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. Author manuscript; available in PMC: 2015 Mar 1.
Published in final edited form as: Res Social Adm Pharm. 2013 Jun 20;10(2):328–340. doi: 10.1016/j.sapharm.2013.05.007

The Association of Subjective Workload Dimensions on Quality of Care and Pharmacist Quality of Work Life

Michelle A Chui 1, Kevin A Look 2, David A Mott 3
PMCID: PMC3805762  NIHMSID: NIHMS485545  PMID: 23791360

Abstract

Background

Workload has been described both objectively (e.g., number of prescriptions dispensed per pharmacist) as well as subjectively (e.g., pharmacist’s perception of busyness). These approaches might be missing important characteristics of pharmacist workload that have not been previously identified and measured.

Objectives

To measure the association of community pharmacists’ workload perceptions at three levels (organization, job, and task) with job satisfaction, burnout, and perceived performance of two tasks in the medication dispensing process.

Methods

A secondary data analysis was performed using cross-sectional survey data collected from Wisconsin (US) community pharmacists. Organization–related workload was measured as staffing adequacy; job-related workload was measured as general and specific job demands; task-related workload was measured as internal and external mental demands. Pharmacists’ perceived task performance was assessed for patient profile review and patient consultation. The survey was administered to a random sample of 500 pharmacists who were asked to opt in if they were a community pharmacist. Descriptive statistics and correlations of study variables were determined. Two structural equation models were estimated to examine relationships between the study variables and perceived task performance.

Results

From the 224 eligible community pharmacists that agreed to participate, 165 (73.7%) usable surveys were completed and returned. Job satisfaction and job-related monitoring demands had direct positive associations with both dispensing tasks. External task demands were negatively related to perceived patient consultation performance. Indirect effects on both tasks were primarily mediated through job satisfaction, which was positively related to staffing adequacy and cognitive job demands and negatively related to volume job demands. External task demands had an additional indirect effect on perceived patient consultation performance, as it was associated with lower levels of job satisfaction and higher levels of burnout.

Implications/Conclusions

Allowing community pharmacists to concentrate on tasks and limiting interruptions while performing these tasks are important factors in improving quality of patient care and pharmacist work life. The results have implications for strategies to improve patient safety and pharmacist performance.

Keywords: Community Pharmacy, Workload, Medication Safety, Structural Equation Modeling

Background

High quality, safe patient care and quality of working life in community pharmacies have been identified in the literature as targets for improvement.1–3 Several studies have shown an equivocal relationship between workload and both the quality of care provided to patients4–6 and pharmacist quality of work life.7,8 In the community pharmacy literature, workload has been described as a ratio of prescriptions dispensed per level of staffing both objectively (e.g., number of prescriptions dispensed per pharmacist) as well as subjectively (e.g., pharmacist’s perception of busyness). These approaches might be missing other important characteristics of pharmacist workload that have not been previously identified and measured.

In medical and nursing disciplines, workload has been conceptualized to have multiple levels.9–11 In addition to objective measures such as number of patient office visits and occupied beds in a hospital unit, studies in these disciplines have been conducted that described subjective dimensions of workload including physical, cognitive, and emotional workload of health care staff.10,12 A similar approach to pharmacist workload would suggest that instead of only quantifying the number of prescriptions dispensed, the focus should be on understanding the characteristics that affect the pharmacists’ perception of workload on multiple, different dimensions.

To better understand the characteristics and effects of workload in community pharmacies, a human factors approach was used for this study.10 Human factors is a field of study focused on designing a system that fits the needs, abilities, and limitations of those working in the system and reducing hazards in order to improve quality and safety.13,14 A human factors perspective brings together two main themes of workload in healthcare; that workload is caused by system factors and that workload affects outcomes related to care that is provided (i.e., quality of care), specifically those outcomes related to patients (i.e., patient safety) and outcomes related to healthcare workers (i.e., quality of working life). Workload is the ratio of demands (task load) to available resources. Human factors also incorporates the concept that actual and perceived workload occurs at multiple levels within a work system: the organization level (staff adequacy and training), job level (general work expectations), and task level (mental demands associated with specific tasks). The levels or types of workload are hypothesized to influence outcomes. With the exception of one recent study conducted in the institutional setting,15 pharmacist workload has not been explored subjectively (i.e., perceived workload) on multiple levels.

Given evidence of increased workload for community pharmacists and very little research focused on the conceptualization, measurement, and effects of community pharmacist workload, a multi-level model that measured nursing workload was adapted (Figure 1).16 The model shows subjective workload demands occurring at three levels (organization-, job-, and task-level) being influenced by demands and resources at each level. Pharmacist workload stems from an imbalance in demands and resources which then results in the subjective reactions to the workload. In other words, if a pharmacist has too many demands for the available resources, then their subjective workload will be high. If they have more resources than demands, then subjective workload will be low. The outcomes impacted by pharmacist workload include both pharmacist-related and quality of care-related outcomes.

Fig. 1.

