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

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          Abstract

          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.

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          Most cited references41

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          The association of registered nurse staffing levels and patient outcomes: systematic review and meta-analysis.

          To examine the association between registered nurse (RN) staffing and patient outcomes in acute care hospitals. Twenty-eight studies reported adjusted odds ratios of patient outcomes in categories of RN-to-patient ratio, and met inclusion criteria. Information was abstracted using a standardized protocol. Random effects models assessed heterogeneity and pooled data from individual studies. Increased RN staffing was associated with lower hospital related mortality in intensive care units (ICUs) [odds ratios (OR), 0.91; 95% confidence interval (CI), 0.86-0.96], in surgical (OR, 0.84; 95% CI, 0.80-0.89), and in medical patients (OR, 0.94; 95% CI, 0.94-0.95) per additional full time equivalent per patient day. An increase by 1 RN per patient day was associated with a decreased odds ratio of hospital acquired pneumonia (OR, 0.70; 95% CI, 0.56-0.88), unplanned extubation (OR, 0.49; 95% CI, 0.36-0.67), respiratory failure (OR, 0.40; 95% CI, 0.27-0.59), and cardiac arrest (OR, 0.72; 95% CI, 0.62-0.84) in ICUs, with a lower risk of failure to rescue (OR, 0.84; 95% CI, 0.79-0.90) in surgical patients. Length of stay was shorter by 24% in ICUs (OR, 0.76; 95% CI, 0.62-0.94) and by 31% in surgical patients (OR, 0.69; 95% CI, 0.55-0.86). Studies with different design show associations between increased RN staffing and lower odds of hospital related mortality and adverse patient events. Patient and hospital characteristics, including hospitals' commitment to quality of medical care, likely contribute to the actual causal pathway.
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            Nurse staffing and inpatient hospital mortality.

            Cross-sectional studies of hospital-level administrative data have shown an association between lower levels of staffing of registered nurses (RNs) and increased patient mortality. However, such studies have been criticized because they have not shown a direct link between the level of staffing and individual patient experiences and have not included sufficient statistical controls. We used data from a large tertiary academic medical center involving 197,961 admissions and 176,696 nursing shifts of 8 hours each in 43 hospital units to examine the association between mortality and patient exposure to nursing shifts during which staffing by RNs was 8 hours or more below the staffing target. We also examined the association between mortality and high patient turnover owing to admissions, transfers, and discharges. We used Cox proportional-hazards models in the analyses with adjustment for characteristics of patients and hospital units. Staffing by RNs was within 8 hours of the target level for 84% of shifts, and patient turnover was within 1 SD of the day-shift mean for 93% of shifts. Overall mortality was 61% of the expected rate for similar patients on the basis of modified diagnosis-related groups. There was a significant association between increased mortality and increased exposure to unit shifts during which staffing by RNs was 8 hours or more below the target level (hazard ratio per shift 8 hours or more below target, 1.02; 95% confidence interval [CI], 1.01 to 1.03; P<0.001). The association between increased mortality and high patient turnover was also significant (hazard ratio per high-turnover shift, 1.04; 95% CI, 1.02 to 1.06; P<0.001). In this retrospective observational study, staffing of RNs below target levels was associated with increased mortality, which reinforces the need to match staffing with patients' needs for nursing care. (Funded by the Agency for Healthcare Research and Quality.).
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              Thresholds for the cost–effectiveness of interventions: alternative approaches

              Abstract Many countries use the cost–effectiveness thresholds recommended by the World Health Organization’s Choosing Interventions that are Cost–Effective project (WHO-CHOICE) when evaluating health interventions. This project sets the threshold for cost–effectiveness as the cost of the intervention per disability-adjusted life-year (DALY) averted less than three times the country’s annual gross domestic product (GDP) per capita. Highly cost–effective interventions are defined as meeting a threshold per DALY averted of once the annual GDP per capita. We argue that reliance on these thresholds reduces the value of cost–effectiveness analyses and makes such analyses too blunt to be useful for most decision-making in the field of public health. Use of these thresholds has little theoretical justification, skirts the difficult but necessary ranking of the relative values of locally-applicable interventions and omits any consideration of what is truly affordable. The WHO-CHOICE thresholds set such a low bar for cost–effectiveness that very few interventions with evidence of efficacy can be ruled out. The thresholds have little value in assessing the trade-offs that decision-makers must confront. We present alternative approaches for applying cost–effectiveness criteria to choices in the allocation of health-care resources.
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                Author and article information

                Contributors
                Journal
                Int J Nurs Stud
                Int J Nurs Stud
                International Journal of Nursing Studies
                Pergamon Press
                0020-7489
                1873-491X
                1 May 2021
                May 2021
                : 117
                : 103901
                Affiliations
                [a ]Faculty of Environmental and Life Sciences, University of Southampton, Southampton, UK
                [b ]National Institute for Health Research Applied Research Collaboration (Wessex), Southampton, UK
                [c ]Portsmouth Hospitals University NHS Trust, Portsmouth, UK
                [d ]University of Exeter Medical School, Exeter, UK
                Author notes
                [* ]Corresponding author at: Faculty of Environmental and Life Sciences, University of Southampton, Southampton, UK. peter.griffiths@ 123456soton.ac.uk
                Article
                S0020-7489(21)00033-X 103901
                10.1016/j.ijnurstu.2021.103901
                8220646
                33677251
                afb8233b-3ba4-4463-af45-3066fe9bc0ba
                © 2021 The Author(s)

                This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

                History
                : 2 November 2020
                : 25 January 2021
                : 4 February 2021
                Categories
                Article

                Nursing
                costs and cost analysis,computer simulation,cost savings,health care economics and organizations,hospital information systems,nursing staff,hospital,patient classification systems,personnel staffing and scheduling,nursing administration research,operations research,patient safety,quality of health care,safer nursing care tool,workload

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