Non‐linear relationships between nurse staffing and patients’ length of stay in acute care units: Bayesian dependence modelling

Pitkäaho, Taina; Partanen, Pirjo; Miettinen, Merja; Vehviläinen‐Julkunen, Katri

doi:10.1111/jan.12550

Cited by 22 publications

(22 citation statements)

References 38 publications

(57 reference statements)

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“…Altogether, 137 nurse staffing and outcome variables were identified and listed in order of frequency (Pitkäaho et al . ).…”

Section: Theoretical Frameworkmentioning

confidence: 97%

“…However, the increase of the RNs proportion in staff did not necessarily make the LOS shorter, the RNs proportion between 65–80% predicted shorter LOS than higher staffing with more than 80% of RNs (Pitkäaho et al . ). The reduction of inpatient complications and LOS cannot be solved simply by adding more nurses in contexts where acceptable staffing levels have been achieved (Schreuders et al .…”

Section: Introductionmentioning

confidence: 97%

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The relationship between nurse staffing and length of stay in acute-care: a one-year time-series data

et al. 2016

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“…Altogether, 137 nurse staffing and outcome variables were identified and listed in order of frequency (Pitkäaho et al . ).…”

Section: Theoretical Frameworkmentioning

confidence: 97%

Section: Introductionmentioning

confidence: 97%

The relationship between nurse staffing and length of stay in acute-care: a one-year time-series data

et al. 2016

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“…From this work and the work of others that describe non-linear relationships,24 33 34 it seems likely that a data-driven approach would be able to develop a specific optimum staffing model if good quality data were available. Insight generated in this way is used in areas such as retail, banking, aeronautics, aviation, petrochemical and other safety critical industries to maximise efficiency, quality and safety.…”

Section: Discussionmentioning

confidence: 91%

“…There have been studies which look at the relationship between RN numbers and outcomes22 23 and the non-linear relationships that exist between staffing and length of stay 24. There is also an increasing amount of data collected about the care, experiences and outcomes of patients.…”

Section: Introductionmentioning

confidence: 99%

Mining routinely collected acute data to reveal non-linear relationships between nurse staffing levels and outcomes

et al. 2016

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ObjectivesNursing is a safety critical activity but not easily quantified. This makes the building of predictive staffing models a challenge. The aim of this study was to determine if relationships between registered and non-registered nurse staffing levels and clinical outcomes could be discovered through the mining of routinely collected clinical data. The secondary aim was to examine the feasibility and develop the use of ‘big data’ techniques commonly used in industry for this area of healthcare and examine future uses.SettingThe data were obtained from 1 large acute National Health Service hospital trust in England. Routinely collected physiological, signs and symptom data from a clinical database were extracted, imported and mined alongside a bespoke staffing and outcomes database using Mathmatica V.10. The physiological data consisted of 120 million patient entries over 6 years, the bespoke database consisted of 9 years of daily data on staffing levels and safety factors such as falls.Primary and secondary outcomesTo discover patterns in these data or non-linear relationships that would contribute to modelling. To examine feasibility of this technique in this field.ResultsAfter mining, 40 correlations (p<0.00005) emerged between safety factors, physiological data (such as the presence or absence of nausea) and staffing factors. Several inter-related factors demonstrated step changes where registered nurse availability appeared to relate to physiological parameters or outcomes such as falls and the management of symptoms. Data extraction proved challenging as some commercial databases were not built for extraction of the massive data sets they contain.ConclusionsThe relationship between staffing and outcomes appears to exist. It appears to be non-linear but calculable and a data-driven model appears possible. These findings could be used to build an initial mathematical model for acute staffing which could be further tested.

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“…It should also be possible to develop other methods to mine these data in a way that exposes nursing's non-linear nature and its relationships with outcomes. Other studies suggest a non-linear relationship (Pitkaaho et al, 2014;Staggs and Dunton, 2014). Ultimately, it is possible to develop data sets that link patients, service models and outcomes and use similar methods for future work.…”

Section: Patient Group Receiving Carementioning

confidence: 99%

Nurse staffing levels and outcomes – mining the UK national data sets for insight

Leary

Tomai

Swift

et al. 2017

IJHCQA

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If you would like to write for this, or any other Emerald publication, then please use our Emerald for Authors service information about how to choose which publication to write for and submission guidelines are available for all. Please visit www.emeraldinsight.com/authors for more information. About Emerald www.emeraldinsight.comEmerald is a global publisher linking research and practice to the benefit of society. The company manages a portfolio of more than 290 journals and over 2,350 books and book series volumes, as well as providing an extensive range of online products and additional customer resources and services.Emerald is both COUNTER 4 and TRANSFER compliant. The organization is a partner of the Committee on Publication Ethics (COPE) and also works with Portico and the LOCKSS initiative for digital archive preservation. Purpose -Despite the generation of mass data by the nursing workforce, determining the impact of the contribution to patient safety remains challenging. Several cross-sectional studies have indicated a relationship between staffing and safety. The purpose of this paper is to uncover possible associations and explore if a deeper understanding of relationships between staffing and other factors such as safety could be revealed within routinely collected national data sets. Design/methodology/approach -Two longitudinal routinely collected data sets consisting of 30 years of UK nurse staffing data and seven years of National Health Service (NHS) benchmark data such as survey results, safety and other indicators were used. A correlation matrix was built and a linear correlation operation was applied (Pearson product-moment correlation coefficient).Findings -A number of associations were revealed within both the UK staffing data set and the NHS benchmarking data set. However, the challenges of using these data sets soon became apparent. Practical implications -Staff time and effort are required to collect these data. The limitations of these data sets include inconsistent data collection and quality. The mode of data collection and the itemset collected should be reviewed to generate a data set with robust clinical application. Originality/value -This paper revealed that relationships are likely to be complex and non-linear; however, the main contribution of the paper is the identification of the limitations of routinely collected data. Much time and effort is expended in collecting this data; however, its validity, usefulness and method of routine national data collection appear to require re-examination.

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Non‐linear relationships between nurse staffing and patients’ length of stay in acute care units: Bayesian dependence modelling

Abstract: Flexible nurse staffing is preferable to fixed staffing to provide patients with shorter length of stay in acute care units. In the present research, the Bayesian method revealed non-linear relationships between nurse staffing and patient and care outcomes.

Cited by 22 publications

References 38 publications

The relationship between nurse staffing and length of stay in acute-care: a one-year time-series data

The relationship between nurse staffing and length of stay in acute-care: a one-year time-series data

Mining routinely collected acute data to reveal non-linear relationships between nurse staffing levels and outcomes

Nurse staffing levels and outcomes – mining the UK national data sets for insight

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