Fig. 1

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Multi-level human factors framework of perceived pharmacist workload. Adapted from a framework of nursing workload.16

Conducting a thorough patient drug profile review and providing a patient drug consultation upon dispensing are important tasks in the dispensing process that can help ensure that a drug dispensed to a patient is appropriate (i.e., correct drug, correct dose, no interactions), will be used correctly, and will maximize patient outcomes. These tasks were chosen because they are tasks that if performed poorly could lead to errors, and it was hypothesized that performance of these tasks required different cognitive and temporal demands on pharmacists. Research shows that pharmacist workload is associated with pharmacists performing these tasks.17–23

Pharmacist job satisfaction and burnout are quality of working life outcomes hypothesized to be associated with workload. Both job satisfaction and burnout could be associated with work demands occurring at each level in the model. Past research of pharmacists shows some association between pharmacist workload and pharmacist job satisfaction and burnout.7,24–27

The objective of this study was to assess whether and which multi-dimensional measures of workload were related to perceived performance of two common tasks in the medication dispensing process: patient drug profile review and patient consultation, and two pharmacist outcomes: job satisfaction and burnout.

Methods

Design and Sampling

This study is a secondary analysis using cross-sectional survey data collected from pharmacists practicing in Wisconsin (US) community pharmacies in 2009.28 For the survey, a sample of 500 pharmacists licensed and having addresses in Wisconsin was randomly selected from a list of all pharmacists licensed in Wisconsin obtained from the Wisconsin Department of Regulation and Licensing. Subjects were mailed a letter describing the study and an opt-in form with three choices: 1) the respondent is a community pharmacist and would like to participate, 2) the respondent is a community pharmacist but does not want to participate, or 3) the respondent is not a community pharmacist. Respondents were asked to report their practice setting since the sampling frame did not contain practice setting information for each licensed pharmacist. If respondents reported they would like to participate, they reported their preferred survey format (paper via postal mail or electronically via an online link contained in an email). Respondents returned the opt-in form either via fax or postage paid return envelope.

The survey instrument contained 63 items, including the study measures and pharmacist and pharmacy characteristics. Surveys were either mailed to subjects or a link to the web-based survey was sent via e-mail. Subjects receiving mailed surveys were asked to either fax or mail back the survey using an included postage paid return envelope. One electronic or mailed reminder was sent to non-respondents two weeks after the initial mailing.

Measures

Endogenous Variables

The dependent variables of interest were pharmacists’ self-reported perceived performance of 2 different tasks in the medication dispensing process: conducting a patient profile review and counseling a patient on a new medication. Perceived performance was assessed using a single item for each task (e.g., “To what extent are you confident that the patient profiles you conduct are complete?” and “To what extent are you confident that patients understand how to correctly take their new medication(s) after your consultation?”). Job satisfaction was measured using a 3-item scale.29 Burnout was measured using the emotional exhaustion subscale of Maslach and Jackson’s (1981) burnout inventory.30

Exogenous Variables

The exogenous variables in the model were divided into three different levels within the work system: organization-related, job-related, and task-related workload. Organization-related workload, categorized in the model as staffing composition and adequacy in the dispensing department of the pharmacy, was measured with questions asking about quantity and quality of pharmacist and technician staffing available in the pharmacy.31

Job-related workload was conceptualized as the demands associated with simultaneously managing all of the tasks and responsibilities of pharmacists, such as dispensing prescriptions efficiently, managing pharmacy personnel, and addressing inventory concerns. Job-related workload demands were divided into 2 primary dimensions: general and specific job demands. General job demands were conceptualized to have two dimensions: cognitive demands, which are related to the use of knowledge and skills learned in pharmacy school, and volume demands, which are related to the pressure of work output. Cognitive demands and volume demands were measured with 3 items and 4 items, respectively, from Karasek (1979).32 Specific job demand items focused on attention demands of the job, and were conceptualized to have two dimensions: monitoring demands, which are passive monitoring activities required to prevent problems, and production responsibility demands, which are related to the problems or negative patient outcomes that could occur. Monitoring demands and production responsibility demands were each measured with 4 separate items from Jackson et al. (1993).33

Task-related workload was defined as task-specific mental demands and was conceptualized to have 2 dimensions: internal task demands, which focus on the degree of concentration and mental effort required to complete a task, and external task demands, which focus on the effect of interruptions, being rushed, and having attention divided. Internal task demands and external task demands were measured with 2 items and 4 items, respectively, from Hart and Staveland (1988) and Reid and Nygren (1988).34,35 Since task-related workload was associated conceptually with each task, internal and external task demands were measured separately for each dispensing task.

All measures were self-reported and asked respondents to answer each question by thinking about the last 30 days. Responses to all study measures were on a numbered 7-point scale ranging from 0 to 6 with the response category labels “not at all”(=0), “just a little”(=1), “a moderate amount”(=2), “pretty much”(=3), “quite a lot”(=4), and “a great deal”(=5), as well as a “don’t know” option.

Analysis Plan

The multi-dimensional workload measures, job satisfaction, burnout, and the two perceived task performance measures were subjected to exploratory factor analysis and purified using principal components analysis (PCA) as described in a previous study.28 Based on the PCA, 33 of the original 38 study items were retained. A summary of the final measures identified for inclusion in this study, including reliability statistics and the questionnaire items for each dependent and independent variable, is presented in Table 1. Descriptive statistics were calculated for included characteristics of pharmacists and pharmacies, and bivariate correlations were calculated for the workload measures, job satisfaction, burnout, and the two perceived performance variables.

