable adjustments that can be made is patient census decline as the week progresses. It was assumed that the size of the staff should be proportional to the number of patients. As a result, the number of RNs and NAs was reduced to match the proportional decline in the patient census. However, the nurse: patient ratio of 1:4 was maintained throughout the week.

Making up the Variance while Complying with Guidelines

Using more FTEs than the budgetary allocations has vast implications on patient care and human and financial resources. It can lead to utilization of agency nurses at some point or denying patients optimal care as adjustments occur to compensate for the lost FTEs. One way of making up the variance is to re-examine the staffing plan and fix any FTE leakages. From a staffing perspective, FTE leakage denotes the time lost when the nursing staff is not scheduled to their full FTEs (Gillen & Blankenship, 2018). FTEs’ overuse can be further fixed by reallocating resources but still complying with guidelines. As earlier mentioned, the staffing plan is based on patient acuity. Alternatively, scheduling to volume patterns can be considered. In this approach, the core staff (RN, NA, and health unit coordinator) would not be fixed in every shift. The number would keep varying as nurses get assigned other roles. However, such flexibility in roles’ assignment is only possible in facilities that encourage an enterprise mentality. This implies that a nurse can be assigned duty anywhere in the facility.


Nurse staffing is a critical component of efficient use of organizational resources. Accordingly, nurse managers must always ensure adequate staffing to meet patient needs while avoiding overuse or underuse. A staffing matrix is a reliable tool for matching nurses’ expertise with patients’ needs as the situations oblige. The staffing matrix also allows unit managers to make appropriate adjustments as the patient census increases or decreases. Utilizing a staffing matrix ensures that nurses can provide maximum care hours to optimize outcomes. The nurse-to-patient ratio and the patients’ volume are crucial factors to consider when developing a staff matrix for a 30-bed medical-surgical unit.




Dixon, J., & Knapp, M. (2018). Whose job? The staffing of advance care planning support in twelve international healthcare organizations: A qualitative interview study. BMC Palliative Care17(1), 1-16. https://doi.org/10.1186/s12904-018-0333-1

Gillen, M., & Blankenship, S. (2018).How data analytics reduces nurse leakage, improves care. HIMSS18. https://365.himss.org/sites/himss365/files/365/handouts/550231475/handout-125.pdf

Long, N. (2020).Acuity-based staffing: Improving patient outcomes and staff satisfaction. University of Texas at Tyler. https://scholarworks.uttyler.edu/cgi/viewcontent.cgi?article=1011&context=nursing_msn


Liu, V. X., Bates, D. W., Wiens, J., & Shah, N. H. (2019). The number needed to benefit: estimating the value of predictive analytics in healthcare. Journal of the American Medical Informatics Association26(12), 1655-1659.https://doi.org/10.1093/jamia/ocz088

Penner, S. J. (2017).Economics and financial management for nurses and nurse leaders (3rd ed.).Springer Publishing.

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