Stress among health care professionals is very high, and its negative consequences (e.g., burnout) are a persistent concern in the health care industry (Aiken, Clarke, Sloane, Sochalski, & Silber, 2002). As concerns for health care provider stress have grown, concerns for patient safety have grown in parallel, and it seems natural to consider the relationships between the two trends (Hickam et al., 2003). In particular, safety in medication administration has generated great interest (Institute of Medicine, 2006; Koppel, Wetterneck, Telles, & Karsh, 2008). A number of studies have already found that stress and burnout may exacerbate concerns about patient safety and lead to lower quality of care. For example, Shanafelt et al. (2009) examined error reporting among 7,905 surgeons and found that nearly 9% reported a major error in the last 3 months; those experiencing burnout were much more likely to have reported an error. In an earlier examination of physician stress, Firth-Cozens and Greenhalgh (1997) surveyed 225 English general practitioners and hospital physicians and found that 82 reported incidents where symptoms of stress had impacted patient care. Stress attributions included fatigue (57%), overwork (28%), depression or anxiety (8%), or the effects of alcohol (5%). There is a wealth of literature on burnout among nurses, suggesting that it is very common and has significant negative outcomes for both nurses and patients (Cimiotti, Aiken, & Poghosyan, 2008; Van Bogaert et al., 2010). However, this literature has only recently begun to address the processes that help explain why health care provider stress and burnout lead to negative patient outcomes (Williams, Lawrence, Campbell, & Spiehler, 2009). This article extends that literature by examining how satisfaction with work processes impacts the link between nurse burnout and potentially unsafe practices, termed work-arounds.

Work-arounds are processes that are undertaken to complete tasks when employees are faced with situations that keep them from doing the work in a way that they are expected to (Ash, Berg, & Coiera, 2004; Ash et al., 2003; Halbesleben, Wakefield, & Wakefield, 2008; Vogelsmeier, Halbesleben, & Scott-Cawiezell, 2008). For example, bar-code medication administration systems are susceptible to work-arounds because they introduce new steps in the work process that, although designed to improve patient safety, add additional workload to nurses' already hectic work lives (Yang, Boon-Yuen, Kankanhalli, & Yip, 2012). As a result, although their frequency is unknown, work-arounds appear to be increasing in frequency (Morath & Turnbull, 2005), including strategies such as making copies of patient code bracelets to keep near medication carts to speed up processing of medications (Morath & Turnbull, 2005; Wideman, Whittler, & Anderson, 2005). Work-arounds have also been observed in other systems, including medication dispensing systems (Azad & King, 2008), computerized order entry systems (Schoville, 2009), and payment systems (Hartmann, Hoff, Palmer, Wroe, Dutta-Linn, & Lee, in press). Work-arounds are a significant concern for health care organizations because they decrease the reliability of systems and create opportunities for errors (Kobayashi, Fussell, Xiao, & Seagull, 2005). We contend that work-arounds are more likely when employees experience high levels of burnout.

Burnout is a reaction to prolonged work stress that is characterized by emotional exhaustion, depersonalization, and reduced personal efficacy (Maslach, 1982). Emotional exhaustion refers to a feeling that one's emotional resources have been drained and he or she can give nothing further to work. Depersonalization is a process of detaching from the work; it is often considered a coping mechanism to deal with the emotional exhaustion. Finally, reduced personal efficacy refers to a perception that one is not as good at his or her job as he or she once was. In this study, we focused on emotional exhaustion as the primary marker of health care provider burnout. This is consistent with much of the recent literature on burnout, which has argued that emotional exhaustion is the "core" dimension of the burnout construct and the symptom most likely to appear first (Demerouti, Bakker, & Bulters, 2004; Shirom, 2003). Emotional exhaustion is of significant concern in nursing largely because it is so widespread among nurses and is associated with worse patient outcomes (Aiken et al., 2002). For example, Laschinger and Leiter (2006), in a study of 8,597 nurses, found that burnout was significantly associated with reduced patient safety, based on nurse reports of frequency of adverse events. As a result, it is good starting point for examining how depleted resources would lead to work-arounds.

