Recruitment freezes are beginning to take hold in the health service but fewer nurses per patient may impact negatively on standards of care
In this article…
- The association between mortality and low nurse-to-patient ratios
- The impact of staff ratios on job satisfaction
- The argument for legislation on nurse-to-patient ratios
- Systems for assessing ideal staffing levels
Jennifer Patterson, senior sister in critical care outreach, Princess Alexandra Hospital, Harlow.
Patterson J (2010) The effects of high patient-nurse ratios. Nursing Times; 107; 2, early online publication.
This article examines the literature on nurse-to-patient ratios to establish the impact on both patients and staff of understaffing on hospital wards. It discusses theories on ideal staff-to-patient ratios and the resource implications of these, and also recommends a number of dynamic and innovative ways to allocate staff.
Keywords Staff-to-patient ratios, Job satisfaction, Burnout
- This article has been double-blind peer reviewed
5 key points
- A computerised system would show whether wards need extra staff and also indicate why a ward’s dependency has increased.
- A group of “floating” staff can be employed to move from ward to ward to help out in busy periods.
- Staffing should to be organised with a view to long-term priorities.
- The cost of additional staff can be offset against savings from improved patient outcomes such as reduced inpatient stays.
- Identifying non-clinical tasks that can be allocated to none nursing staff is one way of reducing the effects of low nurse-to-patient ratios.
Working at several NHS trusts across England has given me a rich tapestry of experience - most of which I consider to be positive. However, there have been times when I have felt my work compromised by what I saw as staff shortages.
In contrast, working in Australia I found lack of staff was rarely an issue. My experience of working in such different environments prompted me to consider whether the NHS is really understaffed, and if this affects the way we treat our patients. I also wondered whether this perception of understaffing was unfounded, and if my negative experiences were being influenced by other reasons. I believed a review of the current literature would help me to better understand the relevance of poor staffing, and if staffing within the NHS is considered suboptimal.
More nurses are becoming concerned that the increasing demands on the NHS cannot be met by its current workforce. RCN members have said they are apprehensive over the difficulties of providing and guaranteeing safe, high-quality nursing care (RCN, 2003).
I conducted a literature search to establish if high patient-nurse ratios cause negative outcomes for patients and nursing staff, based on evidence. Much has been written anecdotally about high nursing workloads and their connection with poor patient outcomes, but in terms of large, well-constructed studies there is relatively little evidence.
A number of studies within the last 10 years have shown strong associations between patient mortality and low staffing levels. Aiken et al (2002) looked at not only how insufficient staff affected patient care, but also how it impacted on nurses’ job satisfaction and burnout. The US study examined patient outcomes both in terms of mortality - defined as patients who died within 30 days of admission - and “failure to rescue” – death in patients who developed serious complications, again within 30 days of admission. This large-scale study across several hospitals was focused on surgical patients in general wards.
Its findings were significant and shocking. The study looked at nurse-to-patient ratios, which were between 1:4 at best and 1:8 at worst. Aiken et al (2002) concluded that patients on wards with the worst staffing ratios had a 31% increase in mortality.
As well as negative patient outcomes, Aiken et al 2002 also demonstrated that nurses working in the worst staffed settings suffered the most job dissatisfaction. Those with the highest patient caseloads were more than twice as likely to experience job-related burnout, and almost twice as likely to be dissatisfied with their jobs.
Maben et al (2007) conducted a study among newly qualified nurses in the UK. The survey found that nurses often began their career with strong ideals, but that after just two years many were disillusioned with the profession, and the standards they had started with felt compromised and were difficult to uphold. “Burnout” was common, and many nurses changed jobs or felt compelled to leave the profession altogether. If better staffing levels could alleviate stress and allow nurses to sustain their ideals with regard to the standard of patient care, burnout and dissatisfaction could be reduced.
Needleman et al (2002) came to similar conclusions about the link between failure to rescue rates and lack of nursing staff in the US. This study agreed with Aiken et al (2002), that low staffing levels are likely to lead to negative patient outcomes.
Other authors (Gurses and Carayon, 2007; Kane et al, 2007) have identified that nurse-to-patient ratios are too crude a measure of workload. Kane et al (2007) found that nurses’ daily working hours did not accurately reflect the time they spent with patients or doing clinical tasks. Activities such as attending meetings, completing educational days or administrative work are not accounted for.
