Analyzing a Current Health Care Problem or IssueWrite a 4-6 page analysis of a current problem or issue in health care, including a
proposed solution and possible ethical implications.
1- Describe the health care problem or issue you selected for use in Assessment 2 and
provide details about it.
• Explore your chosen topic. For this, you should use the first four steps of the Socratic
Problem-Solving Approach to aid your critical thinking. This approach was introduced in
Assessment 2.
• Identify possible causes for the problem or issue.
Staffing Shortages
o Description: Healthcare systems often struggle with staffing
shortages, leading
to increased workloads for existing staff and potential impacts on
patient care.
o Interventions: Recruitment campaigns, training programs, flexible
scheduling.
o Keywords: Nurse-to-patient ratio, healthcare recruitment, workload.
2- Use scholarly information to describe and explain the health care problem or issue and
identify possible causes for it.
•
Identify at least three scholarly or academic peer-reviewed journal articles about
the topic.
•
Use scholarly or academic peer-reviewed journal articles published during the past
3–5 years that relate to your topic
You may find the applicable Undergraduate Library Research Guide helpful in your
search.
•
3-Analyze the health care problem or issue.
•
•
•
•
Describe the setting or context for the problem or issue.
Describe why the problem or issue is important to you.
Identify groups of people affected by the problem or issue.
Provide examples that support your analysis of the problem or issue.
4-Discuss potential solutions for the health care problem or issue.
•
•
•
Describe what would be required to implement a solution.
Describe potential consequences of ignoring the problem or issue.
Provide the pros and cons for one of the solutions you are proposing.
5-Explain the ethical principles (Beneficence, Nonmaleficence, Autonomy, and
Justice) if potential solution was implemented.
•
•
•
Describe what would be necessary to implement the proposed solution.
Explain the ethical principles that need to be considered (Beneficence,
Nonmaleficence, Autonomy, and Justice) if the potential solution was
implemented.
Provide examples from the literature to support the points you are making.
Additional Requirements:
Your assessment should also meet the following requirements:
•
•
•
•
•
•
•
Length: 4–6 typed, double-spaced pages, not including the title page and
reference page.
Font and font size: Times New Roman, 12 point.
APA tutorial: Use the APA Style Paper Tutorial [DOCX] for guidance.
Written communication: Write clearly and logically, with correct use of
spelling, grammar, punctuation, and mechanics.
Using outside sources: Integrate information from outside sources into
academic writing by appropriately quoting, paraphrasing, and summarizing,
following APA style.
References: Integrate information from outside sources to include at least
three scholarly or academic peer-reviewed journal articles and three in-text
citations within the paper.
APA format: Follow current APA guidelines for in-text citations of outside
sources in the body of your paper and also on the reference page.
Organize your paper using the following structure and headings:
•
Title page. A separate page.
•
•
•
•
•
•
•
•
Introduction. A brief one-paragraph statement about the purpose of the paper.
Elements of the problem/issue. Identify the elements of the problem or issue
or question.
Analysis. Analyze, define, and frame the problem or issue.
Considering options. Consider solutions, responses, or answers.
Solution. Choose a solution, response, or answer.
Ethical implications. Ethical implications of implementing the solution.
Implementation. Implementation of the potential solution.
Conclusion. One paragraph.
Competencies Measured:
By successfully completing this assessment, you will demonstrate your proficiency in the
following course competencies and scoring guide criteria:
•
•
•
•
Competency 1: Apply information literacy and library research skills to obtain
scholarly information in the field of health care.
o Use scholarly information to describe and explain a health care
problem or issue and identify possible causes for it.
Competency 2: Apply scholarly information through critical thinking to solve
problems in the field of health care.
o Analyze a health care problem or issue by describing the context,
explaining why it is important and identifying populations affected
by it.
o Discuss potential solutions for a health care problem or issue and
describe what would be required to implement a solution.
Competency 3: Apply ethical principles and academic standards to the study of
health care.
o Explain the ethical principles (Beneficence, Nonmaleficence,
Autonomy, and Justice) if potential solution was implemented.
Competency 4: Write for a specific audience, in appropriate tone and style, in
accordance with Capella’s writing standards.
o Write clearly and logically, with correct use of spelling, grammar,
punctuation, and mechanics.
o Write following APA style for in-text citations, quotes, and
references.
EDITORIAL
Nurse staffing and patient safety in
acute hospitals: Cassandra calls again?
Peter Griffiths , Chiara Dall’Ora
NIHR Applied Research
Collaboration (Wessex),
University of Southampton,
Southampton, UK
Correspondence to
Professor Peter Griffiths, NIHR
Applied Research Collaboration
(Wessex), University of
Southampton, Southampton,
Hampshire, UK;
peter.griffiths@soton.ac.uk
Accepted 8 November 2022
Published Online First
6 December 2022
► http://dx.doi.org/10.1136/
bmjqs-2022-015291
© Author(s) (or their
employer(s)) 2023. No
commercial re-use. See rights
and permissions. Published by
BMJ.
