Inpatient midwifery staffing levels and postpartum readmissions: a retrospective multicentre longitudinal study.

BACKGROUND: Preventing readmission to hospital after giving birth is a key priority, as rates have been rising along with associated costs. There are many contributing factors to readmission, and some are thought to be preventable. Nurse and midwife understaffing has been linked to deficits in care quality. This study explores the relationship between staffing levels and readmission rates in maternity settings. METHODS: We conducted a retrospective longitudinal study using routinely collected individual patient data in three maternity services in England from 2015 to 2020. Data on admissions, discharges and case-mix were extracted from hospital administration systems. Staffing and workload were calculated in Hours Per Patient day per shift in the first two 12-hour shifts of the index (birth) admission. Postpartum readmissions and staffing exposures for all birthing admissions were entered into a hierarchical multivariable logistic regression model to estimate the odds of readmission when staffing was below the mean level for the maternity service. RESULTS: 64 250 maternal admissions resulted in birth and 2903 mothers were readmitted within 30 days of discharge (4.5%). Absolute levels of staffing ranged between 2.3 and 4.1 individuals per midwife in the three services. Below average midwifery staffing was associated with higher rates of postpartum readmissions within 7 days of discharge (adjusted OR (aOR) 1.108, 95% CI 1.003 to 1.223). The effect was smaller and not statistically significant for readmissions within 30 days of discharge (aOR 1.080, 95% CI 0.994 to 1.174). Below average maternity assistant staffing was associated with lower rates of postpartum readmissions (7 days, aOR 0.957, 95% CI 0.867 to 1.057; 30 days aOR 0.965, 95% CI 0.887 to 1.049, both not statistically significant). CONCLUSION: We found evidence that lower than expected midwifery staffing levels is associated with more postpartum readmissions. The nature of the relationship requires further investigation including examining potential mediating factors and reasons for readmission in maternity populations.

Costs and cost-effectiveness of improved nurse staffing levels and skill mix in acute hospitals: A systematic review.

BACKGROUND: Extensive research shows associations between increased nurse staffing levels, skill mix and patient outcomes. However, showing that improved staffing levels are linked to improved outcomes is not sufficient to provide a case for increasing them. This review of economic studies in acute hospitals aims to identify costs and consequences associated with different nurse staffing configurations in hospitals. METHODS: We included economic studies exploring the effect of variation in nurse staffing. We searched PubMed, CINAHL, Embase Econlit, Cochrane library, DARE, NHS EED and the INAHTA website. Risk of bias was assessed using a framework based on the NICE guidance for public health reviews and Henrikson's framework for economic evaluations. Inclusion, data extraction and critical appraisal were undertaken by pairs of reviewers with disagreements resolved by the entire review team. Results were synthesised using a hierarchical matrix to summarise findings of economic evaluations. RESULTS: We found 23 observational studies conducted in the United States of America (16), Australia, Belgium, China, South Korea, and the United Kingdom (3). Fourteen had high risk of bias and nine moderate. Most studies addressed levels of staffing by RNs and/or licensed practical nurses. Six studies found that increased nurse staffing levels were associated with improved outcomes and reduced or unchanged net costs, but most showed increased costs and outcomes. Studies undertaken outside the USA showed that increased nurse staffing was likely to be cost-effective at a per capita gross domestic product (GDP) threshold or lower. Four studies found that increased skill mix was associated with improved outcomes but increased staff costs. Three studies considering net costs found increased registered nurse skill mix associated with net savings and similar or improved outcomes. CONCLUSION: Although more evidence on cost-effectiveness is still needed, increases in absolute or relative numbers of registered nurses in general medical and surgical wards have the potential to be highly cost-effective. The preponderance of the evidence suggests that increasing the proportion of registered nurses is associated with improved outcomes and, potentially, reduced net cost. Conversely, policies that lead to a reduction in the proportion of registered nurses in nursing teams could give worse outcomes at increased costs and there is no evidence that such approaches are cost-effective. In an era of registered nurse scarcity, these results favour investment in registered nurse supply as opposed to using lesser qualified staff as substitutes, especially where baseline nurse staffing and skill mix are low. REGISTRATION: PROSPERO (CRD42021281202). TWEETABLE ABSTRACT: Increasing registered nurse staffing and skill mix can be a net cost-saving solution to nurse shortages. Contrary to the strong policy push towards a dilution of nursing skill mix, investment in supply of RNs should become the priority.

