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

Peter Griffiths,Christina Saville,Jane Ball,Jeremy Jones,Natalie Pattison,Thomas Monks

doi:10.1016/j.ijnurstu.2019.103487

Abstract

BackgroundThe 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. MethodsWe 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. ResultsThe 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. ConclusionsDespite 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 abstractDecades of research on tools to determine nurse staffing requirements is largely uninformative. Little is known about the costs or consequences of widely used tools.

Highlights

Multiple reviews of research have established that higher registered nurse staffing levels in hospitals are associated with better patient outcomes and improved care quality, including lower risks of in-hospital mortality, shorter lengths of stay and fewer omissions of necessary care (e.g. Brennan et al, 2013, Griffiths et al, 2016a, Griffiths et al, 2018b, Kane et al, 2007, Shekelle, 2013)
We consider the evidence base for approaches to measuring nursing workload and tools used to determine the number of nurses that are required for general acute-care hospital wards
Low nurse staffing is associated with omissions of essential nursing care (Griffiths et al, 2018b), identified as a key mechanism leading to adverse patient outcomes (Recio-Saucedo et al, 2018)

Summary

Introduction

Multiple reviews of research have established that higher registered nurse staffing levels in hospitals are associated with better patient outcomes and improved care quality, including lower risks of in-hospital mortality, shorter lengths of stay and fewer omissions of necessary care (e.g. Brennan et al, 2013, Griffiths et al, 2016a, Griffiths et al, 2018b, Kane et al, 2007, Shekelle, 2013). Building on the extensive evidence from crosssectional studies, recent studies have shown associations at a patient- rather than hospital- or unit-level (Griffiths et al, 2018a, Griffiths et al, 2019, Needleman et al, 2011b). These include studies involving direct observation of care delivery (Bridges et al, 2019) and studies showing that omissions in care mediate associations between staffing levels and outcomes (Ball et al, 2018, Bruyneel et al, 2015, Griffiths et al, 2018a).

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