To cite: Griffiths P, Dall’Ora C.
BMJ Qual Saf
2023;32:241–243.
The risk of adverse patient outcomes,
including death, is lower in hospitals that
provide more registered nurses to care
for patients on inpatient wards. The association has been demonstrated in a body
of evidence comprising several hundred
studies, involving hundreds of hospitals and
millions of patients from around the world.
The association has been shown at hospital
level in large cross-sectional studies and in
a growing number of longitudinal studies
examining the effect of variation in staffing
experienced by individuals.1–3 In the context
of such an extensive body of evidence, one
might ask what could possibly be left to
discover?
In this issue of BMJ Quality and Safety,
Zaranko and colleagues contributed some
important new evidence.4 Their findings
highlight further the potential consequences
of the nursing shortages being experienced
in many countries. Using data from 53
inpatient wards from three hospitals in the
English National Health Service (NHS), the
study focused on team size and composition, linking daily staffing rosters to patient
outcomes. Adding an additional registered
nurse to the average ward team on a shift
reduced the odds of a patient death on that
day by 9.6%. Adding more senior nurses (as
measured by pay grade) had a larger effect
than adding more junior registered nurses,
whereas increases in assistant staff (healthcare support workers) and agency employed
registered nurses were not associated with
reduced mortality.
These findings support those of other
studies that have used varied designs and
taken different approaches to exploring
team composition. A systematic review of
63 mainly cross-sectional studies found that
a nursing team with a higher proportion of
registered nurses was associated with lower
mortality and other adverse outcomes.5
Others using more sophisticated longitudinal
designs have found beneficial effects from
higher assistant staffing,6 while research
published by our team in BMJ Quality
and Safety in recent years has pointed to
complex non-
linear relationships between
assistant staffing and quality, with possible
interactions between assistant staffing and
registered nurse staffing levels.7 8 In general,
this research all supports the same conclusion. Support staff are important members
of the team, but they are not effective substitutes for registered nurses when it comes to
maintaining patient safety. Without sufficient registered nurses to supervise support
staff, benefits are not realised and harm can
occur. Similarly, agency staff are not effective substitutes, with other studies indicating possible harms arising from heavy
reliance on temporary staff.9 Zaranko et al
go beyond the existing research in showing
the additional benefits of more senior registered nurses.
These findings are important because they
highlight the importance of skill mix. Strategies that focus exclusively on increasing
numbers to address staff shortages may be
harmful if they lead to a dilution of skill mix
or a reduced number of highly skilled staff,
although such strategies are still advocated.
Zaranko et al’s new study is also important
simply because it offers more diversity to
the methods used to demonstrate associations between nurse staffing and patient
outcomes. Although many regard evidence
that staffing levels and skill mix influence
outcomes as statements of the obvious,
questions about causal inference remain for
others, with some senior figures, including
health policy makers, appearing to dismiss
the causal connection.10 Despite the close
alignment between staffing and outcome
data in Zaranko et al’s research, this is an
observational study, as is almost all other
research on this topic. This research is
novel in the way that daily staffing levels
are associated with daily outcomes with
direct linkage between patient outcomes
and team composition. The fact that similar
findings come from diverse study designs
Griffiths P, Dall’Ora C. BMJ Qual Saf 2023;32:241–243. doi:10.1136/bmjqs-2022-015578
241
Editorial
lend increasing weight to a causal interpretation and the
absence of experimental studies can no longer be used to
dismiss evidence such as this as ‘merely showing an association’. Much like the denials of evidence for a causal
association between lung cancer and smoking, maintained by senior figures in the tobacco industry well after
the epidemiological evidence was clear, the proposition
that staffing levels have no causal influence on patient
outcomes seems increasingly absurd.
However, acting on the evidence is more difficult.
In Greek mythology, Cassandra was a Trojan priestess
and prophet, whose true prophecies were fated to be
ignored. Similarly, the evidence on nurse staffing and
patient outcomes has, in many respects, been effectively ignored by policy makers and those in charge
of planning workforce requirements. Outright denial
is rare, but effective action has not been taken, with
inertia seemingly fuelled by a false belief that the
consequences of predicted staff shortages could be
averted. In the UK, a growing shortage of registered
nurses is underpinned by a persistent failure to provide
enough training capacity for the projected demand, in
part supported by an assumption that demand could
be reduced by using more support staff.11 Enquiries
into failings in hospital care have revealed inadequate
nurse staffing as a core factor,12 13 with low registered nurse staffing ‘enabled’ by use of support staff
as a cheaper alternative. The NHS in England uses
a benchmarking approach that equates productivity
with care hours per patient day from registered and
assistant staff combined, compounding the impression
of a degree of equivalence and seemingly oblivious to
the evidence that links skill mix and registered nurse
staffing levels to the quality and safety of care. As we
note below, there is even some evidence indicating that
reducing skill mix reduces productivity.
