Elsevier

Applied Nursing Research

Volume 24, Issue 4, November 2011, Pages 244-255
Applied Nursing Research

Original Article
Nursing staffing, nursing workload, the work environment and patient outcomes

https://doi.org/10.1016/j.apnr.2009.12.004Get rights and content

Abstract

Nurse staffing (fewer RNs), increased workload, and unstable nursing unit environments were linked to negative patient outcomes including falls and medication errors on medical/surgical units in a mixed method study combining longitudinal data (5 years) and primary data collection.

Introduction

The New South Wales (NSW; Australia) Health Department commissioned a study in 2003 to help the government identify strategies for improving the effectiveness and efficiency of nurse staffing in its hospitals. Building on international research in this area, the investigators designed a study to examine the relationship of nurse staffing and workload, in the context of the work environment, to patient outcomes at the unit level (Duffield et al., 2007). We took advantage of a rich administrative data depository in NSW and combined it with primary unit-level data collection. At the time the study was designed, there were only a few small unit-level studies representing efforts to link nurse staffing, workload, the working environment, and patient outcomes in some configuration.

The methodological conundrum in finding data to measure these concepts is that data to bring them all together potentially exist only at the individual hospital level. In general, single sample studies are a weak source of evidence for public policy making. This is a problem if the purpose of the study is to contribute to health policy, as was the case here. Researchers in the United States have discovered that nursing resource data (staffing, skill mix, nurse/patient ratios) have to be estimated from publicly available sources that are sometimes questionable (Kovner and Gergen, 1998, Needleman et al., 2001, Needleman et al., 2002). Further, some aspects of workload such as patient turnover cannot be obtained except at the local level, and although casemix (Diagnosis-Related Groups [DRGs]) as a component of workload is a feature of administrative datasets, it is never available in concurrent data at the unit level because DRGs are only assigned after discharge. The unit working environment is impossible to measure directly except with primary data collection.

Patient outcomes can be measured in large administrative datasets as mortality or length of stay (LOS). Needleman et al., 2001, Needleman et al., 2002) used the International Classification of Diseases (ICD) coding in publicly available administrative data to define clinical Outcomes Potentially Sensitive to Nursing (OPSN). However, adverse outcomes of special interest to nurses, such as falls and medication errors, cannot be obtained in most large administrative datasets to any reliable degree. Although country, state, and hospital level analyses have been useful, the unit is the place where the effects of government or institutional decisions about the allocation of resources come to rest. Unit-level data are generally not available in large administrative datasets. For this study, we were able to combine longitudinal retrospective analysis of the NSW administrative data with cross-sectional primary unit-level data collection from nurses and patient/unit records. The focus was on medical/surgical nursing units (in Australia referred to as wards), which is where most hospital nurses work and which have been relatively understudied.

There are a number of conceptual frameworks that link nursing as a resource, skill mix and/or nursing education, to nurse or patient outcomes through some kind of pathway that includes the working environment (e.g., Aiken et al., 1997, Leiter and Laschinger, 2006, O'Brien-Pallas et al., 2001, Tourangeau et al., 2007). To guide this study, we built on the Patient Care System Model (O'Brien-Pallas et al., 2001, O'Brien-Pallas et al., 2002, O'Brien-Pallas et al., 2004) because it includes nurse variables (staffing, skill mix, job satisfaction), workload (patient acuity, patient turnover, LOS), and the working environment (including environmental complexity) believed to impact on patient outcomes. The model does not predict exact chains of relationships among these concepts but was useful for organizing primary data collection and analysis. Because linking administrative data with primary data on the unit working environment to our knowledge had not been done previously, we viewed the study as exploratory.