Table 1.

Measures used and reliability statistics.

Construct Reliability statistics
Unit Level
 Staffing Adequacy

  1. To what extent are there enough pharmacists working with you to allow you to get your work done?

  2. To what extent are there enough technicians working with you to allow you to get your work done?

  3. To what extent are the technicians sufficiently skilled to allow you to get your work done?

 α=.73
Job level
General
 Cognitive

  1. To what extent does your job have a clear definition of what others expect of you?

  2. To what extent does your job allow you to use the skills and knowledge that you learned in school?

 ρ=.35
 Volume

  1. To what extent does your job require you to work very fast?

  2. To what extent does your job require a great deal of work to be done?

  3. To what extent do you NOT have enough time to finish your work?

 α=.82
Specific
 Production Responsibility

  1. To what extent could a lapse of attention cause an adverse outcome to a patient?

  2. To what extent could an error on your part cause an adverse outcome to a patient?

  3. If you failed to notice a problem, to what extent could it result in an adverse outcome to a patient?

 α=.90
 Monitoring

  1. To what extent does your work require your undivided attention?

  2. To what extent do you have to concentrate all the time to watch for things going wrong?

  3. To what extent do you have to react quickly to prevent problems from arising?

 α=.73
Task Level
Patient Profile Review
 External Demands

  1. To what extent are you rushed while conducting a patient profile review?

  2. To what extent are you interrupted by insurance related issues while conducting a patient profile review?

  3. To what extent you interrupted by non-insurance related issues while conducting a patient profile review?

  4. To what extent is your attention divided between multiple tasks while conducting a patient profile review?

 α=.87
 Internal Demands

  1. To what extent does the process of conducting a patient profile review require your concentration?

  2. To what extent is mental effort required of you while conducting a patient profile review?

 ρ=.81
Counseling on a New Medication
 External Demands

  1. To what extent are you rushed while counseling a patient on a new medication?

  2. To what extent are you interrupted by insurance related issues while counseling a patient on a new medication?

  3. To what extent are you interrupted by non-insurance related issues while counseling a patient on a new medication?

  4. To what extent is your attention divided between multiple tasks while counseling a patient on a new medication?

 α=.90
 Internal Demands

  1. To what extent does the process of counseling a patient on a new medication require your concentration?

  2. To what extent is mental effort required of you while counseling a patient on a new medication?

 ρ=.88
Job Satisfaction

  1. All in all, to what extent are you satisfied with your job?

  2. In general, to what extent do you NOT like your job?

  3. In general, to what extent do you like working here?

 α=.91
Burnout

  1. To what extent do you feel emotionally drained from your work?

  2. To what extent do you feel used up at the end of the workday?

  3. To what extent do you feel fatigued when you get up in the morning and have to face another day on the job?

  4. To what extent do you feel burned out from your work?

 α=.93
Task Performance
Patient Profile Review

  1. To what extent are you confident that the patient profiles you conduct are complete?

 -
Counseling on a New Medication

  1. To what extent are you confident that patients understand how to correctly take their new medication(s) after your consultation?

 -
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Note: Items assessed using a numbered 7-point scale ranging from 0 to 6 with the response category labels “not at all”(=1), “just a little”(=2), “a moderate amount”(=3), “pretty much”(=4), “quite a lot”(=5), and “a great deal”(=6), as well as a “don’t know”(=0) option. Revised items represent those measures remaining after purification using exploratory factor analysis.28

For data analysis, a multi-level structural equation modeling (SEM) estimation model was constructed based upon a model developed to study the effect of nursing workload on job satisfaction, burnout, and medication error likelihood (Figure 2).16 SEM is a statistical technique useful for determining if a theoretical model is supported by the data collected, and can be used to analyze a wide variety of complex data, research designs, and theoretical models.36 Although this method is most commonly used to measure and understand the relationships among latent, unobserved variables, it can also be used to explore the direct and indirect effects of observable variables in the proposed model as a succession of structural equations akin to running several multiple regression equations.36

Fig. 2.

Fig. 2

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Multi-level structural equation modeling estimation model

Separate models were estimated for the 2 dispensing tasks: confidence that patient profile reviews are complete and confidence that patients understand how to correctly take their new medication(s) after consultation. Endogenous variables were job satisfaction, burnout, and the 2 dispensing tasks. Exogenous variables were measures of organization-related, general job-related, specific job-related, internal task-related and external task-related workload. Statistical adjustment was made for gender, years of experience, and pharmacy setting (large chain, independent, supermarket, mass merchandiser, or other) for each relationship estimated in the model. Adding covariates did not change the pattern of results or any outcomes of significance tests; therefore, only statistical values adjusted for covariates are reported. An a priori alpha criterion of 0.05 was used. All analyses were conducted using Stata version 12.1 (STATA Corp, College Station, TX).