So, why has the substantive body of research on nurse
staffing led to so little action? In part, it might be due to
national and local decision makers being affected by the
normalcy bias, a cognitive bias that leads people to simply
ignore warnings of imminent threat.14 Perceptions about
limitations of the evidence base have clearly inhibited decisive action in some circumstances. In developing guidance
on safe staffing for England in 2014, the National Institute for Health and Care Excellence noted that evidence
was of insufficient quality to inform decision-making. Yet
many system changes are implemented in health services
with far weaker evidence. For example, while electronic
medical records seem an obvious necessity in modern
healthcare, evidence of clinical or economic benefits from
their implementation is sparse and often contradictory.15
The assumption that future changes in care delivery
will dramatically alter the demand for staff has often
underpinned optimistic appraisals that demand for staff
can be reduced. Technology is often offered as the solution to workforce shortages but evidence to support such
claims is scant and, in many cases, it appears that workload is increased.16 Healthcare is labour intensive and
242
likely to remain so for the foreseeable future. Extraordinary advances in health technology in the modern era
have created opportunities to improve care outcomes but
rarely do they remove the need for people to support the
delivery of care. Improved modes of treatment alongside
better housing conditions and a growing awareness of the
adverse effects of simply being in hospital for a period
of recovery have enabled hospitals to operate with fewer
beds relative to activity but increased acuity of patients
means that more nurses are required to safely staff each
bed.
In many countries, a shortage in supply of registered nurses provides a seemingly compelling case
to search for alternatives and innovation should not
be ruled out, provided it is supported by evidence.
What is less clear, as in the case of perennial failures of
workforce planning in the UK, is whether those who
control the policy levers, be they government departments commissioning training or those setting wages
and working conditions, have ever fully committed to
solving the registered nursing shortage with the one
evidence-based solution we already know of—more
registered nurses. It is unclear why this is the case. In
part, local decision makers may feel powerless in the
face of system supply issues or the pressure of finance
directors to control costs. Certainly, any significant
increase in the number of registered nurses appears
to be potentially expensive for the simple reason that
registered nurses are such a large proportion of the
hospital workforce, and hence, the pay bill.
If the costs of expanding the registered nurse workforce could be a major factor inhibiting action, close
attention has to be paid to the economics of nurse
staffing and the relevant evidence. Generically, there is
evidence that spending on healthcare gives a positive
return on investment through increased population
health, keeping more people economically active, in
addition to the immediate contribution of the spending
power of workers in this labour-intensive sector of
the economy.17 Government spend on healthcare can
therefore make a significant contribution to economic
growth. A position of principle that society simply
cannot afford the additional expense of investing in
nurse staffing must therefore be questioned and should
never be taken as a given. Is nurse staffing the best
investment to make in healthcare? In truth, that is a
hard question to answer, although evidence indicates
a possibility that increases in registered nurse staffing
in acute hospitals may be cost-effective at a level that
makes it a strong candidate for investment, and there
is more evidence that a shift towards a more skilled
nursing workforce could be cost neutral because of
improved patient outcomes and more efficient use
of beds.8 18–20 More research into the economics of
nurse staffing and approaches to determining staffing
requirements (a field distinguished by a staggering
volume of outputs but remarkably little progress21) is
certainly needed, but that should not obscure the fact
Griffiths P, Dall’Ora C. BMJ Qual Saf 2023;32:241–243. doi:10.1136/bmjqs-2022-015578
Editorial
that the current evidence provides some clear priorities for action.
In the 1990s, a compelling case was made by the
evidence-based practice movement that implementing
interventions that were already known to be effective
was likely to provide a better return on investment
than the discovery of novel treatments. Zaranko et
al’s study contributes to a body of evidence that reinforces the same point about staffing health services.
Investment in training registered nurses, including
continuing professional education and developing a
cadre of experienced and skilled senior clinical nurses,
is an evidence-based solution that is likely to provide
good returns. Perhaps it is time to stop looking for
alternatives. It is certainly time to stop implementing
solutions that are likely to be ineffective.
Cassandra prophesised the fall of Troy. With many
now fearing the collapse of the publicly funded NHS
in the UK in the face of staffing shortages that have
been predicted for some time, the message of this
research is that you cannot deliver safe modern healthcare without enough registered nurses, including
senior experienced clinical nurses, on hospital wards.
It is time that those able to make decisions at a local
and national level listened and acted.
Twitter Peter Griffiths @workforcesoton and Chiara Dall’Ora
@ora_dall
Contributors PG and CD both drafted the manuscript,
reviewed it and revised it. Both approved the final version.
Funding This study was funded by NIHR Applied Research
Collaboration (Wessex).
Competing interests Both PG and CD receive funding from
the National Institute for Health Research for research projects
related to nurse staffing.
Patient consent for publication Not applicable.
Ethics approval Not applicable.
Provenance and peer review Commissioned; internally peer
reviewed.