Theoretical Framework: Conservation of Resources

The conservation of resources (COR) theory (Hobfoll, 1988, 1989, 1998, 2001) has become a popular theory in the stress and burnout literature and has seen a great deal of empirical support (Halbesleben & Buckley, 2004). The COR theory focuses on our psychological and physical resources, defined as objects, states, and conditions that we value (Hobfoll, 1988). The theory is built on two basic principles: that we seek to avoid loss of resources and that we invest resources to gain additional resources. The first principle suggests that when our resources are lost or threatened, we experience stress (Hobfoll, 1989); over the long-term, the repeated loss of resources could lead to psychological strain (Hobfoll & Freedy, 1993; Shirom, 2003). The second principle of the COR theory suggests that if we are to avoid strain resulting from resource loss, we need to invest our resources in ways that protect our current resources and hopefully lead to gains in resources over the long-term.

The conservation of resources (COR) theory (Hobfoll, 1988, 1989, 1998, 2001) has become a popular theory in the stress and burnout literature and has seen a great deal of empirical support (Halbesleben & Buckley, 2004). The COR theory focuses on our psychological and physical resources, defined as objects, states, and conditions that we value (Hobfoll, 1988). The theory is built on two basic principles: that we seek to avoid loss of resources and that we invest resources to gain additional resources. The first principle suggests that when our resources are lost or threatened, we experience stress (Hobfoll, 1989); over the long-term, the repeated loss of resources could lead to psychological strain (Hobfoll & Freedy, 1993; Shirom, 2003). The second principle of the COR theory suggests that if we are to avoid strain resulting from resource loss, we need to invest our resources in ways that protect our current resources and hopefully lead to gains in resources over the long-term.

The conservation of resources (COR) theory (Hobfoll, 1988, 1989, 1998, 2001) has become a popular theory in the stress and burnout literature and has seen a great deal of empirical support (Halbesleben & Buckley, 2004). The COR theory focuses on our psychological and physical resources, defined as objects, states, and conditions that we value (Hobfoll, 1988). The theory is built on two basic principles: that we seek to avoid loss of resources and that we invest resources to gain additional resources. The first principle suggests that when our resources are lost or threatened, we experience stress (Hobfoll, 1989); over the long-term, the repeated loss of resources could lead to psychological strain (Hobfoll & Freedy, 1993; Shirom, 2003). The second principle of the COR theory suggests that if we are to avoid strain resulting from resource loss, we need to invest our resources in ways that protect our current resources and hopefully lead to gains in resources over the long-term.

The conservation of resources (COR) theory (Hobfoll, 1988, 1989, 1998, 2001) has become a popular theory in the stress and burnout literature and has seen a great deal of empirical support (Halbesleben & Buckley, 2004). The COR theory focuses on our psychological and physical resources, defined as objects, states, and conditions that we value (Hobfoll, 1988). The theory is built on two basic principles: that we seek to avoid loss of resources and that we invest resources to gain additional resources. The first principle suggests that when our resources are lost or threatened, we experience stress (Hobfoll, 1989); over the long-term, the repeated loss of resources could lead to psychological strain (Hobfoll & Freedy, 1993; Shirom, 2003). The second principle of the COR theory suggests that if we are to avoid strain resulting from resource loss, we need to invest our resources in ways that protect our current resources and hopefully lead to gains in resources over the long-term.

The second principle, resource investment, is the key principle for this study. In addition to suggesting that we invest resources to gain resources, Hobfoll (2001) proposed that (a) having more resources puts us in a better position to gain resources (a resource gain cycle), (b) having fewer resources leaves us more vulnerable to resource loss (a resource loss cycle; cf., Demerouti et al., 2004), and (c) when our resources are limited, we are more likely to take a defensive position in our resource investment strategies to first protect our current resources before attempting to gain more resources (see also Baltes & Baltes, 1990; Baltes, 1997). These three ideas give us further insight into the link between exhaustion and work-arounds.

Under the COR theory, emotional exhaustion is a situation of severely depleted work resources (Hobfoll & Freedy, 1993). As a result, the notions that resource loss cycles are more likely and that we are more likely to take a defensive posturing come into play. Halbesleben (2010) suggested that this would lead to a great reliance on work-arounds because it is easier for health care professionals to work around a block in the process than it is to correct the problem that is causing the block. Thus, a work-around requires fewer resources than correcting the problem that caused the block. When resources are limited, the COR theory, as well as empirical research, suggests that individuals will select the path that requires the fewest resources to get the job done; in the present case, it would likely be a work-around (Halbesleben, 2010; Hobfoll, 2001). Thus, we hypothesize:

Resource investment strategies are highly strategic; individuals invest their resources in ways that minimize their losses and maximize their gains (Hobfoll, 2001). As a result, individuals rationally consider the likelihood that their investments will be successful prior to choosing to invest in a certain course of action. In the context of medication administration, we argue that one's perceptions of the process used to administer medications will impact the likelihood of investing resources in working around the system rather than addressing the blocks in the system. Specifically, if the nurse is satisfied with the medication administration system, he or she may be more willing to invest resources in its improvement rather than bypassing the system to simply "get the work done." Satisfaction with the system implies a positive affective connection to the system and a greater desire to see its success. As a result, despite limited resources associated with exhaustion, one may be more willing to invest in process improvement rather than work around the system. On the other hand, if the nurse is dissatisfied with the system and has limited resources, there would be little perceived gain in investing in the process and a work-around would be the more likely response. Together, this suggests that satisfaction with the medication administration system moderates the positive relationship between exhaustion and work-arounds such that those who are satisfied are less likely to engage in work-arounds.

To measure emotional exhaustion, we utilized the emotional exhaustion subscale of the Maslach Burnout Inventory-General Survey (Schaufeli, Leiter, Maslach, & Jackson, 1996). The emotional exhaustion subscale includes five questions that were scored on a 7-point frequency scale from never (0) to daily (6); a sample question is "I feel burned out at my work." The scale demonstrated acceptable reliability (internal consistency) in our study, with Cronbach's [alpha] values of .91 for Sample 1 and .90 for Sample 2. This is the most widely used measure of emotional exhaustion in the literature, with a vast literature supporting its validity (Halbesleben & Buckley, 2004).

Work-arounds

Work-arounds were assessed using an adapted version of the measure of work-arounds in health care of Halbesleben and Rathert (2008). We adapted the four items to focus on medication administration. For example, "I have altered my work processes because of problems with technology" was changed to "When administering medications, I have altered my work processes because of problems with technology." Another sample item was "I have altered my work processes because the medication administration system is not designed well." Items were scored on a 7-point frequency scale from never (1) to every day (7). In Sample 1, this scale was completed by both the participant and his or her supervisor. The scale demonstrated acceptable reliability in our study, with Cronbach's [alpha] values for self-ratings of .87 for Sample 1 and .83 for Sample 2; the supervisor ratings had an internal consistency of .75.

Satisfaction with medication administration

Satisfaction with medication administration was assessed using the Medication Administration System-Nurses Assessment of Satisfaction scale of Hurley et al. (2006). It includes 18 items; a sample item was "The current medication administration system helps me to be more efficient at medication administration." Items were scored on a 5-point Likert-type scale from strongly disagree (1) to strongly agree (5); higher scores indicate greater satisfaction. The scale demonstrated acceptable reliability in our study, with Cronbach's [alpha] values of .95 for Sample 1 and .91 for Sample 2.

Control variables

We tested our hypothesized model using the hierarchical regression procedures outlined by Baron and Kenny (1986) and Aiken and West (1991). Prior to running the regression models, we calculated the mean score for each of the variables. The interaction was calculated by multiplying the mean exhaustion score by the mean medication administration system satisfaction score for each participant. Then, we mean centered the independent and moderator variables, following the recommendations of Aiken and West (1991).

Descriptive statistics from both samples, including means, standard deviations, internal consistency estimates (Cronbach's [alpha]), and correlations between the variables, are displayed in Table 1. As expected, emotional exhaustion was positively associated with work-arounds in both samples and satisfaction with the medication administration system was negatively associated with work-arounds in both samples. The regression results can be found in Tables 2 and 3. In Hypothesis 1, we predicted that exhaustion would be positively related to work-arounds. Examining the regression results in Tables 2 and 3, in particular Step 4 of each model, exhaustion was significantly and positively associated with self-rated ([beta] = .45, p < .01) and supervisor-observed ([beta] = .38, p < .01) work-arounds in Sample 1. In Sample 2, Time 1 exhaustion was significantly related to Time 2 work-arounds ([beta] = .59, p < .01). Thus, Hypothesis 1 was supported.

