Research ArticleOriginal Research

Physician and Advanced Practice Clinician Burnout in Rural and Urban Settings

Melissa L. Harry, Nancy L. Sudak, Mary J. Engels, Karly K. Horn, Kate Dean, Sara Poplau, Martin J. Stillman and Mark Linzer
The Journal of the American Board of Family Medicine January 2024, 37 (1) 43-58; DOI: https://doi.org/10.3122/jabfm.2023.230233R1

Abstract

Introduction: Recruiting rural-practicing clinicians is a high priority. In this study, we explored burnout and contributing work conditions among rural, urban, and family practice physicians and advanced practice clinicians (APCs) in an Upper Midwestern health care system.

Methods: The Mini Z burnout reduction measure was administered by anonymous electronic survey in March 2022. We conducted bivariate analyses of study variables, then assessed relationships of study variables to burnout with multivariate binary logistic regression.

Results: Of 1118 clinicians (63% response rate), 589 physicians and 496 APCs were included in this study (n = 1085). Most were female (56%), physicians (54%), and White (86%), while 21% were in family practice, 46% reported burnout, and 349 practiced rurally. Rural and urban clinician burnout rates were comparable (45% vs 47%). Part-time work protected against burnout for family practice and rural clinicians, but not urban clinicians. In multivariate models for rural clinicians, stress (OR: 8.53, 95% CI: 4.09 to 17.78, P < .001), lack of workload control (OR: 3.06, 95% CI: 1.47-6.36, P = .003), busy/chaotic environments (OR: 2.53, 95% CI: 1.29-4.99, P = .007), and intent to leave (OR: 2.18, 95% CI: 1.06-4.45, P = .033) increased burnout odds. In family practice clinicians, stress (OR: 13.43 95% CI: 4.90-36.79, P < .001) also significantly increased burnout odds.

Conclusions: Burnout was comparable between rural and urban physicians and APCs. Part-time work was associated with decreased burnout in rural and family practice clinicians. Addressing burnout drivers (stress, workload control, chaos) may improve rural work environments, reduce turnover, and aid rural clinician recruitment. Addressing stress may be particularly impactful in family practice.

Introduction

The COVID-19 pandemic drew needed attention to the issue of burnout and workplace stress among health care clinicians.1–3 In the 11th Revision of the International Classification of Diseases (ICD-11), burnout syndrome is considered an occupational phenomenon rather than a medical condition,4,5 and results from unmanaged chronic workplace stress distinguished by “1) feelings of energy depletion or exhaustion; 2) increased mental distance from one’s job, or feelings of negativism or cynicism related to one’s job; and 3) a sense of ineffectiveness and lack of accomplishment.”5 In 2020, 49% of more than 20,000 clinicians reported burnout in the American Medical Association’s Coping with COVID survey.6 These numbers soared to more than 60% later in 2021.7 Burnout was also associated with intent to leave health care employment during the pandemic.8 Frontline clinicians experienced moral injury, a concept identified in combat veterans,9 due to morally injurious encounters when clinicians were unable to cure seriously ill COVID-19 patients,10,11 or provide care concordant with their professional values.

Burnout in health care was a major concern before SARS-CoV-2,12–18 when burnout affected 35 to 54% of physicians and nurses in the US.19,20 In a 2017 to 2018 survey, 43.9% of physicians affirmed having 1 symptom of burnout.21 As summarized by West et al.,22 burnout negatively impacts physician health, including increasing risk of suicide, particularly in general practice and internal medicine,23 and impacts both patient care and health care systems.13 Factors associated with burnout can be influenced by individual and organizational characteristics, showing the need for interventions at multiple levels.24,25 Few studies have been conducted on stress and burnout among nurse practitioners (NPs) and physician assistants (PAs) together,19 suggesting more research is needed with Advanced Practice Clinicians (APCs), a large part of the rural health care workforce.26 A recent study showed that 38.5% of APCs reported 1 burnout symptom.27