ORCID iDs
Peter Griffiths http://orcid.org/0000-0003-2439-2857
Chiara Dall’Ora http://orcid.org/0000-0002-6858-3535
REFERENCES
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patient outcomes: a systematic review of longitudinal studies.
Int J Nurs Stud 2022;134:104311.
2 Kane RL, Shamliyan TA, Mueller C, et al. The association
of registered nurse staffing levels and patient outcomes:
systematic review and meta-analysis. Med Care
2007;45:1195–204.
3 Shekelle PG. Nurse-patient ratios as a patient safety strategy: a
systematic review. Ann Intern Med 2013;158:404–9.
4 Zaranko B, Sanford NJ, Kelly E, et al. Nurse staffing
and inpatient mortality in the english national health
Griffiths P, Dall’Ora C. BMJ Qual Saf 2023;32:241–243. doi:10.1136/bmjqs-2022-015578
service: a retrospective longitudinal study. BMJ Qual Saf
2023;32:254–63.
5 Twigg DE, Kutzer Y, Jacob E, et al. A quantitative systematic
review of the association between nurse skill mix and nursing-
sensitive patient outcomes in the acute care setting. J Adv Nurs
2019;75:3404–23.
6 Needleman J, Liu J, Shang J, et al. Association of registered
nurse and nursing support staffing with inpatient hospital
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7 Griffiths P, Maruotti A, Recio Saucedo A, et al. Nurse staffing,
nursing assistants and hospital mortality: retrospective
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8 Griffiths P, Ball J, Bloor K, et al. Nurse staffing levels, missed
vital signs and mortality in hospitals: retrospective longitudinal
observational study. Health Services and Delivery Research
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9 Dall’Ora C, Maruotti A, Griffiths P. Temporary staffing
and patient death in acute care hospitals: a retrospective
longitudinal study. J Nurs Scholarsh 2020;52:210–6.
10 Cayton H. Mandating staffing levels is not the answer to
reducing poor care. Health Service Journal 2012.
11 Buchan J, Gershlick B, Charlesworth A. Falling short: the NHS
workforce challenge. London: The Health Foundation, 2019.
12 The Mid Staffordshire NHS Foundation Trust Inquiry chaired
by Robert Francis QC. Independent inquiry into care provided
by mid Staffordshire NHS Foundation trust January 2005 –
March 2009. London: The Stationary Office, 2010.
13 Keogh B. Review into the quality of care and treatment
provided by 14 Hospital trusts in England: overview report:
NHS, 2013.
14 Drabek TE. Human system responses to disaster: An inventory
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15 Reis ZSN, Maia TA, Marcolino MS, et al. Is there evidence
of cost benefits of electronic medical records, Standards, or
Interoperability in hospital information systems? overview of
systematic reviews. JMIR Med Inform 2017;5:e26.
16 Priestman W, Sridharan S, Vigne H. What to expect from
electronic patient record system implementation; lessons
learned from published evidence. Journal of Innovation in
Health Informatics 2018;25:13.
17 Reeves A, Basu S, McKee M, et al. Does investment in the
health sector promote or inhibit economic growth? Global
Health 2013;9:43.
18 Griffiths P, Saville C, Ball JE, et al. Beyond ratios flexible and resilient nurse staffing options to deliver
cost-effective hospital care and address staff shortages: A
simulation and economic modelling study. Int J Nurs Stud
2021;117:103901.
19 Twigg DE, Myers H, Duffield C, et al. Is there an economic
case for investing in nursing care–what does the literature tell
us? J Adv Nurs 2015;71:975–90.
20 Needleman J, Buerhaus PI, Stewart M, et al. Nurse staffing
in hospitals: is there a business case for quality? Health Aff
2006;25:204–11.
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243
© 2023 Author(s) (or their employer(s)) 2023. No commercial re-use. See
rights and permissions. Published by BMJ.
International Journal of Nursing Studies 117 (2021) 103901
Contents lists available at ScienceDirect
International Journal of Nursing Studies
journal homepage: www.elsevier.com/ijns
Beyond ratios – flexible and resilient nurse staffing options to deliver
cost-effective hospital care and address staff shortages: A simulation
and economic modelling study
Peter Griffiths a,b,c,∗, Christina Saville a,b, Jane E. Ball a,b, Jeremy Jones a, Thomas Monks d , On
behalf of the Safer Nursing Care Tool study team
a
Faculty of Environmental and Life Sciences, University of Southampton, Southampton, UK
National Institute for Health Research Applied Research Collaboration (Wessex), Southampton, UK
c
Portsmouth Hospitals University NHS Trust, Portsmouth, UK
d
University of Exeter Medical School, Exeter, UK
b
article info
abstract
Article history:
Received 2 November 2020
Received in revised form 25 January 2021
Accepted 4 February 2021
Background: In the face of pressure to contain costs and make best use of scarce nurses, flexible staff
deployment (floating staff between units and temporary hires) guided by a patient classification system
may appear an efficient approach to meeting variable demand for care in hospitals.