Results

Of the 500 mailed opt-in invitations to complete the survey, 443 were returned. A total of 266 respondents reported that they were community pharmacists, and 224 of these community pharmacists agreed to participate. A total of 169 surveys were completed and returned by the 224 study subjects. Four surveys were missing more than 80% of the requested data and were removed from the analysis. This yielded a usable sample size of 165 (73.7%) community pharmacists of the 224 that agreed to participate. Table 2 contains a summary of the characteristics of the respondents. Mean respondent age was 49 years, and respondents had a mean of 24 years of experience. There was a nearly identical distribution between male and female gender. A little more than one-half (52%) of respondents worked in a community chain pharmacy, with smaller numbers working in independent (22%) or mass merchandiser (12%) pharmacy settings. The characteristics of the respondents were similar to the characteristics of community pharmacists in Wisconsin in 2009.37

Table 2.

Sample pharmacist characteristics (n=165).

Mean Age (SD) 48.9 years (13.5)
Female 49.7%
Mean Years of Experience (SD) 23.7 years (23.7)
Practice Setting
 Large Chain 52.1%
 Independent 22.4%
 Supermarket 6.1%
 Mass Merchandiser 11.5%
 Other 7.9%
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SD indicated standard deviation.

Tables 3 and 4 show that the workload measures were correlated within and across levels of workload. Job satisfaction and burnout were significantly negatively correlated. Comparing the bivariate correlations of the workload measures between the two outcome measures showed some different workload measures were correlated with each outcome measure.

Table 3.

Pearson correlations between workforce measures, job satisfaction, burnout, and conducting a patient profile review.

Profile Review U JG1 JG2 JS1 JS2 T1 T2 Job Satisfaction Burnout
Unit workload (U)
Job workload (general): Cognitive (JG1) 0.27**
Job workload (general): Volume (JG2) −0.48** −0.16*
Job workload (specific): Production (JS1) −0.08 0.08 0.26**
Job workload (specific): Monitoring (JS2) −0.17* 0.03 0.50** 0.51**
Task workload: Internal (T1) −0.11 0.01 0.41** 0.36** 0.63**
Task workload: External (T2) −0.38** −0.23** 0.63** 0.34** 0.51** 0.46**
Job satisfaction 0.47** 0.46** −0.45** −0.09 −0.20* −0.23** −0.36**
Burnout −0.41** −0.15 0.59** 0.17* 0.33** 0.36** 0.47** −0.65**
Profile review 0.13 0.20** −0.03 0.01 0.14 0.06 −0.15 0.26** −0.13
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*

p<0.05,

**

p<0.01

Table 4.

Pearson correlations between workforce measures, job satisfaction, burnout, and counseling a patient on a new medication.

Consult New Rx U JG1 JG2 JS1 JS2 T1 T2 Job Satisfaction Burnout
Unit workload (U)
Job workload (general): Cognitive (JG1) 0.27**
Job workload (general): Volume (JG2) −0.48** −0.16*
Job workload (specific): Production (JS1) −0.08 0.08 0.26**
Job workload (specific): Monitoring (JS2) −0.17* 0.03 0.50** 0.51**
Task workload: Internal (T1) −0.04 0.07 0.30** 0.31** 0.52**
Task workload: External (T2) −0.41** −0.23** 0.56** 0.19* 0.35** 0.18*
Job satisfaction 0.47** 0.46** −0.45** −0.09 −0.20* −0.16* −0.44**
Burnout −0.41** −0.15 0.59** 0.17* 0.33** 0.25** 0.51** −0.65**
Consult 0.10 0.23** −0.10 0.16* 0.16* −0.01 −0.21** 0.31** −0.16*
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*

p<0.05,

**

p<0.01

Figure 3 shows the adjusted results of the SEM model for the outcome of pharmacist perceived performance on patient profile reviews. (Only the statistically significant pathways are shown for clarity.) The observed data appear to fit the hypothesized model based on the fit indices. Direct effects were such that job satisfaction and monitoring demands were positively related to pharmacist perceived performance on patient profile reviews. That is, higher levels of job satisfaction and increased awareness of what’s going on around you in the pharmacy were associated with a higher perceived completeness of patient profile reviews. Indirect effects on patient profile review were mediated through both job satisfaction and burnout. Staffing adequacy was positively related to job satisfaction and negatively related to burnout. Job satisfaction was also positively related to cognitive job demands, which are related to the use of knowledge and skills learned in pharmacy school. High volume demands, such as having to work very fast, having a lot of work to be done, and not having enough time to finish work, were associated with lower levels of job satisfaction and higher levels of burnout. Internal task demands, such as concentration and mental effort required to complete a task, were positively related to burnout. As expected, job satisfaction and burnout had a strong negative correlation.

Fig. 3.

Fig. 3

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Estimated SEM model depicting significant relationships with the outcome of perceived performance on a patient profile review.

Outcome: Confidence that patient profile reviews are complete

Chi-square = 10.58 (12 df), p= 0.565 CFI = 1.000 TLI = 1.024 RMSEA = 0.000

* = p<0.05; ** = p<0.01. Results are unstandardized parameter estimates (SD).