Research in this field has reached the point at which there are systematic literature reviews (Dall et al., 2009, Kane et al., 2007) and other collections of studies (Hyun et al., 2008, Unruh, 2008) of relationships among various combinations of nurse staffing (numbers and skill mix), workload, work environment, and patient outcomes. Kane et al. (2007) concluded that evidence on the positive effect of higher proportions of RNs on patient outcomes in intensive care and surgery was strong and consistent. Higher RN staffing was associated with less hospital mortality, failure to rescue, cardiac arrest, hospital-acquired pneumonia, and fewer other adverse events. Findings were stronger in unit-level studies than hospital-, state-, or country-level studies. They concluded that future research should include additional factors such as the organization of nursing units and staff, patient characteristics, and medical practice patterns in large multicenter studies. They did not mention workload components or turnover. Dall et al. (2009) concluded that when nurse staffing levels increase, risk of nosocomial complications, ranging from sepsis to falls and including failure to rescue and LOS, are decreased. Workload and the working environment were not considered. In a meta-analysis of studies of critical care units, Numata et al. (2006) concluded that nurse staffing levels did not affect hospital mortality but noted that measuring exposure status (how long patients had been in the intensive care unit [ICU] and therefore how much mortality could attach to ICU stay) was a methodological challenge. Neither workload nor the unit working environment were discussed.

Studies of legislatively imposed minimum nurse staffing ratios have been reviewed by Lang, Hodge, Olson, Romano, and Kravitz (2004), who concluded that there is no support for minimum nurse–patient ratios for acute care hospitals, especially when skill mix and patient casemix are not considered. Total nursing hours did “appear to affect some important patient outcomes” (p. 326). There is no direct measurement of workload or the working environment in the studies reviewed because the authors assume that a more favorable (lower) nurse–patient ratio implicitly creates a more positive workplace out of either equity of patient assignment or adequate staff.

In the State of the Science paper on nursing performance measurement, Needleman, Kurtzman, and Kizer (2007) concluded that the Nursing Quality Forum measures are a good beginning; there is much more to be done to develop measures that speak to nursing and to stakeholders such as consumers; purchasers; and, we would add in an international context, government. They do not specifically mention workload or the working environment. In addition to these reviews, there is substantial research evidence that supports relationships among nurse staffing, workload, working environment, and patient outcomes, although the terminology used among studies in this area is not standard. Table 1 shows the major concepts of interest and the relevant variables discussed in this article. The placement of some measures in categories or data levels is sometimes arbitrary. For instance, skill mix could be in the workload column if a lower percentage of RNs increases their workload through the necessity for supervision of other staff; patient outcomes can be collected in primary data collection including chart review but are also available in administrative data, albeit with questions of reliability and validity.

Lower levels of RN staffing are associated with higher rates of OPSN—urinary tract infections, pneumonia, shock and cardiac arrest, upper gastrointestinal (GI) bleeding, failure to rescue—and length of hospital stay (LOS) in both medical and surgical patients treated in hospitals in U.S. Medicare and other publicly available administrative data (Needleman et al., 2001, Needleman et al., 2002). These researchers creatively used administrative data, such as ICD-9 codes and DRGs, to define clinical outcomes beyond mortality. Similarly, Kovner and Gergen (1998) found a significant inverse relationship between number of RNs per adjusted patient day and urinary tract infections and pneumonia after surgery. Both studies used American Hospital Association (AHA) data for nursing staffing. AHA data come from hospital self-reports and cannot be tracked to the nursing unit. In addition, AHA has changed its definitions of nurse staffing from time to time to include or not include unlicensed personnel, depending on the agenda of the Association at the time.

Increased skill mix (%RN) but decreased nursing hours per patient day (NHPPD) in the presence of the “chaotic” (p. 1140) work environment created by health care reform in New Zealand (NZ; including decreased LOS) created significant increases in the 11 OPSN measured (McCloskey & Diers, 2005). Nursing data came from a questionnaire attached to annual practice certificates required by the NZ government, and the policy context provided an external source for measuring the working environment.

