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Costs and consequences of using average demand to plan baseline nurse staffing levels: a computer simulation study
  1. Christina Saville1,
  2. Thomas Monks2,3,
  3. Peter Griffiths1,3,
  4. Jane Elisabeth Ball1,4
  1. 1 School of Health Sciences, University of Southampton, Southampton, Hampshire, UK
  2. 2 University of Exeter Medical School, University of Exeter, Exeter, Devon, UK
  3. 3 NIHR CLAHRC Wessex, University of Southampton, Southampton, Hampshire, UK
  4. 4 Karolinska Institutet, Stockholm, Sweden
  1. Correspondence to Dr Christina Saville, School of Health Sciences, University of Southampton, Southampton SO17 1BJ, UK; C.E.Saville{at}


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.

  • simulation
  • nurses
  • health policy
  • health services research
  • decision analysis

This is an open access article distributed in accordance with the Creative Commons Attribution 4.0 Unported (CC BY 4.0) license, which permits others to copy, redistribute, remix, transform and build upon this work for any purpose, provided the original work is properly cited, a link to the licence is given, and indication of whether changes were made. See:

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  • Funding This report presents independent research funded by the UK’s National Institute for Health Research (NIHR) Health Services and Delivery Research Programme (award number 14/194/21).

  • Disclaimer The views and opinions expressed in this publication are those of the authors and do not necessarily reflect those of the NHS, the NIHR, NETSCC, the PHR programme or the Department of Health and Social Care.

  • Competing interests PG is a member of the National Health Service Improvement (NHSI) safe staffing faculty steering group. The safe staffing faculty programme is intended to ensure that knowledge of the Safer Nursing Care Tool (SNCT), its development and its operational application is consistently applied across the NHS.

  • Patient consent for publication Not required.

  • Provenance and peer review Not commissioned; externally peer reviewed.

  • Data availability statement This paper draws on research and data to be reported in more detail in the NIHR Journals Library Health Services and Delivery Research. The data for this paper consist of anonymous ward and hospital parameters and simulation results. All data requests should be submitted to the corresponding author for consideration ( Access to available anonymised data may be granted following review. The simulation model and accompanying documentation are also available from the corresponding author on reasonable request. AnyLogic simulation software can be downloaded from

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