Figure 4 shows the adjusted results of the SEM model for the outcome of perceived performance of a patient consultation. This model also showed good fit to the hypothesized model based on the fit indices. Similar to the previous model, direct effects were such that higher levels of job satisfaction and monitoring demands were associated with more confidence that patients understood how to use their new medication(s) after pharmacist consultation. Additionally, external demands had a negative direct effect, such that frequent interruptions or having attention divided between multiple tasks while counseling was associated with poorer perceived consultation performance. Indirect effects on patient consultation were primarily mediated through job satisfaction, which was positively related to staffing adequacy and cognitive job demands. In contrast, high volume demands and external demands were associated with lower levels of job satisfaction and higher levels of burnout.

Fig. 4.

Fig. 4

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Estimated SEM model depicting significant relationships with the outcome of perceived performance of counseling a patient on a new medication.

Outcome: Confidence that patients understand how to correctly take their new medication(s) after consultation.

Chi-square = 14.94 (12 df), p= 0.245 CFI = 0.988 TLI = 0.957 RMSEA = 0.039

* = p<0.05; ** = p<0.01. Results are unstandardized parameter estimates (SD).

Discussion

The goal of this study was to test the workload-outcome relationships proposed by the multi-level model in Figure 1. The results show the value and importance of studying multiple levels of subjective workload demands experienced by community pharmacists. Similar to studies in nursing and medicine, the results suggest that task performance is influenced by workload perceptions at various levels, corresponding to a conceptual model derived from a human factors approach to workload. Also, the results suggest that workload-outcome relationships are different depending on the task studied. This is consistent with Grasha et al.38,39 whose research found weak associations between workload and medication errors, which was inconsistent with previous research.5–7,40,41 Grasha posited that his findings may be because the dispensing process is multifaceted and diverse, as is the time and attention involved in each part of the process. This is an important finding, as it provides an idea of strategies that can be used in pharmacies to redesign work to improve performance of activities. Examining different outcomes is also important, as it provides clearer information and allows researchers to develop strategies that facilitate the likelihood that pharmacists will perform the tasks that they wish to perform.

The results for both tasks highlight the importance of job-related monitoring demands, which measure such things as concentrating, watching for things to go wrong, and reacting to prevent problems. It appears that community pharmacists who are able to focus on their job as a whole and that are aware of the entire work process in a community pharmacy have more confidence in their performance on patient profile reviews and patient consultation. This is consistent with published evidence that “having the big picture”, also called “situation awareness” in the human factors literature,42 is required for the safe operation of complex dynamic systems like community pharmacies. Situation awareness is the ability to quickly and effectively integrate relevant information from multiple sources in order to develop an accurate understanding of the environment, even under high uncertainty or rapid change.43 Research has shown that a lack of situation awareness in other health care disciplines can result in increased workload and errors.44 Therefore, it makes sense that poor pharmacist performance is more likely to occur when pharmacists have their attention diverted or divided, lose their concentration, or do not know the processes occurring in the pharmacy around them.

Efforts to improve task performance need to focus on improving situation awareness in the pharmacy. To facilitate situation awareness, pharmacists must work in an environment in which they can visualize or perceive what is going on in the pharmacy, understand and diagnose issues as they come up, and predict or anticipate future problems.42 As a result, studies suggest that complex systems should be designed to support the need for additional necessary information when unexpected things go wrong in an easy-to-interpret manner.45 For instance, automation has been widely implemented in pharmacies to carry out much of the dispensing processing. However, research shows that automation can reduce situation awareness when it is not transparent what the automation is doing or when the automation does not perform as intended. Just as it is important for technologies to display relevant information to improve situation awareness, it is important to design the pharmacy so pharmacists and technicians visualize and interpret “the big picture” while they are performing other tasks.

The significance of external task-related demands such as interruptions on poor perceived consultation performance reinforces the cognitive nature of this task. This is consistent with the human factors literature suggesting that task disruptions can increase required cognitive workload46 and can result in errors.47 More specifically, in community pharmacies, Flynn et al. found that not only were pharmacists subjected to numerous interruptions and distractions, but that the number of interruptions were significantly associated with dispensing errors.48 In contrast, external task-related demands were not significantly related to perceived patient profile review performance. This may be due to the ease with which a pharmacist can return to this task after being interrupted, whereas a patient consultation is a one-time event in which interruptions may have more of an impact upon the pharmacist’s perceived performance. In comparison to external task-related demands, the positive relationship between internal task-related demands and burnout suggests pharmacists may become frustrated if they are unable to focus or concentrate while performing drug profile reviews.

The effects of organization-related demands such as staffing adequacy and general job-related demands such as volume and cognitive job demands were mediated through burnout and job satisfaction, which in turn directly impacted task performance. These results are important, as they suggest that changes in work design that are organization-related, and/or related to the type of work and the volume of work a pharmacist performs will not directly impact performance of drug profile review and patient consultation, but affect pharmacist job satisfaction. Instead, redesign of work should focus on ways to improve re-allocation of work, communication, or teamwork in order to allow pharmacists to concentrate on the work they must perform. As may be expected, pharmacists reporting high levels of burnout also reported lower job satisfaction; this in turn led to decreased perceived performance on drug profile review and patient consultation.

