Introduction
With the given issue, the opportunity for improvement revolves around recruiting and retaining the nursing staff by implementing full-time employment. Therefore, the aim is to influence both working nurses and nursing candidates. The date by which it is desirable to achieve the desired level of improvement is six months. Additional full-time nurses can be retained if nurse compensation is raised and a two-year sign-on incentive is provided. Patient outcomes will increase as the managers keep more full-time nurses and adequately educate them, and efficient communication will increase when it happens often. Therefore, the stakeholder population to benefit from the interventions includes the organization’s staff, patients, and their families. In this sense, the improvement will occur in a healthcare facility, and the outcomes will be measured in terms of retained and recruited nurses, who will provide feedback.
Discussion
Lack of primary nursing care is linked to low nurse staffing, which has been highlighted as a significant factor contributing to poor patient outcomes. Recent research has demonstrated relationships at the patient level rather than the hospital or unit level, building on the substantial data from cross-sectional studies. Some studies indicate omissions in care moderate correlations between staffing levels and outcomes, as well as research that directly observes care delivery. Although precise conclusions about causation and effect cannot be drawn from an observational study, there is mounting evidence that inadequate nurse staffing harms patients. Examining the counterargument will help make the case more effective. It appears incredibly implausible that insufficient nurse staffing levels have no harmful effects. Policies of mandated staffing minimums have been heavily debated and enacted in some jurisdictions, most notably California, USA, in part as a response to this evidence. However, even in cases where mandated staffing policies are in place, patient care requirements beyond the bare minimum must be identified, and staffing levels must be modified. The optimal way to determine the necessary nurse staffing level is still debated.
Since the beginning of nursing research, determining the correct nurse staffing levels and measuring workload have been investigated. Numerous reviews on techniques for figuring out nurse staffing requirements have been published over the years. All have called attention to significant gaps in the evidence. According to Daniel & Smith, due to expanded duties and care locations, there is a growing need for nurses in the healthcare industry (2018, p. e12122). Although the techniques’ intentions are admirable, it was determined that none are vital due to the lack of information on the relative costs or efficacy of different staffing tactics and the scant proof of their reliability or validity. This is a complex subject to summarize due to the sheer amount of information and unresolved issues in other evaluations. The current review is characterized as systematic in that its goal is to clarify how it identifies and chooses material. It is nonetheless regarded as a scoping review to summarize findings and identify knowledge gaps. It primarily aimed to map the literature, identifying recent developments, key features, and areas of relative strength and weakness without giving each study an in-depth critical appraisal.
The process used to calculate the necessary durations for patient groups or tasks differs. The literature explains estimating the typical duration of jobs or patient classifications using expert judgment and actual observations. Sometimes, there is a conscious effort to hinge workload/time allocations on achieving a certain quality standard. Non-patient contact time, such as care planning and documentation or other tasks performed away from the patient’s bedside and which are not necessarily simple to associate with specific patients, is handled in various ways. All methods consider this, frequently allocating a fixed amount of time over and above measurable direct care. Studies from the operational research tradition are just one example of a larger body of literature emphasizing nurses depending entirely on workload-measuring instruments (Saville et al., 2019). These studies show that using rosters based on the typical staffing requirement may not be the best way to address the varying patient needs. All approaches use average time allocations with the unstated assumption that when summed across tasks and patients, individual variation can be accommodated, even though some systems seem to be more precise than others, using detailed patient care plans at one extreme and seemingly assuming all patients have similar needs at the other.
The broad methods can be distinguished using the classifications, although there are no absolute fundamental differences. Professional judgment-based approaches, for instance, might use benchmarking to establish a fixed setup for an award based on an underlying staffing model that aims for a specific nurse-patient ratio on each shift and thus resembles a volume-based strategy. Without a formal workload estimate based on measurements, the original estimation of the staffing required may have involved a thorough assessment of patient needs on a specific ward, considering numerous parameters comparable to those in other systems. On the other hand, the setups or daily staffing plans for prototype or indicator systems are based on a measurement of a sample of individual patient demands, with the assumption that this can be generalized to the patient population as a whole.
Conclusion
After the establishment is made, it is implied that the number of hours per day or nurse-to-patient ratio will permanently be fixed, even though these ratios may vary throughout wards. Because there is an indicated absolute minimum staffing level per patient associated with the prototype with the lowest staffing requirement, a prototype classification system, such as the Safer Nursing Care Tool, resembles a perceived loudness-mandated sentence workforce policy aided by assessment of variation above the base requirement, such as that implemented in California.
References
Daniel, K. M., & Smith, C. Y. (2018). Present and future needs for nurses. Journal of Applied Biobehavioral Research, 23(1), e12122. Web.
Saville, C. E., Griffiths, P., Ball, J. E., & Monks, T. (2019). How many nurses do we need? A review and discussion of operational research techniques applied to nurse staffing. International journal of nursing studies, 97, 7–13. Web.