Staffing levels and hospital mortality in England: a national panel study using routinely collected data.

OBJECTIVES: Examine the association between multiple clinical staff levels and case-mix adjusted patient mortality in English hospitals. Most studies investigating the association between hospital staffing levels and mortality have focused on single professional groups, in particular nursing. However, single staff group studies might overestimate effects or neglect important contributions to patient safety from other staff groups. DESIGN: Retrospective observational study of routinely available data. SETTING AND PARTICIPANTS: 138 National Health Service hospital trusts that provided general acute adult services in England between 2015 and 2019. OUTCOME MEASURE: Standardised mortality rates were derived from the Summary Hospital level Mortality Indicator data set, with observed deaths as outcome in our models and expected deaths as offset. Staffing levels were calculated as the ratio of occupied beds per staff group. We developed negative binomial random-effects models with trust as random effects. RESULTS: Hospitals with lower levels of medical and allied healthcare professional (AHP) staff (e.g, occupational therapy, physiotherapy, radiography, speech and language therapy) had significantly higher mortality rates (rate ratio: 1.04, 95% CI 1.02 to 1.06, and 1.04, 95% CI 1.02 to 1.06, respectively), while those with lower support staff had lower mortality rates (0.85, 95% CI 0.79 to 0.91 for nurse support, and 1.00, 95% CI 0.99 to 1.00 for AHP support). Estimates of the association between staffing levels and mortality were stronger between-hospitals than within-hospitals, which were not statistically significant in a within-between random effects model. CONCLUSIONS: In additional to medicine and nursing, AHP staffing levels may influence hospital mortality rates. Considering multiple staff groups simultaneously when examining the association between hospital mortality and clinical staffing levels is crucial. TRIAL REGISTRATION NUMBER: NCT04374812.

The association between multi-disciplinary staffing levels and mortality in acute hospitals: a systematic review.

OBJECTIVES: Health systems worldwide are faced with the challenge of adequately staffing their hospital services. Much of the current research and subsequent policy has been focusing on nurse staffing and minimum ratios to ensure quality and safety of patient care. Nonetheless, nurses are not the only profession who interact with patients, and, therefore, not the only professional group who has the potential to influence the outcomes of patients while in hospital. We aimed to synthesise the evidence on the relationship between multi-disciplinary staffing levels in hospital including nursing, medical and allied health professionals and the risk of death. METHODS: Systematic review. We searched Embase, Medline, CINAHL, and the Cochrane Library for quantitative or mixed methods studies with a quantitative component exploring the association between multi-disciplinary hospital staffing levels and mortality. RESULTS: We included 12 studies. Hospitals with more physicians and registered nurses had lower mortality rates. Higher levels of nursing assistants were associated with higher patient mortality. Only two studies included other health professionals, providing scant evidence about their effect. CONCLUSIONS: Pathways for allied health professionals such as physiotherapists, occupational therapists, dietitians, pharmacists, to impact safety and other patient outcomes are plausible and should be explored in future studies.

Nurse staffing levels and patient outcomes: A systematic review of longitudinal studies.

BACKGROUND: 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. OBJECTIVES: 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. METHODS: 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. RESULTS: 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. CONCLUSION: 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. TWEETABLE ABSTRACT: 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.

Ward staffing guided by a patient classification system: A multi-criteria analysis of "fit" in three acute hospitals.