Tourangeau et al. (2007) found that a 10% increase in nurse-reported adequacy of staffing was associated with 17 fewer deaths per 1,000 discharged patients. A United Kingdom study showed that hospitals with the highest patient-to-nurse (RN) ratios had 26% higher mortality (Rafferty et al., 2007). Staffing data were obtained from a survey of clinical nurses aggregated to the hospital level. Aiken, Clarke, Cheung, Sloane, and Silber (2003) showed that surgical patients experienced lower mortality and failure to rescue rates in hospitals with higher proportions of nurses educated at the baccalaureate level or higher. In an examination of skill mix at the nursing unit level, Canadian researchers (McGillis-Hall, Doran, & Pink, 2004), using staffing data collected by a survey of the managers of 77 units in Ontario, concluded that a higher proportion of RNs on medical and surgical units was associated with positive outcomes such as lower rates of medication errors and hospital-acquired infections.

Finally, the study by Sales et al. (2008) on the U.S. Veterans Administration data found that although RN staffing was not associated with in-hospital mortality for patients with an ICU stay, it was for non-ICU patients; increased RN staffing was associated with decreased mortality risk. These authors note that the hospital level data did not consider the mix of patients at the unit level but did include actual staffing hours.

In summary, researchers in several countries have found that increased nursing hours and a richer skill mix (more RNs) improve patient outcomes. Most of this work has been undertaken at the hospital rather than the unit level and has used estimates or self-reported staffing.

Measures of workload as used in the literature includes characteristics of patients (e.g., casemix) and patient turnover (Unruh & Fottler, 2006), as well as patient acuity/intensity (Graf et al., 2003, Hyun et al., 2008). Aiken, Clarke, Sloane, Sochalski, Busse, et al. (2001) surveyed nurses in five countries and found that one result of increased workload was that basic nursing interventions were left undone. Being unable to provide the required level of patient care was linked to lower job satisfaction and staff retention.

Staff shortages and the resulting increased workload have led to concerns about the quality of health care provided to patients in surveys of nurses and as reported in literature reviews (Aiken et al., 2001, Haberfelde et al., 2005, Lankshear et al., 2005). In the UK, Adams, Lugsden, Chase, Arber, and Bond (2000) found that when skill mix was decreased, nurses reported increased intensity of work with constant heavy workloads, significant role changes and pressures to broaden the range of nursing skills, and more overtime. However, workload pressures and inadequate staffing can sometimes be offset by a positive team nursing environment within a unit (Sexton et al., 2006). Workload was not directly measured in these studies.

O'Brien-Pallas et al. (2004) collected the hours of care required for patients at the unit level with the PRN-80 (Programme de Recherche en Nursing; Chagnon, Audette, Lebrun, & Tilquin, 1978) and divided it by nurse worked hours to create a new metric of workload: nursing demand/supply (also referred to as nursing productivity/utilization). In that study of six Canadian hospitals, it was found that when nursing demand/supply levels exceeded 80%, the number of negative outcomes increased not only for patients but also for nurses and hospitals. In a study using administrative data from Pennsylvania hospitals, Unruh and Fottler (2006) calculated indices of patient turnover using LOS data at the hospital level over time and AHA survey data to determine patient days of care. They found that using nurse-to-patient ratios underestimated nursing workload and overstated RN staffing levels. They concluded that patient turnover should be included in assessing workload in future research.

At the unit level, casemix (e.g., higher number of DRGs by nursing unit) has been associated with longer LOS (Diers and Potter, 1997, Duffield et al., 2009). Number of “off-service” patients (Czaplinski & Diers, 1998) has been associated with more mortality and longer LOS. Patient age and complexity of patients (e.g., alcohol withdrawal, confusion, tracheostomy; Beglinger, 2006) have been used to justify nurse staffing.

Aiken's canonical study of Magnet hospitals, known as “good places for nurses to work” (Aiken, Smith, & Lake, 1994), used U.S. Medicare data to link the working environment and patient outcomes. There was a 4.6% decrease in mortality comparing Magnet with matched non-Magnet hospitals.