According to Gurses and Carayon (2007) other factors, such as the physical work environment and communication between multidisciplinary team members, will also have a positive or negative impact on patient outcomes This links in with the idea that hospitals with low staffing levels may also have a poor physical work environment and a lack of resources other than staff. This will result in poor patient outcomes and makes it difficult to set an arbitrary nurse-to-patient ratio.
There has been little robust research on the relationship between hospital mortality and staffing levels. In 2006, Rafferty et al produced a UK study based on the same model as that used by Aiken et al (2002) in the US. Although the UK varied more dramatically in terms of how understaffed some of its hospitals were, with some trusts in the study demonstrating levels as low as one RN to nearly 15 patients, patient mortality was similar to the US study.
Hospitals with the worst staffing had an increased mortality of 26% and 29% in the “failure to rescue” group. The nurses in the hospitals with the worst staffing were up to 92% times more likely to show job dissatisfaction and burnout, and also rated the quality of care on their wards as “low” or “deteriorating” (Rafferty et al, 2006). The findings suggested that if all 30 NHS trusts studied had had the nurse-to-patient ratio of around 1:7 (the best found in the study) around 246 fewer deaths would have been seen.
Unfortunately neither the US studies nor the one undertaken in the UK suggested what an optimal nurse-to-patient ratio is, although they allude to 1:4 providing the best patient outcomes.
What the studies did agree on is that the more patients a nurse is allocated, the worse the patient outcome is likely to be. None of the studies include any information about the teams outside of the nursing workforce (which would greatly complicate the study designs) and the impact this may have on patients and nurses. For example, low doctor-to-patient ratios may have a similarly negative effect on patient outcomes, and affect the stress levels of the nursing workforce. There are also no studies which demonstrate an intervention, nor are there controlled trials. All the studies are observational, collecting data on existing situations without intervening (Kane et al, 2007).
It is important to note that not all studies have been concerned with negative patient outcomes simply in terms of mortality. Others focus on patients who develop unexpected complications but survive to discharge. Studies have found associations between poor staffing levels and urinary tract infections (Needleman et al, 2002), hospital acquired pneumonias (Kovner and Gergen, 1998) and extended length of stay (Needleman et al, 2002) among other adverse events. Not only do these have implications for patient safety and wellbeing, but there is also a huge cost implication.
Supporting safe staffing levels
So, if there is growing body of evidence to support the idea that low staffing levels lead to increased patient mortality and adverse events, what is being done to support safe staffing levels?
In California, US and Victoria, Australia there is now legislative backing for safe staffing levels for RNs. The Victoria agreed levels are shown in Table 1 (RCN, 2003).
The Victoria ratios are set out in terms of Group A hospitals and Group B hospitals. A Group A hospital is considered a large metropolitan hospital equivalent to a city or teaching hospital in the UK. A Group B hospital is more rural, but still of a reasonable size and more on a par with a district general hospital. It is not possible for them to be directly comparable as there is a considerable difference between hospital and population size in Australia compared with the UK. However, it can give a guide.
In California the ratio has been set at 1:5 for medical and surgical wards, with an eventual aim of 1:4; this applies to public and private hospitals. In Victoria the forced ratios only apply to public hospitals. Less information was available about the breakdown, for example with regard to night shift staffing for the US legislation.
There is good evidence that nurses’ self-esteem has been boosted since the introduction of fixed staffing ratios. More have returned to practice and recruitment and retention have increased across Victoria (O’Connor, 2006). The Australia Nursing Federation (ANF) has suggested many nurses would now consider quitting, retiring early or cutting their hours if protected nurse-to-patient ratios were abolished. Similarly, The California Nurses Association (CNA) spoke favourably about fixed nurse to patient ratios, and suggested that nurses are far happier with the care they are able to provide with the improved staffing levels.
Work has yet to begin with regard to in-hospital mortality since the introduction of fixed staffing levels. Much more needs to be done to demonstrate that an increase in staffing alone improves patient safety. Most studies highlight that the way data is currently gathered in this area is inconsistent. Much of it draws the same conclusions - that low staffing leads to adverse outcomes for patients, as well as poor staff morale. However, interpreting existing data is difficult because of the lack of consistent methodology used, so future research needs to follow more concise guidelines to make evidence from different studies, hospitals and countries comparable. This would help to add weight to the current conclusions.