Objectives: We modelled the cost-effectiveness of different approaches to planning baseline numbers of
nurses to roster on general medical/surgical units while using flexible staff to respond to fluctuating demand.
Design and setting: We developed an agent-based simulation, where hospital inpatient units move between being understaffed, adequately staffed or overstaffed as staff supply and demand (as measured by
the Safer Nursing Care Tool patient classification system) varies. Staffing shortfalls are addressed by floating staff from overstaffed units or hiring temporary staff. We compared a standard staffing plan (baseline
rosters set to match average demand) with a higher baseline ‘resilient’ plan set to match higher than
average demand, and a low baseline ‘flexible’ plan. We varied assumptions about temporary staff availability and estimated the effect of unresolved low staffing on length of stay and death, calculating cost
per life saved.
Results: Staffing plans with higher baseline rosters led to higher costs but improved outcomes. Cost savings from lower baseline staff mainly arose because shifts were left understaffed and much of the staff
cost saving was offset by costs from longer patient stays. With limited temporary staff available, changing
from low baseline flexible plan to the standard plan cost £13,117 per life saved and changing from the
standard plan to the higher baseline ‘resilient’ plan cost £8,653 per life saved.
Although adverse outcomes from low baseline staffing reduced when more temporary staff were available, higher baselines were even more cost-effective because the saving on staff costs also reduced. With
unlimited temporary staff, changing from low baseline plan to the standard cost £4,520 per life saved
and changing from the standard plan to the higher baseline cost £3,693 per life saved.
Conclusion: Shift-by-shift measurement of patient demand can guide flexible staff deployment, but the
baseline number of staff rostered must be sufficient. Higher baseline rosters are more resilient in the
face of variation and appear cost-effective. Staffing plans that minimise the number of nurses rostered in
advance are likely to harm patients because temporary staff may not be available at short notice. Such
plans, which rely heavily on flexible deployments, do not represent an efficient or effective use of nurses.
Study registration: ISRCTN 12307968
Tweetable abstract: Economic simulation model of hospital units shows low baseline staff levels with
high use of flexible staff are not cost-effective and don’t solve nursing shortages.
© 2021 The Author(s). Published by Elsevier Ltd.
This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/)
Keywords:
Costs and cost analysis
Computer simulation
Cost savings
Health care economics and organizations
Hospital information systems
Nursing staff
Hospital
Patient classification systems
Personnel staffing and scheduling
Nursing administration research
Operations research
Patient safety
Quality of health care
Safer Nursing Care Tool
Workload
∗
Corresponding author at: Faculty of Environmental and Life Sciences, University of Southampton, Southampton, UK.
E-mail address: peter.griffiths@soton.ac.uk (P. Griffiths).
https://doi.org/10.1016/j.ijnurstu.2021.103901
0020-7489/© 2021 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/)
2
P. Griffiths, C. Saville, J.E. Ball et al. / International Journal of Nursing Studies 117 (2021) 103901
What is already known about the topic?
• Because nursing is the largest staff group, accounting for
a significant proportion of hospitasl’ variable costs, nurse
staffing is frequently the target of cost containment measures
• Staffing decisions need to address both the baseline staff
establishment to roster, and how best to respond to fluctuating demand as patient census and care needs vary
• Flexible deployment of staff, including floating staff and
using temporary hires, has the potential to reduce expenditure while meeting varying patient need, but high use of
temporary staff may be associated with adverse outcomes.
What this paper adds
• Low baseline staff rosters that rely heavily on flexible staff
provide cost savings largely because units are often left
short staffed, leading adverse patient outcomes and increased non-staff costs.
• A staffing plan set to meet average demand is cost effective compared to a plan with a lower baseline.
• A staffing plan with a higher baseline, set to meet demand
90% of the time, is more resilient in the face of variation
and may still be highly cost effective
1. Introduction
In the face of pressure to contain costs and to use nursing staff,
who are in short supply, as efficiently as possible, it is important
to understand how best to plan staffing on hospital units. Key decisions relate to the balance between the baseline staffing level to
routinely roster (schedule) and the use of flexible staffing (floats
and temporary hires) to meet variation in demand caused by variation in census and the needs of patients. The goal is to ensure
that the unit staffing system is able to meet fluctuating demand
while avoiding wasteful use of human resources and the associated
costs. Flexible approaches to staffing deployment to meet variable
demand for care have been advocated as a way of ensuring staffing
levels are maintained in the face of nursing shortages (Aiken et al.,
2013). Some studies have claimed that flexible staffing plans are
superior to fixed plans (Kortbeek et al., 2015) but concerns have
been raised about potential adverse effects on quality from high
use of temporary staff (e.g. Bae et al., 20152010; Dall’Ora et al.,
2019b).
In addressing staff shortages, it is important to test assumptions
about efficient and effective staff deployment. In a recent review of
staffing tools, we found that there is a dearth of evidence about the
performance of staffing methods in practice and, in particular, little evidence of the costs and effectiveness of different approaches
to determining nurse staffing requirements (Griffiths et al., 2020a).