AIMS: To assess how well the Safer Nursing Care Tool (SNCT) predicts staffing requirements on hospital wards, and to use professional judgement to generate hypotheses about factors associated with a "poor fit". BACKGROUND: The SNCT is widely used in the UK, but there is scant evidence about factors that influence the quality of staffing decisions based upon such patient classification systems. METHODS: Secondary analysis of data from 69 wards in three acute hospitals to assess the precision of the estimated staffing requirement, variation of estimates, correspondence with professional judgement and achieved staffing levels. Nursing workforce leads suggested factors associated with poor fit, based on the wards that rated worst. RESULTS: 39% of wards were frequently understaffed, while frequent overstaffing was less common (12%). 24% of wards needed a sample of over 182 days to estimate the establishment precisely. Potential reasons identified for poor fit included high turnover, older patients, high levels of 1-to-1 specialing, cancer care, small ward size and high within-day variation in demand. CONCLUSIONS: Using a staffing tool without applying professional judgement or triangulating against other methods can lead to inaccurate estimates of staffing requirements and unsafe staffing levels. IMPLICATIONS FOR NURSING MANAGEMENT: Despite the availability of software to calculate staffing requirements, application of professional judgement remains essential.

Beyond ratios - flexible and resilient nurse staffing options to deliver cost-effective hospital care and address staff shortages: A simulation and economic modelling study.

BACKGROUND: 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. OBJECTIVES: 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. DESIGN AND SETTING: 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. RESULTS: 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. CONCLUSION: Shift-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.

Costs and consequences of using average demand to plan baseline nurse staffing levels: a computer simulation study.

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.

The association between 12-hour shifts and nurses-in-charge's perceptions of missed care and staffing adequacy: a retrospective cross-sectional observational study.

BACKGROUND: Due to worldwide nursing shortages and difficulty retaining staff, long shifts for nursing staff (both registered nurses and nursing assistants) working in hospitals have been adopted widely. Because long shifts reduce the daily number of shifts from three to two, many assume that long shifts improve productivity by removing one handover and staff overlap. However, it is unclear whether staffing levels are more likely to be perceived as adequate when more long shifts are used. OBJECTIVES: To investigate the association between the proportion of long (≥12-hour) shifts worked on a ward and nurses-in-charge's perceptions that the staffing level was sufficient to meet patient need. METHODS: A retrospective cross-sectional study using routinely collected data (patient administrative data and rosters) linked to nurses-in-charge's reports from 81 wards within four English hospitals across 1 year (2017). Hierarchical logistic regression models were used to determine associations between the proportion of long shifts and nurses-in-charge's reports of having enough staff for quality or leaving necessary nursing care undone, after controlling for the staffing level relative to demand (shortfall). We tested for interactions between staffing shortfall and the proportion of long shifts. RESULTS: The sample comprised 19648 ward days. On average across wards, 72% of shifts were long. With mixed short and long shifts, the odds of nurses-in-charge reporting that there were enough staff for quality were 14-17% lower than when all shifts were long. For example, the odds of reporting enough staff for quality with between 60-80% long shifts was 15% lower (95% confidence interval 2% to 27%) than with all long shifts. Associations with nursing care left undone were consistent with this pattern. Although including interactions between staffing shortfalls and the proportion of long shifts did not improve model fit, the effect of long shifts did appear to differ according to shortfall, with lower proportions of long shifts associated with benefits when staffing levels were high relative to current norms. CONCLUSIONS: Rather than a clear distinction between wards using short and long shifts, we found that a mixed pattern operated on most days and wards, with no wards using all short shifts. We found that when wards use exclusively long shifts rather than a mixture, nurses-in-charge are more likely to judge that they have enough staff. However, the adverse effects of mixed shifts on perceptions of staffing adequacy may be reduced or eliminated by higher staffing levels. ISRCTN 12307968. Tweetable abstract 12-hour shifts in nursing: a mix of short and long shifts may be worse than all long shifts.