A number of investigators have used the Revised Nursing Work Index (NWI-R; Aiken and Patrician, 2000, Kramer and Hafner, 1989) as a measure of the work environment. The NWI-R grew out of the Magnet Hospital research and measures nurse autonomy, control over practice, nurse–doctor relations, nursing leadership, and resource adequacy. Units with higher subscale scores demonstrated higher patient satisfaction (Vahey, Aiken, Sloane, Clarke, & Vargas, 2004), lower nurse emotional exhaustion (Leiter & Laschinger, 2006), and lower incidence of needlestick injuries (Clarke, 2007). In the one study using NWI-R at the unit level (Boyle, 2004), nurses' perceptions of control of practice, autonomy/collaboration, and continuity/specialization were associated with a lower incidence of urinary tract infections, pneumonia, cardiac arrest, and shorter LOS. Higher levels of nurse manager support were associated with lower rates of pressure ulcers and mortality, yet higher rates of failure to rescue. Improved collegial relationships between nurses and doctors, along with better educated nurses and richer skill mix have been linked with decreased patient mortality (Estabrooks, Midodzi, Cummings, Ricker, & Giovannetti, 2005). Hospitals with poor care environments had a higher percentage of nurses reporting high burnout levels and dissatisfaction with their jobs (Aiken, Clarke, Sloane, Lake, & Cheney, 2008). Self-reported collaboration between medical ICU nurses and physicians was linked to improved patient outcomes (Baggs et al., 1999).

More complex care environments also have an impact on patient outcomes. Using the Environmental Complexity Scale (ECS), O'Brien-Pallas et al. (2004) found high levels of environmental complexity were associated with more medical consequences for patients. In that study, nurses' perceptions of unit violence (emotional abuse, threats, or actual assault) were associated with delayed nursing interventions.

No study has been designed to put together nurse staffing, nursing workload, the working environment, and patient outcomes at unit level in one design because data have not been available on all four aspects of the model in the same settings. This is what we attempted to do. Based on the literature and the research objectives requested by NSW Health, the research questions were:

  • 1.

    Has nursing workload (measured as inpatient acuity, shorter LOS, patient turnover, and casemix) and skill mix increased over time?

  • 2.

    What are the relationships among patient outcomes (OPSN, falls, and medication errors), nursing skill mix, nursing workload, and the nursing work environment?

Section snippets

Setting

NSW is the most populous of Australia's seven States and Territories and contains a third of Australia's population of 21.5 million (Australian Bureau of Statistics, 2008). The State is divided into Area Health Services (AHS), 17 at the time the study began and 8 when it finished. Commonwealth and State governments fund AHS on a population-based funding model. Data were obtained from the public hospital system (61% of all hospital discharges; Australian Institute of Health and Welfare, 2008).

Nurse staffing

With total nursing time on the unit as the denominator, the average skill mix across hospitals for medical, surgical, or general units for the 5-year period showed 68.4% RN, 7.4% CNS, 20.4% EN, and 3.8% AIN/TEN. With the exception of day units, the proportion of RNs (including CNS) was lowest in general (70.3% RN and 7.3% CNS), medical (65.4% and 7.2%), and surgical (68.5% and 7.6%) units and highest in specialty units.

Over the 5 years, there was a significant increase in AIN/TEN hours relative

Discussion

The longitudinal study results show that although there had been increased investments in nursing over the 5-year period, they were primarily in specialized nursing units, such as critical care and ED, and primarily in metropolitan hospitals. At the same time, there was increased casualization (rates of part-time hours worked) of the nursing workforce and downward substitution, converting nursing positions to AIN. These findings parallel similar trends in many countries including the United

Conclusions

Unit environments are much more variable than is revealed in state or hospital level analyses. This variation is evident in regard to most staffing, workload, and working environment measures. The provision of a quality work environment and steps to manage workload may be effectively managed at the unit level. The findings of this study suggest that improvements in these factors will lead to improvements in patient and nurse outcomes.

Unit-level data including staffing would not be difficult to

Acknowledgment

This research was funded by NSW Health. The views expressed in this article are the authors' and do not necessarily reflect the views of the funding organization. The authors wish to acknowledge the support given this research by the following individuals and organizations: the NSW Health Nursing and Midwifery Office, in particular Adjunct Professor Debra Thoms, Adjunct Professor Judith Meppem, Adjunct Professor Kathy Baker, Professor Mary Chiarella, Adjunct Professor Joan Englert, Ms. Marianne

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