Evidence map›Paper›PMID 32217698›Full record

ArticleBMJ quality & safety2021

Costs and consequences of using average demand to plan baseline nurse staffing levels: a computer simulation study.

Christina Saville, Thomas Monks, Peter Griffiths, Jane Elisabeth Ball

Open access · hybridAbstract read
In one paragraph

Article in BMJ quality & safety, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.

0numbers the graph read from it
0cells of the map it votes in
12citing papers in PubMed
6.5field-weighted citation impact, top 3% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

12 citing papers in PubMed, 32 citations in OpenAlex.

  1. Nurse staffing models that rely on employment of temporary nurses: A realist review.International journal of nursing studies advances · 2026
    Review
  2. Article
  3. Article
  4. Observational
  5. Article
  6. Article
  7. Article
  8. Review
  9. Beyond patient classification: the "hidden" face of nursing workload.Revista da Escola de Enfermagem da U S P · 2022
    Article
  10. Observational
  11. Article
  12. Observational
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

4 authors at 3 institutions in 2 countries.

Christina SavilleSchool of Health Sciences, University of Southampton, Southampton, Hampshire, UK C.E.Saville@soton.ac.uk.ORCID 0000-0001-7718-5689
Thomas MonksUniversity of Exeter Medical School, University of Exeter, Exeter, Devon, UK.
Peter GriffithsSchool of Health Sciences, University of Southampton, Southampton, Hampshire, UK.
Jane Elisabeth BallSchool of Health Sciences, University of Southampton, Southampton, Hampshire, UK.
University of Southampton · GBKarolinska Institutet · SEUniversity of Exeter · GB

Funding

Department of Health 14/194/21
6 · The paper itself

Abstract

backgroundPlanning 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.

objectiveUsing computer simulation to estimate the costs and understaffing/overstaffing rates delivered/caused by different approaches to setting staffing establishments.

methodsWe 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.

resultsWhen 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.

conclusionWhile 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.

Indexed as

NursesNursing Staff, HospitalComputer SimulationHumansPersonnel Staffing and SchedulingWorkforcedecision analysishealth policyhealth services researchnursessimulation

Identifiers

PMID32217698
PMCPMC7788209
OpenAlexW3012199246

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.