Although there is some evidence that tools in use can measure demand, it is not clear that they identify an optimal staffing level,
nor do tools intrinsically address how to schedule staff in advance
to meet anticipated variation in demand.
Managing staffing to address variation in demand is a major
challenge. Studies have shown substantial variation in demand for
nursing care between different hospital units but also from day
to say within a unit (Davis et al., 2014b; Griffiths et al., 2018;
Van den Heede et al., 2009). Rather than operating with a high
baseline staff to accommodate anticipated peaks, flexible deployment of staff is often assumed to be the most efficient approach to
meeting such variable demand but there is a lack of evidence for
cost-effectiveness or the appropriate balance between the core establishment and flexible deployments (Dall’ora and Griffiths, 2018).
Because the adverse effect of low nurse staffing has been demonstrated in many studies and is now widely accepted, there has
been much focus on mandatory staffing policies and minimum
staffing ratios (Driscoll et al., 2018; Griffiths et al., 2016; Kane et al.,
2007). However, the use of ratios is often considered inflexible and
inefficient (Buchan, 2005) and even when such a policy is in operation the challenge remains to ensure the proper balance between
permanent staff who are rostered in advance, and flexible staffing
in order to maintain the required staffing level as demand varies.
In a previous publication, Saville et al. (2021) we explored
different staffing policies guided by the Safer Nursing Care Tool
(The Shelford Group, 2014). The Safer Nursing Care Tool (often referred to by initials SNCT or as the ‘Shelford Tool’) is a patient classification system. It is used in most English National Health Service Hospitals to guide baseline establishments (that is the number of nurses to employ) and, increasingly, daily staff deployments
(Ball et al., 2019) informing decision about redeployment of staff
between units (floating) or the hiring of temporary staff from the
hospital internal pool (bank) or external staffing agencies. In an
extensive literature review we found no evidence to determine
the cost effectiveness of different ways to use such tools to guide
staffing decisions (Griffiths et al., 2020a).
In our previous study, we used a simulation model to compare
a standard staffing plan, following Safer Nursing Care Tool recommendations, where baseline staffing was set to meet average demand, with two alternatives. Firstly, we considered a staffing plan
in which fewer staff are rostered routinely, where the emphasis
is on the use of flexible deployments, anticipating that most fluctuations in demand would be met by internal redeployment and
use of temporary staff. Secondly, we considered a staffing plan in
which the baseline staff to be rostered is set at a level that is
higher than the mean and is designed to be sufficient to cope with
most peaks in demand. While still using flexible staffing, this plan
emphasised the resilience of the baseline roster in the face of varying levels of demand.
We found that if the number of staff from the permanent establishment rostered on each shift was set at a low level, costs
were reduced, but this apparent efficiency was achieved by leaving many shifts understaffed, largely because of the limited availability of staff to float between units or fulfil short notice requests
for additional temporary staff (Saville et al., 2021). Both the levels
of understaffing and cost savings were highly dependent on our
assumptions about the availability of temporary staff. When more
temporary staff were available, understaffing was less common but
consequently cost savings were much reduced. In this paper, we
consider this further, extending our models to consider the costeffectiveness of the different approaches.
2. Methods
Using data from an observational study in general inpatient
units (wards) of acute care hospitals, we developed a simulation
model of demand for unit based inpatient nursing. We used this
to test various staffing plans guided by the Safer Nursing Care Tool
patient classification system (Griffiths et al., 2020b; Saville et al.,
2021), simulating the staffing levels achieved on each unit and for
each shift in the face of variable demand and variable supply of
staff. We then estimated the costs and consequences of the resulting staffing levels in an economic model, calculating cost per life
saved using estimates of the effects of low staffing on length of
stay and risk of death derived from a recent study (Griffiths et al.,
2018).
2.1. Staffing plans
We considered and compare three staffing plans. In the ‘standard’ plan, a baseline number of staff are rostered (scheduled) to
work on each unit, set at a level designed to meet average demand
P. Griffiths, C. Saville, J.E. Ball et al. / International Journal of Nursing Studies 117 (2021) 103901
3
Table 1
Staffing scenarios tested in the simulation model.
Staffing plans∗
low baseline (flexible)
Standard (reference)
high baseline (resilient)
Staff roster set to meet 80% of average demand measured by the Safer Nursing Care Tool patient
classification system across 20 days, set to provide minimum coverage with high use of flexible staffing.
Staff roster set to meet average demand measured by the Safer Nursing Care Tool across 20 days, as
recommended by the tool guidelines.
Staff roster set to meet 90th percentile of demand measured by the Safer Nursing Care Tool across 20 days.
Designed to meet demand through permanent staff on 90% of days if all rostered staff attend.