The current staffing deficit
Many perceive that the NHS is understaffed, but compared with an ideal how much of a deficit is there? It is difficult to establish from publicly available information exactly how poorly staffed UK hospitals are, because it is difficult to discover the average number of staff allocated per patient within UK hospitals. Since there is no mandatory staffing levels, decisions can be made at trust level and therefore will vary from trust to trust and even ward to ward. Even if a “safe” level has been set, there is no information available as to how each trust’s staffing compares with its desired levels.
What can be drawn from current information is that RNs work an average of six hours per week unpaid, according to the RCN (2003). These six hours add up to 312 per year per registered nurse and could be the result of them not being able to take breaks, or having to work after a shift has ended. This has huge implications if nurses worked to rule.
Table 2 shows how between one and six hours of unpaid overtime worked by registered nurses equates to hours, equivalent cost and nurses needed to fill the gap if the profession worked to rule. It is important to remember that while employing nurses to cover the deficit in unpaid overtime would allow nurses to take breaks and leave work on time, it would not actually improve the service because we need a further boost to the number of registered nurses.
The figures are based on 322,425 full-time equivalent (FTE) registered nurses on mean basic salaries of £29,200. These numbers are provided by The NHS Information Centre for Health and Social Care (salary for October to December 2009).
I also recognise that the original RCN (2003) survey, although sizable did not interview all NHS RNs, so the six hours of average overtime may or may not be a true reflection. However, the survey had some 16,000 respondents, which should represent a good cross-section of the nursing population. Even a conservative estimate of half of the six hours worked equates to some 25,794 additional nurses.
We know from the Rafferty et al (2006) study that some UK nurses look after as many as 15 patients - more than three times the recommended levels set out by the ANF and CNA. This is also more than three times the levels suggested as optimal by Aiken et al (2002). The more the figures are broken down, the more the deficit appears to spiral out of control. The figures suggest that some trusts may have to as much as double - if not more - their RN FTEs to meet a safe staffing level.
Given that the NHS does not have an infinite budget and increasing the staff levels up to threefold would be somewhat improbable, where does the service go from here?
The first area that needs to be tackled is how information is gathered on how nursing time is spent. Several computerised systems exist which create dependency scores for wards; these translate into suggested person hours or staff needed to care for current patient workloads (RCN, 2003). These systems have been criticised for assuming all nurse tasks are patient or clinically related; they do not take into account time spent on such as answering phones, talking with families, or disseminating information to multidisciplinary team members. For these systems to truly reflect a nursing day and how many hours are used within a shift for all tasks, they need to be sophisticated enough for every activity to be inputted.
The Association of UK University Hospitals (AUKUH) acuity/dependency tool also equates patient dependency to staff hours. It is a clinically relevant tool for scoring patients’ acuity and is based around evidence from the Department of Health (2000). Originally scores were given for patients in critical care and this has been adapted for ward use. Ward patients fall into one of four categories: level 0, level 1, level 1a and level 1b. Above this, level s2 and 3 patients should be nursed in an appropriate high dependency or intensive care unit. The lowest acuity score accounts for “regular” ward patients who need what is considered routine care. Each level above this is concerned with more dependent patients who will require more nursing hours. For example, a patient needing complex drug regimens would fall into level 1b and needs the equivalent of 1.86 FTE nurses per bed.
The document provides FTE nursing requirements for all levels of dependency - this can be translated into the number of nurses needed for the entire contingency of patients on any given ward. Over time this kind of data can be collated to identify staffing needs and how they may change seasonally.
Again, making allowances for non-clinical tasks with this system seems problematic. If “all time needed” is calculated on the basis of patients’ needs and acuity, how is time for non-clinical tasks - such as answering phones and study leave – to be accurately assessed?
It should also be noted that simply completing an acuity score that reflects the ward needing a particular number of nurses does not ensure their allocation. When future data is being interpreted, whether or not wards are allocated all the staff they require should be studied, to highlight any deficits in the nursing workforce.