The association between 12-hour shifts and nurses-in-charge's perceptions of missed care and staffing adequacy: a retrospective cross-sectional observational study.

BACKGROUND: Due to worldwide nursing shortages and difficulty retaining staff, long shifts for nursing staff (both registered nurses and nursing assistants) working in hospitals have been adopted widely. Because long shifts reduce the daily number of shifts from three to two, many assume that long shifts improve productivity by removing one handover and staff overlap. However, it is unclear whether staffing levels are more likely to be perceived as adequate when more long shifts are used. OBJECTIVES: To investigate the association between the proportion of long (≥12-hour) shifts worked on a ward and nurses-in-charge's perceptions that the staffing level was sufficient to meet patient need. METHODS: A retrospective cross-sectional study using routinely collected data (patient administrative data and rosters) linked to nurses-in-charge's reports from 81 wards within four English hospitals across 1 year (2017). Hierarchical logistic regression models were used to determine associations between the proportion of long shifts and nurses-in-charge's reports of having enough staff for quality or leaving necessary nursing care undone, after controlling for the staffing level relative to demand (shortfall). We tested for interactions between staffing shortfall and the proportion of long shifts. RESULTS: The sample comprised 19648 ward days. On average across wards, 72% of shifts were long. With mixed short and long shifts, the odds of nurses-in-charge reporting that there were enough staff for quality were 14-17% lower than when all shifts were long. For example, the odds of reporting enough staff for quality with between 60-80% long shifts was 15% lower (95% confidence interval 2% to 27%) than with all long shifts. Associations with nursing care left undone were consistent with this pattern. Although including interactions between staffing shortfalls and the proportion of long shifts did not improve model fit, the effect of long shifts did appear to differ according to shortfall, with lower proportions of long shifts associated with benefits when staffing levels were high relative to current norms. CONCLUSIONS: Rather than a clear distinction between wards using short and long shifts, we found that a mixed pattern operated on most days and wards, with no wards using all short shifts. We found that when wards use exclusively long shifts rather than a mixture, nurses-in-charge are more likely to judge that they have enough staff. However, the adverse effects of mixed shifts on perceptions of staffing adequacy may be reduced or eliminated by higher staffing levels. ISRCTN 12307968. Tweetable abstract 12-hour shifts in nursing: a mix of short and long shifts may be worse than all long shifts.

The association between 12-hour shifts and nurses-in-charge's perceptions of missed care and staffing adequacy: a retrospective cross-sectional observational study.

BACKGROUND: Due to worldwide nursing shortages and difficulty retaining staff, long shifts for nursing staff (both registered nurses and nursing assistants) working in hospitals have been adopted widely. Because long shifts reduce the daily number of shifts from three to two, many assume that long shifts improve productivity by removing one handover and staff overlap. However, it is unclear whether staffing levels are more likely to be perceived as adequate when more long shifts are used. OBJECTIVES: To investigate the association between the proportion of long (≥12-hour) shifts worked on a ward and nurses-in-charge's perceptions that the staffing level was sufficient to meet patient need. METHODS: A retrospective cross-sectional study using routinely collected data (patient administrative data and rosters) linked to nurses-in-charge's reports from 81 wards within four English hospitals across 1 year (2017). Hierarchical logistic regression models were used to determine associations between the proportion of long shifts and nurses-in-charge's reports of having enough staff for quality or leaving necessary nursing care undone, after controlling for the staffing level relative to demand (shortfall). We tested for interactions between staffing shortfall and the proportion of long shifts. RESULTS: The sample comprised 19648 ward days. On average across wards, 72% of shifts were long. With mixed short and long shifts, the odds of nurses-in-charge reporting that there were enough staff for quality were 14-17% lower than when all shifts were long. For example, the odds of reporting enough staff for quality with between 60-80% long shifts was 15% lower (95% confidence interval 2% to 27%) than with all long shifts. Associations with nursing care left undone were consistent with this pattern. Although including interactions between staffing shortfalls and the proportion of long shifts did not improve model fit, the effect of long shifts did appear to differ according to shortfall, with lower proportions of long shifts associated with benefits when staffing levels were high relative to current norms. CONCLUSIONS: Rather than a clear distinction between wards using short and long shifts, we found that a mixed pattern operated on most days and wards, with no wards using all short shifts. We found that when wards use exclusively long shifts rather than a mixture, nurses-in-charge are more likely to judge that they have enough staff. However, the adverse effects of mixed shifts on perceptions of staffing adequacy may be reduced or eliminated by higher staffing levels. ISRCTN 12307968. Tweetable abstract 12-hour shifts in nursing: a mix of short and long shifts may be worse than all long shifts.

