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Background: Nurses play a crucial role in maintaining the safety of surgical patients. Few nurse staffing studies have looked specifically at surgical patients to examine the impact of exposure to low staffing on patient outcomes. Methods: A longitudinal patient analysis was conducted in four organizations in England using routine data from 213 910 admissions to all surgical specialties. Patients' staffing exposures were modelled as counts of understaffed registered nurse and nurse assistant days in the first 5 inpatient days. Understaffing was identified when staffing per patient-day was below the mean for the ward. Cox models were used to examine mortality within 30 days of admission and readmission within 30 days of discharge. Generalized linear models were used to investigate duration of hospital stay and occurrence of hospital-acquired conditions. Results: Increased exposure to registered nurse understaffing was associated with longer hospital stay and increased risk of deep vein thrombosis, pneumonia, and pressure ulcers. This was also true for nurse assistant understaffing, but the effect sizes tended to be smaller. In the Cox models, there were similarly increased hazards of death for registered nurse understaffing (HR 1.09, 95% c.i. 1.07 to 1.12) and nurse assistant understaffing (HR 1.10, 1.08 to 1.13), whereas the effect size of registered nurse understaffing for readmission (HR 1.02, 1.02 to 1.03) was greater than that seen with nurse assistants (HR 1.01, 1.01 to 1.02). Conclusion: Understaffing by both registered nurses and nursing assistants is associated with increased risks of a range of adverse events, and generally larger effects are seen with registered nurse understaffing. This observational longitudinal study examined nurse staffing levels and their association with a variety of adverse patient outcomes for inpatient admissions to all surgical specialties. The patient-level data came from four disparate National Health Service hospital Trusts in England. The results indicated generally larger adverse effects for registered nurse understaffing compared with understaffing for nurse assistants (healthcare support workers and nurse associates). [ABSTRACT FROM AUTHOR]

Background Planning numbers of nursing staff allocated to each hospital ward (the 'staffing establishment') is challenging because both demand for and supply of staff vary. Having low numbers of registered nurses working on a shift is associated with worse quality of care and adverse patient outcomes, including higher risk of patient safety incidents. Most nurse staffing tools recommend setting staffing levels at the average needed but modelling studies suggest that this may not lead to optimal levels. Objective Using computer simulation to estimate the costs and understaffing/overstaffing rates delivered/caused by different approaches to setting staffing establishments. Methods We used patient and roster data from 81 inpatient wards in four English hospital Trusts to develop a simulation of nurse staffing. Outcome measures were understaffed/overstaffed patient shifts and the cost per patient- day. We compared staffing establishments based on average demand with higher and lower baseline levels, using an evidence-based tool to assess daily demand and to guide flexible staff redeployments and temporary staffing hires to make up any shortfalls. Results When baseline staffing was set to meet the average demand, 32% of patient shifts were understaffed by more than 15% after redeployment and hiring from a limited pool of temporary staff. Higher baseline staffing reduced understaffing rates to 21% of patient shifts. Flexible staffing reduced both overstaffing and understaffing but when used with low staffing establishments, the risk of critical understaffing was high, unless temporary staff were unlimited, which was associated with high costs. Conclusion While it is common practice to base staffing establishments on average demand, our results suggest that this may lead to more understaffing than setting establishments at higher levels. Flexible staffing, while an important adjunct to the baseline staffing, was most effective at avoiding understaffing when high numbers of permanent staff were employed. Low staffing establishments with flexible staffing saved money because shifts were unfilled rather than due to efficiencies. Thus, employing low numbers of permanent staff (and relying on temporary staff and redeployments) risks quality of care and patient safety. [ABSTRACT FROM AUTHOR]

Despite a long history of health services research that indicates that having sufficient nursing staff on hospital wards is critical for patient safety, and sustained interest in nurse staffing methods, there is a lack of agreement on how to determine safe staffing levels. For an alternative viewpoint, we look to a separate body of literature that makes use of operational research techniques for planning nurse staffing. Our goal is to provide examples of the use of operational research approaches applied to nurse staffing, and to discuss what they might add to traditional methods. The paper begins with a summary of traditional approaches to nurse staffing and their limitations. We explain some key operational research techniques and how they are relevant to different nurse staffing problems, based on examples from the operational research literature. We identify three key contributions of operational research techniques to these problems: "problem structuring", handling complexity and numerical experimentation. We conclude that decision-making about nurse staffing could be enhanced if operational research techniques were brought in to mainstream nurse staffing research. There are also opportunities for further research on a range of nurse staff planning aspects: skill mix, nursing work other than direct patient care, quantifying risks and benefits of staffing below or above a target level, and validating staffing methods in a range of hospitals. [ABSTRACT FROM AUTHOR]

