Patient acuity and nurse staffing challenges in Norwegian neonatal intensive care units

Ohnstad, Mari Oma; Solberg, Marianne Trygg

doi:10.1111/jonm.12495

Cited by 13 publications

(12 citation statements)

References 24 publications

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“…With regard to seasonality, NSLs (patients per nurse) are higher during the summer season ( M = 11.34; SD = 3.6). This might reflect the decrease in recruitment during the vacation period in hospitals, despite scientific evidence that there is no drop in demand for nursing during summer vacation vs. the rest of the year (Ohnstad & Solberg, 2017).…”

Section: Discussionmentioning

confidence: 99%

“…Results showed that shift patterns could range from 8.94 patients per nurse on morning shifts to 12.4 patients per nurse on evening or night shifts. This traditional shift pattern (50% in the morning, 30% in the evening, and 20% at night) is based on projected activity levels of nurses over a three‐shift pattern, but takes scant account of the fluctuating demand and dependency of care and patient status (Ohnstad & Solberg, 2017).…”

Section: Discussionmentioning

confidence: 99%

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Factors That Influence Nurse Staffing Levels in Acute Care Hospital Settings

Pórcel‐Gálvez

Fernández‐García

Rafferty

et al. 2021

J of Nursing Scholarship

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Purpose To identify which patient and hospital characteristics are related to nurse staffing levels in acute care hospital settings. Design A cross‐sectional design was used for this study. Methods The sample comprised 1,004 patients across 10 hospitals in the Andalucian Health Care System (southern Spain) in 2015. The sampling was carried out in a stratified, consecutive manner on the basis of (a) hospital size by geographical location, (b) type of hospital unit, and (c) patients’ sex and age group. Random criteria were used to select patients based on their user identification in the electronic health record system. The variables were grouped into two categories, patient and hospital characteristics. Multilevel linear regression models (MLMs) with random intercepts were used. Two models were fitted: the first was the null model, which contained no explanatory variables except the intercepts (fixed and random), and the second (explanatory) model included selected independent variables. Independent variables were allowed to enter the explanatory model if their univariate association with the nurse staffing level in the MLM was significant at p < .05. Results Two hierarchical levels were established to control variance (patients and hospital). The model variables explained 63.4% of the variance at level 1 (patients) and 71.8% at level 2 (hospital). Statistically significant factors were the type of hospital unit (p = .002), shift (p < .001), and season (p < .001). None of the variables associated with patient characteristics obtained statistical significance in the model. Conclusions Nurse staffing levels were associated with hospital characteristics rather than patient characteristics. Clinical Relevance This study provides evidence about factors that impact on nurse staffing levels in the settings studied. Further studies should determine the influence of patient characteristics in determining optimal nurse staffing levels.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Factors That Influence Nurse Staffing Levels in Acute Care Hospital Settings

Pórcel‐Gálvez

Fernández‐García

Rafferty

et al. 2021

J of Nursing Scholarship

View full text Add to dashboard Cite

show abstract

“…Many observational studies have estimated the number of nurses needed in a unit based on estimates of patient acuity levels. For example, one study estimated the number of nurses needed in a neonatal intensive care unit (NICU) by assigning nurse ratios based on a five‐level acuity scale (Ohnstad & Solberg, 2017). However, as this study demonstrates, these ratios can provide erroneous estimates of true nurse staffing.…”

Section: Discussionmentioning

confidence: 99%

Quantifying paediatric intensive care unit staffing levels at a paediatric academic medical centre: A mixed‐methods approach

et al. 2021

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Aim To identify, simulate and evaluate the formal and informal patient‐level and unit‐level factors that nurse managers use to determine the number of nurses for each shift. Background Nurse staffing schedules are commonly set based on metrics such as midnight census that do not account for seasonality or midday turnover, resulting in last‐minute adjustments or inappropriate staffing levels. Methods Staffing schedules at a paediatric intensive care unit (PICU) were simulated based on nurse‐to‐patient assignment rules from interviews with nursing management. Multivariate regression modelled the discrepancies between scheduled and historical staffing levels and constructed rules to reduce these discrepancies. The primary outcome was the median difference between simulated and historical staffing levels. Results Nurse‐to‐patient ratios underestimated staffing by a median of 1.5 nurses per shift. Multivariate regression identified patient turnover as the primary factor accounting for this difference and subgroup analysis revealed that patient age and weight were also important. New rules reduced the difference to a median of 0.07 nurses per shift. Conclusion Measurable, predictable indicators of patient acuity and historical trends may allow for schedules that better match demand. Implications for Nursing Management Data‐driven methods can quantify what drives unit demand and generate nurse schedules that require fewer last‐minute adjustments.

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“…Because patient acuity levels may decrease during weekends, evenings, and holidays, 49,50 some hospital administrations lower nurse staffing levels during these times. However, when staffing nurses by patient acuity rather than by day of the week or season, researchers have found that staffing levels should be maintained, not lowered, to enhance patient safety 51 . Similarly, De Cordova et al 26 noted that patients were more likely to have a longer hospital LOS when staffing levels were lower during nightshift compared to dayshift 24 .…”

Section: Introductionmentioning

confidence: 99%

Nurse staffing legislation: Empirical evidence and policy analysis

2021

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Unsafe nurse staffing conditions in hospitals have been shown to increase the risk of adverse patient events, including mortality. Consequently, United States and international professional nursing organizations often advocate for safer staffing conditions. There are a variety of factors to consider when staffing nurses for patient safety, such as the number of patients per nurse, nurse preparation, patient acuity, and nurse autonomy. The complex issue of staffing nurses often is compounded by cost issues and can become politicized. When nurse organizations' recommendations for safe staffing measures are disregarded by hospital administrations, nurse lobbyists and interest groups often pursue legislative action to protect patients and nurses from unsafe staffing conditions. This article presents a narrative review of safe nurse staffing factors and an analysis of nurse staffing legislation. Using a patient‐centric lens, three state‐level nurse staffing policies (mandated nurse‐to‐patient ratios, public reporting of staffing plans, and nurse staffing committees) were evaluated by empirical evidence, cost to hospitals and state governments, political feasibility, and potential to affect patient populations. Although nurse staffing policy analysis can be conducted in several ways, it is crucial that nurses consider empirical evidence related to staffing policies as well as evaluations of implemented policies and political influences.

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Patient acuity and nurse staffing challenges in Norwegian neonatal intensive care units

Abstract: Planning for reduced staffing for weekends and summer seasons is ineffective. Staffing planned for most of the days in a year instead of the median need for nurses will result in a greater increase in the need for nurses in small units compared to larger units.

Cited by 13 publications

References 24 publications

Factors That Influence Nurse Staffing Levels in Acute Care Hospital Settings

Factors That Influence Nurse Staffing Levels in Acute Care Hospital Settings

Quantifying paediatric intensive care unit staffing levels at a paediatric academic medical centre: A mixed‐methods approach

Nurse staffing legislation: Empirical evidence and policy analysis

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