Table 1 Descriptive statistics (means, standard deviations, internal consistency, and correlations)

Table 2 Sample 1 regression results of the moderated relationship between exhaustion, satisfaction with medication administration, and work-arounds

Table 3 Sample 2 regression results of the moderated relationship between exhaustion, satisfaction with medication administration, and work-arounds

Table 3 Sample 2 regression results of the moderated relationship between exhaustion, satisfaction with medication administration, and work-arounds

With Hypothesis 2, we predicted that satisfaction with medication administration moderated the relationship between exhaustion and work-arounds. In both samples, we found that the addition of the interaction between satisfaction with medication administration and exhaustion led to a significant increase in R² (Tables 2 and 3). Again examining Step 4 in Tables 2 and 3, we found that the interaction between satisfaction with medication administration and exhaustion was significant for both self-rated ([beta] = -.19, p < .01) and supervisor-observed ([beta] = -.28, p < .01) work-arounds in Sample 1. The interaction was also significant in Sample 2 ([beta] = -.30, p < .01).

To illustrate the moderation effects, we graphed the interactions (Figures 1 and 2) using the procedures outlined by Aiken and West (1991) and Preacher, Curran, and Bauer (2006). We also performed simple slope tests (Aiken & West, 1991) to examine whether the slopes depicted in Figures 1 and 2 were significantly different from zero. The simple slope tests indicated that the slopes were positive (e.g., significantly greater than zero) in each case (Sample 1, self-rated work-arounds: low satisfaction, [beta] = .55, p < .01; high satisfaction, [beta] = .28, p < .05; Sample 1, supervisor-rated work-arounds: low satisfaction, [beta] = .49, p < .01; high satisfaction, [beta] = .21, p < .05. Sample 2, low satisfaction, [beta] = .64, p < .01; high satisfaction, [beta] = .36, p < .01). The pattern depicted in Figures 1 and 2 is consistent with the predictions-there is a main effect for exhaustion (exhaustion is positively associated with work-arounds) but that effect is diminished among those who are higher in satisfaction with the medication administration system. Overall, the findings support Hypothesis 2.

Figure 1. Work-arounds as a function of emotional exhaustion and medication administration satisfaction in Sample 1

Figure 2. Work-arounds as a function of emotional exhaustion and medication administration satisfaction in Sample 2

Discussion

Our findings are consistent with the work of Tucker (2004) and Tucker and Edmondson (2002, 2003), who suggested that simple solutions to problems at work (of which work-arounds are an example) can often lead to additional problems and do little to address critical operational failures that lead to error (Halbesleben, Savage, Wakefield, & Wakefield, 2010). As resources are limited, employees may, out of necessity, turn their attention to simply getting the work done ("single-loop" problem solving) rather than carefully considering the underlying causes of the blocks they encounter and engaging in collaborative problem solving to eliminate those blocks ("double-loop" problem solving), depending on the severity of the block. To some extent, this problem can be attributed to leadership, which may facilitate cultures of collaboration or cultures of blame. Tucker and Edmondson (2003) reported that 70% of nurses they interviewed believed that their managers expected them to solve everyday problems by themselves, and potentially engage in a work-around, to address or risk being perceived as being incompetent.

We acknowledge that there are some limitations to this study that need to be considered when interpreting the results. The data from Sample 1 were cross-sectional, limiting our ability to draw causal conclusions and increasingly the likelihood that common method biases influenced the results. These limitations were mitigated by the use of supervisor ratings of work-arounds in Sample 1 and longitudinal data in Sample 2. Although the single-source nature of the data from Sample 2 means that common method bias is still a potential concern, the 6-month time lag reduces this concern somewhat (Podsakoff, MacKenzie, Lee, & Podsakoff, 2003).

Our findings suggest that efforts need to be taken to reduce exhaustion among employees and improve satisfaction with the medication administration system. Based on our findings, both of these approaches could reduce the frequency of work-arounds in medication administration. There is a common denominator supporting both reducing exhaustion and improving satisfaction with a work process: support. In the case of medication administration systems (and clinical information systems broadly), one form of support that improves implementation and satisfaction with system is the use of super users, or individuals who have additional expertise in the system and can serve as a resource for other users (Halbesleben, Wakefield, Ward, Brokel, & Crandall, 2009; Wetterneck, Skibinski, Schroeder, Roberts, & Carayon, 2004; Wideman et al., 2005). On the basis of our findings, we suggest that organizations consider developing super users who can not only improve satisfaction with medication administration systems but also serve as a more general mechanism for support in the workplace (thus reducing exhaustion).

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