Future research could explore additional performance outcomes and examine additional workload demands that pharmacists experience. Also, research could examine workload demands on pharmacists in other practice settings such as hospitals and clinics.

Limitations

The small sample of community pharmacists in Wisconsin limits generalizability, and renders the estimated coefficients less than efficient in an SEM framework. One way to potentially improve model fit would be the use of workload measures specific to community pharmacy practice.49,50 Many of the survey items were adapted from older studies in other disciplines. Thus, the items may not have been applicable to or may have been confusing to pharmacists; as a result, some measures may not represent the underlying construct well (e.g., general job level cognitive demands). Additionally, task performance measures were single-item measures; thus they have limited construct validity. However, the single performance items did correlate highly with each other. The use of multiple task performance measures may lead to better measurement of pharmacist perceived task performance. Further, although task performance was subjectively measured, these perceptions were not validated by direct observation or other objective measures of task performance.

A number of pharmacists did not respond to the survey, which suggests some potential non-response bias. There were no statistically significant differences in pharmacist characteristics, job satisfaction, burnout, and perceived drug profile review performance between early and late respondents. However, late responders reported higher perceived patient consultation performance, which was statistically significant (p<0.05).

Conclusion

This study showed the importance of measuring different levels of pharmacist perceived workload and how they are associated with task performance. A key result is that subjective measures of pharmacist workload are associated with performance of drug profile review and patient consultation performed by community pharmacists, and that different mental demands are associated with the performance of each task. This implies that no one solution will improve pharmacist performance on tasks, since solutions need to consider how pharmacists’ specific work environments impact workload perceptions.

Acknowledgments

The project described was supported by the Clinical and Translational Science Award (CTSA) program, previously through the National Center for Research Resources (NCRR) grant 1UL1RR025011, and now by the National Center for Advancing Translational Sciences (NCATS), grant 9U54TR000021. The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH.

Footnotes

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Contributor Information

Michelle A. Chui, Social & Administrative Sciences Division, University of Wisconsin – Madison, School of Pharmacy, Madison, WI.

Kevin A. Look, Social & Administrative Sciences Division, University of Wisconsin – Madison, School of Pharmacy, Madison, WI.

David A. Mott, Social & Administrative Sciences Division, University of Wisconsin – Madison, School of Pharmacy, Madison, WI.