Scenarios – variation in temporary
staff availability
No temporary staff
Observed
Higher availability
Unlimited availability
∗
No bank or agency staff available
Empirical availability of temporary staff (1) in
required staffing and 14% (11/81) showed a strong positive skew in
achieved staffing. Only one ward showed a strong negative skew
in achieved staffing. Both required (median 0.7) and achieved
staffing (median 1.1) tended to positive kurtosis, meaning there
were more values in the tails of the distribution (further from the
mean) than expected under a normal distribution, with 52% (42)
of units having kurtosis of >1 for achieved staffing.
The achieved staffing level was lowest for the low baseline flexible staffing plan and highest for the high baseline resilient plan
(Table 2) although differences between the plans reduced as the
assumed availability of temporary staff increased. The achieved
staffing levels and costs for the high baseline staffing plan were
much less sensitive to changes in the availability of temporary staff
than were the other plans.
Staffing plans with higher baseline staff (standard vs low baseline and high baseline vs standard) were associated with higher
costs but shorter lengths of stay and fewer deaths (Table 3). As the
assumed availability of temporary staffing increased, differences in
outcomes and costs between staffing plans reduced. Staffing plans
with higher baseline staffing became more cost effective (less additional cost per improved outcome) as temporary staffing availability increased.
Where the availability of temporary staff was limited (based on
the observed availability), the high baseline resilient staffing plan
increased staffing costs by 5.5% whereas the low baseline flexible staffing plan reduced staff costs by 10.8% compared to standard plan. When the availability of temporary staff was unlimited,
the high baseline plan was associated with a 2.9% increase in staff
costs while the low baseline plan was associated with a reduction
of 1.6%.
However, while lower baseline staffing plans were associated
with reduced staff costs, they were also associated with worse outcomes because the achieved staffing levels were lower, despite the
use of flexible deployments. With limited temporary staff availability, the high baseline resilient staffing plan was associated with a
1.2% reduction in the average length of hospital stay, and a 4.5%
reduction in the relative risk of death, equating to one life saved
for every 665 patients admitted to a hospital (number needed to
treat). By contrast, with the low baseline flexible plan, many shifts
were left critically understaffed, and consequently there was a 1.7%
increase in the average length of hospital stay and an 8.3% increase
in the risk of death, equating to one additional death for every 361
patients.
£ 14,729
£ 10,568
£ 4749
£ 515
£ 12,528
£ 8193
£ 5642
£ 2687
£ 22,013
£ 17,537
£ 7623
£ 1156
£ 24,364
£ 18,404
£ 15,067
£ 11,356
204
296
305
283
632
627
703
766
−14.6%
−10.2%
−11.1%
−15.1%
−2.4%
−1.7%
−0.8%
−0.3%
−1.4%
−1.2%
−1.0%
−0.9%
−4.7%
−4.7%
−4.4%
−4.1%
£ 13,155
£ 8653
£ 6451
£ 3693
£ 23,936
£ 21,766
£ 20,422
£ 10,141
£25,584
£ 19,437
£ 17,230
£ 15,612
222
361
719
2828
663
665
873
1272
−13.4%
−8.3%
−4.4%
−1.9%
−4.5%
−4.5%
−3.8%
−3.0%
−2.4%
−1.7%
−0.8%
−0.3%
−1.4%
−1.2%
−1.0%
−0.9%
The outcomes for the low baseline flexible staffing plan were
more sensitive to the availability of temporary staff than were
those for the resilient plan. If no temporary staff were available,
then deaths were increased by 13.4% with a flexible staffing plan
(relative to the standard plan), whereas with unlimited temporary
staff availability deaths were increased by only 1.9%. For the resilient plan the equivalent range reduction in death ranged from
4.5% with no temporary staff availability to 3% with unlimited temporary staff.
Compared to the low baseline flexible plan the standard staffing
plan staff cost £ 21,766 per life saved when availability of temporary staff was limited. Much of the additional staff cost is offset
by the value of reduced hospital stays. The net cost per life saved
was £13,117. Similarly staff costs per life saved associated with the
higher baseline resilient plan were £19,437 (compared to the standard plan). More than 50% of this cost was offset by the value of
the reduced length of stay, leading to a net cost of £9506 per life
saved.
Although the adverse effects of plans with lower baseline
staffing were reduced with higher temporary staff availability, the
relative cost-effectiveness of higher staffing was more favourable
under these circumstances. For example, with unlimited temporary staff availability the net cost per life saved for the standard
plan relative to the flexible plan is only £4250, while the cost per
life saved for the resilient plan relative to the flexible plan was reduced to £3963.
Our primary analysis assumed that temporary staff are as effective as permanent staff and so adverse outcomes result from understaffing alone. There is some evidence that high levels of temporary staffing can have an adverse effect on patient outcomes and
so we also considered these additional adverse effects of temporary staffing on mortality (see Table 3). The estimated mortality
associated with lower baseline staffing was increased when considering an adverse effect from high levels of temporary staff. In the
primary analysis higher availability of temporary staff tended to reduce the difference between plans and mitigate the adverse effects
of lower baseline staffing. This was not the case when an adverse
effect of high temporary staff was included. When unlimited temporary staff were available the low baseline flexible staffing plan
was associated with a 15.1% increase in mortality compared to the
standard plan.