Implications for practice
While we need to aim for safe staffing levels, allocating staff could become a more dynamic, innovative process. For example, by collecting more evidence, a baseline safe staffing level could be arrived at for general wards.
A computerised system may reveal on any given day whether a ward will need an extra staff member. A sophisticated system would also tell the user why the dependency of a ward has increased. Often busy periods are not uniform across the day: ward A may be hectic in the morning because there are three patients dependent for hygiene needs; ward B may be busy in the afternoon because there are two routine blood transfusions to run.
A hospital could employ a group of “float” staff who, rather than staying allocated to a particular area, will move from ward to ward to assist in these busy periods. This can be agreed from the beginning of the day because the computer system will have predicted what need is where. So as not to deskill ward nurses, the float nurses would not always need to be responsible for the task - such as the complex dressing - but could cover other tasks on the ward during that period. This idea does not excuse the need for mandatory staffing levels. However, a sophisticated allocation system and experienced ward managers would recognise that although a 20-bed ward may have five nurses, this does not mean a 1:4 split - one nurse may be with two seriously ill patients, while another cares for six who are less ill (O’Connor, 2006). UK hospitals would struggle to meet a 1:4 ratio in the near future, but with float staff hospitals could explore the idea that when a ward needs assistance extra staff can be present – but not necessarily for an entire shift.
Articles have been written (Browne and Odell, 2004; Gainsbury, 2009) about how the dilution of skill mix can influence patient outcomes, however this article does not have the breadth to cover a discussion on skill mix at length. The general idea is that skill dilution can also have a negative impact on patient outcomes (Carr-hill et al, 1992) but not all studies agree on this (Meyer and Spilsbury, 1998).
Increasingly there are fewer trained staff and more healthcare support workers (HCSW) on general wards, and much of the hands-on patient care is being undertaken by staff who are not RNs. More research is needed on the effects of diluting skill mix. If nurse-patient ratios are set at a mandatory level, it stands to reason that there also needs to be a gold standard for RN skill mix - namely band 5 to band 7 - and also an RN to HCSW ratio. Currently the RCN (2003) recommends a 65:35 ratio of RNs to HCSWs. This is figure is supported by the Health Care Commission (2005) but the DH offers no ratio recommendation (Gainsbury, 2009).
Other staff such as levels of doctors, physiotherapists, occupational therapists, specialist nurses, and nutritionists will play some part in patients’ care. Further studies could be conducted on the multidisciplinary team as a whole, and how the understaffing of branches of it will affect patients and other staff. Medical staff in particular have been identified by other studies as needing scrutiny. Similar studies to that undertaken by Aiken et al (2002) could look at doctor-to-patient ratios and the effects, rather than nursing ratios.
Many non-staffing issues also lead to adverse outcomes for patients and low morale for staff, and these also need to be addressed in further research. Poor access to supplies, a dysfunctional work environment, and highly dependent family members, are all things which can negatively impact on nurses’ workload (Gurses and Carayon, 2007). Some are difficult to change without significant investment, but simple changes could be made to improve others. Streamlining the way wards operate so all services are efficient is extremely important, particularly where understaffing is an issue.
Studies into time spent on mandatory but non-clinical tasks would be useful. They may highlight some activities that need not be undertaken by clinical staff and could be redirected towards, for example clerical staff, freeing nurses for patient-focused tasks.
I recognise that several of the studies I have discussed have been undertaken outside the UK, where service delivery and client groups may vary. The size and allocation of healthcare budgets may be significantly different– particularly in countries where the private sector provides the majority of healthcare. Given that many of the studies are also somewhat dated, we need more current UK research into the effects of staff-to-patient ratios and skill mix in order to draw more concrete conclusions.
Although providing additional staff would be costly, the difference it could make to patient outcomes could offset this expense, for example by reducing inpatient stays. Recruitment and retention would almost certainly improve and in the long run this should reduce spending. Staffing needs to be considered over the long term.
With increasing shortfalls in staff there is little hope of achieving excellence in standards of care. The difficulty in providing adequate care is becoming apparent, and the likelihood of patients experiencing adverse outcomes will increase as staffing is continually compromised. The warning as budget cuts and recruitment freezes loom is stark -the fewer registered nurses per patients, the higher the chances of adverse outcomes, increased mortality and staff exhaustion.
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