References

  • 1.Boyle TA, MacKinnon NJ, Mahaffey T, Duggan K, Dow N. Challenges of standardized continuous quality improvement programs in community pharmacies: the case of SafetyNET-Rx. Res Social Adm Pharm. 2012;8:499–508. doi: 10.1016/j.sapharm.2012.01.005. [DOI] [PubMed] [Google Scholar]
  • 2.Ferguson J, Ashcroft D, Hassell K. Qualitative insights into job satisfaction and dissatisfaction with management among community and hospital pharmacists. Res Social Adm Pharm. 2011;7:306–316. doi: 10.1016/j.sapharm.2010.06.001. [DOI] [PubMed] [Google Scholar]
  • 3.Hardigan PC, Sangasubana N. A latent class analysis of job satisfaction and turnover among practicing pharmacists. Res Social Adm Pharm. 2010;6:32–38. doi: 10.1016/j.sapharm.2009.03.002. [DOI] [PubMed] [Google Scholar]
  • 4.Kreling DH, Doucette WR, Mott DA, Gaither CA, Pedersen CA, Schommer JC. Community pharmacists’ work environments: evidence from the 2004 National Pharmacist Workforce Study. J Am Pharm Assoc. 2006;46:331–339. doi: 10.1331/154434506777069651. [DOI] [PubMed] [Google Scholar]
  • 5.Malone DC, Abarca J, Skrepnek GH, et al. Pharmacist workload and pharmacy characteristics associated with the dispensing of potentially clinically important drug-drug interactions. Med Care. 2007;45:456–462. doi: 10.1097/01.mlr.0000257839.83765.07. [DOI] [PubMed] [Google Scholar]
  • 6.Rupp MT, DeYoung M, Schondelmeyer SW. Prescribing problems and pharmacist interventions in community practice. Med Care. 1992;30:926–940. doi: 10.1097/00005650-199210000-00005. [DOI] [PubMed] [Google Scholar]
  • 7.Gidman WK, Hassell K, Day J, Payne K. The impact of increasing workloads and role expansion on female community pharmacists in the United Kingdom. Res Social Adm Pharm. 2007;3:285–302. doi: 10.1016/j.sapharm.2006.10.003. [DOI] [PubMed] [Google Scholar]
  • 8.Eden M, Schafheutle EI, Hassell K. Workload pressure among recently qualified pharmacists: an exploratory study of intentions to leave the profession. Int J Pharm Pract. 2009;17:181–187. [PubMed] [Google Scholar]
  • 9.Carayon P, Gürses AP. A human factors engineering conceptual framework of nursing workload and patient safety in intensive care units. Intensive Crit Care Nurs. 2005;21:284–301. doi: 10.1016/j.iccn.2004.12.003. [DOI] [PubMed] [Google Scholar]
  • 10.Holden RJ, Alper SJ, Escoto KH, et al. Nursing workload and its effect on patient and employee safety. Human Factors and Ergonomics Society Proceedings. 2007 [Google Scholar]
  • 11.Upeniecks W, Kotlerman J, Akhavan J, Esser J, Ngo MJ. Assessing nursing staffing ratios: variability in workload intensity. Policy Polit Nurs Pract. 2007;8:7–19. doi: 10.1177/1527154407300999. [DOI] [PubMed] [Google Scholar]
  • 12.Carayon P, Alvarado CJ. Workload and patient safety among critical care nurses. Crit Care Nurs Clin North Am. 2007;19:121–129. doi: 10.1016/j.ccell.2007.02.001. [DOI] [PubMed] [Google Scholar]
  • 13.Carayon P. Handbook of human factors and ergonomics in health care and patient safety. 2. CRC Press; Boca Raton, FL: 2012. p. 4. [Google Scholar]
  • 14.Karsh BT, Holden RJ, Alper SJ, Or CK. A human factors engineering paradigm for patient safety: designing to support the performance of the healthcare professional. Qual Saf Health Care. 2006;15 (Suppl 1):i59–65. doi: 10.1136/qshc.2005.015974. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 15.Holden RJ, Patel NR, Scanlon MC, Shalaby TM, Arnold JM, Karsh BT. Effects of mental demands during dispensing on perceived medication safety and employee well-being: a study of workload in pediatric hospital pharmacies. Res Social Adm Pharm. 2010;6:293–306. doi: 10.1016/j.sapharm.2009.10.001. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 16.Holden RJ, Scanlon MC, Patel NR, et al. A human factors framework and study of the effect of nursing workload on patient safety and employee quality of working life. BMJ Qual Saf. 2011;20:15–24. doi: 10.1136/bmjqs.2008.028381. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 17.Latif A, Pollock K, Boardman HF. The contribution of the Medicines Use Review (MUR) consultation to counseling practice in community pharmacies. Patient Educ Couns. 2011;83:336–344. doi: 10.1016/j.pec.2011.05.007. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 18.Gadkari AS, Mott DA, Kreling DH, Bonnarens JK. Pharmacy characteristics associated with the provision of drug therapy services in nonmetropolitan community pharmacies. J Rural Health. 2009;25:290–295. doi: 10.1111/j.1748-0361.2009.00232.x. [DOI] [PubMed] [Google Scholar]
  • 19.Fritsch MA, Lamp KC. Low pharmacist counseling rates in the Kansas City, Missouri, metropolitan area. Ann Pharmacother. 1997;31:984–991. doi: 10.1177/106002809703100905. [DOI] [PubMed] [Google Scholar]
  • 20.Barnes JM, Riedlinger JE, McCloskey WW, Montagne M. Barriers to compliance with OBRA’90 regulations in community pharmacies. Ann Pharmacother. 1996;30:1101–1105. doi: 10.1177/106002809603001006. [DOI] [PubMed] [Google Scholar]
  • 21.Sarriff A. A survey of patient-orientated services in community pharmacy practice in Malaysia. J Clin Pharm Ther. 1994;19:57–60. doi: 10.1111/j.1365-2710.1994.tb00810.x. [DOI] [PubMed] [Google Scholar]
  • 22.Raisch DW. Barriers to providing cognitive services. Am Pharm. 1993;NS33:54–58. doi: 10.1016/s0160-3450(15)30574-2. [DOI] [PubMed] [Google Scholar]
  • 23.Laurier C, Archambault A, Contandriopoulos AP. Communication of verbal information by community pharmacists. DICP. 1989;23:862–868. doi: 10.1177/106002808902301105. [DOI] [PubMed] [Google Scholar]