Consequently, if a negative effect from high temporary staff was
assumed, the cost effectiveness of higher baseline staffing was further improved, particularly when comparing standard staffing to
the low baseline ‘flexible’ staffing plan and when temporary staff
availability was higher. For example, the net cost per life saved for
the standard plan compared the low baseline plan was only £515
per life saved (compared to £4250 in the primary analysis with no
adverse effect from high temporary staff).
19.9%
10.8%
5.5%
1.6%
19.9%
10.8%
5.5%
1.6%
7.8%
5.5%
4.0%
2.9%
7.8%
5.5%
4.0%
2.9%
3.1. Sensitivity analyses
Low staffing effects
only
Temporary staff
availability
None
Limited
Higher
Unlimited3
Including temporary
staffing effects
Temporary staff
availability
None
Limited
Higher
Unlimited3
Comparison
High base
(resilient) Vs
Standard
High base
(resilient) Vs
Standard
High base
(resilient) Vs
Standard
High base
(resilient) Vs
Standard
Comparison
Standard vs
Low base
(flexible)
High base
(resilient) Vs
Standard
High base
(resilient) Vs
Standard
Comparison
Standard vs
Low base
(flexible)
Comparison
Standard vs
Low base
(flexible)
Comparison
Standard vs
Low base
(flexible)
Comparison
Standard vs
Low base
(flexible)
Net cost / life
Staff cost / life
NNT (NNH)
Death
Bed days
Staff cost
Table 3
Changes in Costs, effects and cost-effectiveness for standard vs low baseline (flexible) and high baseline (resilient) vs standard plans for varying levels of temporary staff availability.
£ 16,015
£ 13,117
£ 12,722
£ 4520
P. Griffiths, C. Saville, J.E. Ball et al. / International Journal of Nursing Studies 117 (2021) 103901
Standard vs
Low base
(flexible)
6
We undertook a series of sensitivity analyses. Although the pattern of results was largely unchanged by variation in model parameters, the magnitude of differences between staffing plans was sensitive to core parameters in the model, although these differences
were generally unlikely to change substantive conclusions about
cost effectiveness. Table 4 illustrates this by showing the change in
net cost per life saved for the high baseline resilient staffing plan
relative to the standard plan for limited and unlimited availability
of temporary staff associated with alteration of some core parameters.
The most significant sensitivity was the estimated effect of low
staffing on mortality. Taking the upper bound of the 95% confidence interval for the mortality effect considerably reduced the
cost per life saved, whereas taking the lower bound increased it.
P. Griffiths, C. Saville, J.E. Ball et al. / International Journal of Nursing Studies 117 (2021) 103901
7
Table 4
Effect of changing model parameters on net cost per life saved for high baseline resilient vs standard staffing plan with
limited and unlimited availability of temporary staff.
Parameter alteration
Mortality estimate at upper 95% CI
Mortality estimate at lower 95% CI
Additional bed day cost + 25%
Cost of agency staff + 25%
Cost of agency staff=cost of bank staff
Cost of bank staff=cost of permanent staff
No floating of staff between units
All costs increased by 25%
Change in cost per life saved estimate
Limited temporary staff
Unlimited temporary staff
-£3705
£18,377
-£2729
-£870
£348
-£233
£2477
£2377
-£2187
£11,019
-£2736
-£4312
£1724
-£1105
£2146
£1380
With limited availability of temporary staff net cost per life saved
was increased by £18,377 if the low bound estimate of the effect
on mortality was used.
In our original model, we assumed that bank staff were cheaper
than permanent staff (because pension costs were reduced) and
that agency staff were paid at the rate capped by NHS Improvement guidance. Changing these assumptions, including changing
the assumptions so that bank and agency staff had similar costs
(equivalent to sourcing all temporary staff from the bank) made
most difference when unlimited availability of temporary staff was
assumed and only small differences when availability was limited.
A 25% increase in agency staff costs substantially reduced the cost
per life saved associated with higher baseline staffing when temporary staff availability was unlimited.
If no floating of staff from overstaffed units to understaffed
staffed units was permitted, the cost per life saved associated with
the resilient staffing plan increased, although the magnitude of the
difference was small relative to the impact of assumptions about
the availability of temporary staff (see supplementary material Appendix 1).
The main model results are based on the unweighted average
of models built with data and unit configurations of three different hospitals. Individual hospital results varied in their magnitude
but the pattern of results for the relative costs or effects of different staffing plans and the impact of varying availability of temporary staff was generally consistent (supplementary material Table 9). However, for one hospital, the staff costs under the flexible
staffing plan were marginally more expensive than for the standard plan if there was unlimited availability of temporary staff. In
all cases the net cost per life saved for the resilient staffing plan
was
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