Performance of the Safer Nursing Care Tool to measure nurse staffing requirements in acute hospitals: a multicentre observational study.

OBJECTIVES: The best way to determine nurse staffing requirements on hospital wards is unclear. This study explores the precision of estimates of nurse staffing requirements made using the Safer Nursing Care Tool (SNCT) patient classification system for different sample sizes and investigates whether recommended staff levels correspond with professional judgements of adequate staffing. DESIGN: Observational study linking datasets of staffing requirements (estimated using a tool) to professional judgements of adequate staffing. Multilevel logistic regression modelling. SETTING: 81 medical/surgical units in four acute care hospitals. PARTICIPANTS: 22 364 unit days where staffing levels and SNCT ratings were linked to nurse reports of "enough staff for quality". PRIMARY OUTCOME MEASURES: SNCT-estimated staffing requirements and nurses' assessments of staffing adequacy. RESULTS: The recommended minimum sample of 20 days allowed the required number to employ (the establishment) to be estimated with a mean precision (defined as half the width of the CI as a percentage of the mean) of 4.1%. For most units, much larger samples were required to estimate establishments within ±1 whole time equivalent staff member. When staffing was lower than that required according to the SNCT, for each hour per patient day of registered nurse staffing below the required staffing level, the odds of nurses reporting that there were enough staff to provide quality care were reduced by 11%. Correspondingly, the odds of nurses reporting that necessary nursing care was left undone were increased by 14%. No threshold indicating an optimal staffing level was observed. Surgical specialty, patient turnover and more single rooms were associated with lower odds of staffing adequacy. CONCLUSIONS: The SNCT can provide reliable estimates of the number of nurses to employ on a unit, but larger samples than the recommended minimum are usually required. The SNCT provides a measure of nursing workload that correlates with professional judgements, but the recommended staffing levels may not be optimal. Some important sources of systematic variations in staffing requirements for some units are not accounted for. SNCT measurements are a potentially useful adjunct to professional judgement but cannot replace it. TRIAL REGISTRATION NUMBER: ISRCTN12307968.

Nursing workload, nurse staffing methodologies and tools: A systematic scoping review and discussion.

BACKGROUND: 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. METHODS: 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. RESULTS: 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. CONCLUSIONS: 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. TWEETABLE ABSTRACT: 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.

Operational research techniques applied throughout cancer care services: a review.

Cancer is a disease affecting increasing numbers of people. In the UK, the proportion of people affected by cancer is projected to increase from 1 in 3 in 1992, to nearly 1 in 2 by 2020. Health services to tackle cancer can be grouped broadly into prevention, diagnosis, staging, and treatment. We review examples of Operational Research (OR) papers addressing decisions encountered in each of these areas. In conclusion, we find many examples of OR research on screening strategies, as well as on treatment planning and scheduling. On the other hand, our search strategy uncovered comparatively few examples of OR models applied to reducing cancer risks, optimising diagnostic procedures, and staging. Improvements to cancer care services have been made as a result of successful OR modelling. There is potential for closer working with clinicians to enable the impact of other OR studies to be of greater benefit to cancer sufferers.

How many nurses do we need? A review and discussion of operational research techniques applied to nurse staffing.

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.