  • 24.Gidman W. Increasing community pharmacy workloads in England: causes and consequences. Int J Clin Pharm. 2011;33:512–520. doi: 10.1007/s11096-011-9498-x. [DOI] [PubMed] [Google Scholar]
  • 25.Calgan Z, Aslan D, Yegenoglu S. Community pharmacists’ burnout levels and related factors: an example from Turkey. Int J Clin Pharm. 2011;33:92–100. doi: 10.1007/s11096-010-9461-2. [DOI] [PubMed] [Google Scholar]
  • 26.McCann L, Hughes CM, Adair CG, Cardwell C. Assessing job satisfaction and stress among pharmacists in Northern Ireland. Pharm World Sci. 2009;31:188–194. doi: 10.1007/s11096-008-9277-5. [DOI] [PubMed] [Google Scholar]
  • 27.Kreling DH, Doucette WR, Mott DA, Gaither CA, Pedersen CA, Schommer JC. Community pharmacists’ work environments: evidence from the 2004 National Pharmacist Workforce Study. J Am Pharm Assoc. 2006;46:331–339. doi: 10.1331/154434506777069651. [DOI] [PubMed] [Google Scholar]
  • 28.Chui MA, Mott DA. Community pharmacists’ subjective workload and perceived task performance: a human factors approach. J Am Pharm Assoc. 2012;52:e153–160. doi: 10.1331/JAPhA.2012.11135. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 29.Cammann C, Fichman M, Jenkins D, Klesh J. Assessing the attitudes and perceptions of organizational members. In: Seashore SE, Lawler EE, Mirvis PH, Cammann C, editors. Assessing organizational change: a guide to methods, measures and practices. John Wiley & Sons; New York: 1983. [Google Scholar]
  • 30.Maslach C, Jackson SE. Maslach Burnout Inventory. University of California, Berkeley; Berkeley, CA: 1981. [Google Scholar]
  • 31.Aiken LH, Patrician PA. Measuring organizational traits of hospitals: the Revised Nursing Work Index. Nurs Res. 2000;49:146–153. doi: 10.1097/00006199-200005000-00006. [DOI] [PubMed] [Google Scholar]
  • 32.Karasek RA. Job demands, job decision latitude, and mental strain, implications for job redesign. Admin Sci Quart. 1979;24:285–308. [Google Scholar]
  • 33.Jackson PR, Wall TD, Martin R, Davids K. New measures of job control, cognitive demand, and production responsibility. J Appl Psychol. 1993;78:753–762. [Google Scholar]
  • 34.Hart SG, Staveland LE. Development of the NASA-TLX (Task Load Index): results of empirical and theoretical research. In: Hancock PA, Meskati N, editors. Human mental workload. Elsevier; Amsterdam: 1998. pp. 139–183. [Google Scholar]
  • 35.Reid GB, Nygren TE. The subjective workload assessment technique: a scaling process for measuring mental workload. In: Hancock PA, Meskati N, editors. Human mental workload. Elsevier; Amsterdam: 1998. pp. 185–218. [Google Scholar]
  • 36.Schreiber JB. Core reporting practices in structural equation modeling. Res Social Adm Pharm. 2008;4:83–97. doi: 10.1016/j.sapharm.2007.04.003. [DOI] [PubMed] [Google Scholar]
  • 37.Kreling DH, Mott DA, Chen YC. The 2009 Biennial Wisconsin Pharmacist Workforce Survey. J Pharm Soc WI. 2010;13:30–34. [Google Scholar]
  • 38.Grasha AF, Schell KL. Psychosocial factors, workload, and human error in a simulated pharmacy dispensing task. Percept Mot Skills. 2001;92:53–71. doi: 10.2466/pms.2001.92.1.53. [DOI] [PubMed] [Google Scholar]
  • 39.Schell KL, Grasha AF. State anxiety, performance accuracy, and work pace in a simulated pharmacy dispensing task. Percept Mot Skills. 2000;90:547–61. doi: 10.2466/pms.2000.90.2.547. [DOI] [PubMed] [Google Scholar]
  • 40.Guernsey BG, Ingrim NB, Hokanson JA, et al. Pharmacists’ dispensing accuracy in a high-volume outpatient pharmacy service: focus on risk management. Drug Intell Clin Pharm. 1983;17:742–746. doi: 10.1177/106002808301701011. [DOI] [PubMed] [Google Scholar]
  • 41.Allan EL. Relationships among facility design variables and medication errors in a pharmacy [doctoral dissertation] Auburn: Auburn University; 1995. [Google Scholar]
  • 42.Endsley MR. Towards a theory of situation awareness in dynamic systems. Human Factors. 1995;37:32–64. [Google Scholar]
  • 43.Carayon P. Handbook of human factors and ergonomics in health care and patient safety. 2. CRC Press; Boca Raton, FL: 2012. p. 180. [Google Scholar]
  • 44.Autrey P, Moss J. High-reliability teams and situation awareness: implementing a hospital emergency incident command system. J Nurs Adm. 2006;36:67–72. doi: 10.1097/00005110-200602000-00004. [DOI] [PubMed] [Google Scholar]
  • 45.Sanderson PM, Watson MO, Russell WJ, et al. Advanced auditory displays and head-mounted displays: advantages and disadvantages for monitoring by the distracted anesthesiologist. Anesth Analg. 2008;106:1787–1797. doi: 10.1213/ane.0b013e31817325cb. [DOI] [PubMed] [Google Scholar]
  • 46.Zijlstra FRH, Roe RA, Leonora AB, Krediet I. Temporal factors in mental work: effects of interrupted activities. J Occup Organ Psych. 1999;72:163–185. [Google Scholar]
  • 47.Monsell S. Task Switching. Trends Cogn Sci. 2003;7:134–140. doi: 10.1016/s1364-6613(03)00028-7. [DOI] [PubMed] [Google Scholar]
  • 48.Flynn EA, Barker KN, Gibson JT, et al. Impact of interruptions and distractions on dispensing errors in an ambulatory care pharmacy. Am J Health Syst Pharm. 1999;56:1319–1325. doi: 10.1093/ajhp/56.13.1319. [DOI] [PubMed] [Google Scholar]
  • 49.Schreiber JB. Core reporting practices in structural equation modeling. Res Social Adm Pharm. 2008;4:83–97. doi: 10.1016/j.sapharm.2007.04.003. [DOI] [PubMed] [Google Scholar]
  • 50.Kenny DA, McCoach DB. Effect of the number of variables on measures of fit in structural equation modeling. Struct Equ Modeling. 2003;10:333–351. [Google Scholar]

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