The contribution of registered nurses towards safe patient care has been demonstrated in many studies. However, most of the evidence linking staffing levels to outcomes is cross-sectional with intrinsic limitations including an inability to establish that presumed cause (staffing) precedes the effect. No reviews have summarised longitudinal studies considering nurse staffing and patient outcomes. To identify and assess the evidence for an association between nurse staffing levels, including the composition of the nursing team, and patient outcomes in acute care settings from longitudinal studies. We undertook a systematic review of studies where the association between nurse staffing with patient outcomes was assessed in a longitudinal design. Studies with repeated cross-sectional analyses were excluded unless a difference-in-difference design was used. We searched Medline, CINAHL, Embase and the Cochrane Library up to February 2022. We used the ROBINS-I tool to assess risk of bias. We synthesised results in a tabular form and a narrative grouped by outcome. 27 papers were included. Studies were conducted in a variety of settings and populations, including adult general medical/surgical wards and adult and neonatal intensive care units. Staffing measures were operationalised in a variety of different ways, making direct comparisons between studies difficult and pooled estimates impossible. Most studies were either at serious (n = 12) or critical (n = 5) risk of bias, with only 3 studies at low risk of bias. Studies with the most risk of bias were judged as likely to underestimate the effect of higher registered nurse staffing. Findings are consistent with an overall picture of a beneficial effect from higher registered nurse staffing on preventing patient death. The evidence is less clear for other patient outcomes with a higher risk of bias, but in general the proposition that higher registered nurse staffing is likely to lead to better patient outcomes is supported. Evidence about the contribution of other nursing staff groups is unclear. The causal relationship between low registered nurse staffing and mortality is plausible and these estimates of relationships from longitudinal studies provide further support. To address residual uncertainties, future studies should be conducted in more than one hospital and using standardised measures when reporting staffing levels. Having more registered nurses on hospital wards is causally linked to reduced mortality – new review shows there is little room for doubt @ora_dall @workforcesoton @turnel. [ABSTRACT FROM AUTHOR]

In the face of pressure to contain costs and make best use of scarce nurses, flexible staff deployment (floating staff between units and temporary hires) guided by a patient classification system may appear an efficient approach to meeting variable demand for care in hospitals. We modelled the cost-effectiveness of different approaches to planning baseline numbers of nurses to roster on general medical/surgical units while using flexible staff to respond to fluctuating demand. We developed an agent-based simulation, where hospital inpatient units move between being understaffed, adequately staffed or overstaffed as staff supply and demand (as measured by the Safer Nursing Care Tool patient classification system) varies. Staffing shortfalls are addressed by floating staff from overstaffed units or hiring temporary staff. We compared a standard staffing plan (baseline rosters set to match average demand) with a higher baseline 'resilient' plan set to match higher than average demand, and a low baseline 'flexible' plan. We varied assumptions about temporary staff availability and estimated the effect of unresolved low staffing on length of stay and death, calculating cost per life saved. Staffing plans with higher baseline rosters led to higher costs but improved outcomes. Cost savings from lower baseline staff mainly arose because shifts were left understaffed and much of the staff cost saving was offset by costs from longer patient stays. With limited temporary staff available, changing from low baseline flexible plan to the standard plan cost £13,117 per life saved and changing from the standard plan to the higher baseline 'resilient' plan cost £8,653 per life saved. Although adverse outcomes from low baseline staffing reduced when more temporary staff were available, higher baselines were even more cost-effective because the saving on staff costs also reduced. With unlimited temporary staff, changing from low baseline plan to the standard cost £4,520 per life saved and changing from the standard plan to the higher baseline cost £3,693 per life saved. S hift-by-shift measurement of patient demand can guide flexible staff deployment, but the baseline number of staff rostered must be sufficient. Higher baseline rosters are more resilient in the face of variation and appear cost-effective. Staffing plans that minimise the number of nurses rostered in advance are likely to harm patients because temporary staff may not be available at short notice. Such plans, which rely heavily on flexible deployments, do not represent an efficient or effective use of nurses. Study registration: ISRCTN 12307968 Tweetable abstract: Economic simulation model of hospital units shows low baseline staff levels with high use of flexible staff are not cost-effective and don't solve nursing shortages. [ABSTRACT FROM AUTHOR]

The importance of nurse staffing levels in acute hospital wards is widely recognised but evidence for tools to determine staffing requirements although extensive, has been reported to be weak. Building on a review of reviews undertaken in 2014, we set out to give an overview of the major approaches to assessing nurse staffing requirements and identify recent evidence in order to address unanswered questions including the accuracy and effectiveness of tools. We undertook a systematic scoping review. Searches of Medline, the Cochrane Library and CINAHL were used to identify recent primary research, which was reviewed in the context of conclusions from existing reviews. The published literature is extensive and describes a variety of uses for tools including establishment setting, daily deployment and retrospective review. There are a variety of approaches including professional judgement, simple volume-based methods (such as patient-to-nurse ratios), patient prototype/classification and timed-task approaches. Tools generally attempt to match staffing to a mean average demand or time requirement despite evidence of skewed demand distributions. The largest group of recent studies reported the evaluation of (mainly new) tools and systems, but provides little evidence of impacts on patient care and none on costs. Benefits of staffing levels set using the tools appear to be linked to increased staffing with no evidence of tools providing a more efficient or effective use of a given staff resource. Although there is evidence that staffing assessments made using tools may correlate with other assessments, different systems lead to dramatically different estimates of staffing requirements. While it is evident that there are many sources of variation in demand, the extent to which systems can deliver staffing levels to meet such demand is unclear. The assumption that staffing to meet average need is the optimal response to varying demand is untested and may be incorrect. Despite the importance of the question and the large volume of publication evidence about nurse staffing methods remains highly limited. There is no evidence to support the choice of any particular tool. Future research should focus on learning more about the use of existing tools rather than simply developing new ones. Priority research questions include how best to use tools to identify the required staffing level to meet varying patient need and the costs and consequences of using tools. Decades of research on tools to determine nurse staffing requirements is largely uninformative. Little is known about the costs or consequences of widely used tools. [ABSTRACT FROM AUTHOR]