Burnout in rural clinicians has been studied recently in a few settings and relatively small samples. Graduates of a South Dakota family practice residency program showed a low 25% burnout rate in rural-based family physicians versus 38% in midsize cities and 51% in metropolitan areas, highlighting favorable aspects of rural family practice, like greater workload control, community integration, and respect.28 There were prominent gender differences with female physicians having higher burnout rates, though these rates were comparable between rural and urban sites.28 Another survey showed a high 64% burnout rate among rural PAs, with isolation a potential contributing factor to emotional exhaustion, and a moderately strong correlation with work control as a mitigator of burnout.29 However, the response rate was low (11%).29 Another recent study in Northern Canada demonstrated care complexities in settings providing health services to Indigenous peoples where postcolonial structural inequities and language and cultural barriers still exist, and where significant factors associated with burnout included the electronic medical record (EMR), insufficient payments, and cross-cultural issues.30 Short visits, administrative burden, lack of continuity, and turnover were burnout aggravators whereas relationships within the office and community were mitigators, though relationships with persons in the community (blurred boundaries) were a double-edged sword.30 Thus, the recent literature offers data leaning toward favorable effects of rural practice, despite many challenges, and further study is justified. A gap in the literature also exists regarding whether burnout-related factors differ between rural physicians and APCs, including in family practice settings.

Study Objective and Aims

Our objective was to address identified literature gaps using secondary data from a quality improvement project that surveyed clinicians regarding worklife and burnout in a large, integrated, Upper Midwestern health care system. Survey questions used in this study included those from the validated Mini Z (Zero Burnout Program) instrument, which evaluates drivers of workplace stress, burnout, and satisfaction.31 This study was exploratory, with an objective of developing and advancing hypotheses testable in future research.

Our primary aims were twofold: 1) explore whether burnout and contributing work conditions differed between rural and urban clinicians and between physicians and APCs; and 2) explore contributors to burnout in family practice clinicians.

Methods

Study Population

The study population included physicians and APCs (NPs, PAs, clinical nurse specialists, midwives, certified registered nurse anesthetists) practicing in an integrated health care system serving a chiefly rural geography with mixed rural and urban populations and facilities in 3 Upper Midwestern states. The health care system’s Institutional Review Board determined this secondary data analysis study to be exempt human subjects research.

Instrumentation

The survey questions and response items used in this study are shown in Appendix 1, Table A1. The Mini Z is available in multiple languages and versions and was developed from previously validated questions used in the Minimizing Error Maximizing Outcome (MEMO) study’s clinician survey.31–34 The Mini Z version 1.0 used in this study is available at: https://www.professionalworklife.com/mini-z-survey. In the Mini Z, a single burnout item was adapted from Freeman’s tedium index.35 This item was found by Rohland et al.36 to be a suitable substitute for the gold standard for measuring burnout, the Maslach Burnout Inventory,37 with a significant correlation (r: 0.64) with the emotional exhaustion subscale. Burnout is rated on a 5-point scale where 3 responses represent burnout.31 Other Mini Z items include satisfaction, stress, values alignment, workload control, EMR time pressure, home EMR time, teamwork, and work atmosphere. In this study, internal consistency reliability of the Mini Z as represented by Cronbach’s α was good in the full sample (0.81), rural (0.82) and urban (0.81) subgroups, and in physicians (0.81) and APCs (0.82), and acceptable in family practice clinicians (0.79).

We included 2 items on the frequency of encountering negative experiences at work due to gender or race that were adapted from prior research.38,39 As noted by Audi et al., a single gender and race item correlated with burnout and other key burnout predictors.39 In addition, we included an item on intent to leave work in the next 2 years used in other studies on workplace stress,33,40,41 and in a recent study on the impact of COVID-19 on stress and work intentions among health care workers.8 As in prior studies, responses of moderately likely, likely, or definitely were considered as intending to leave. As noted by Sinsky et al.,8 physician intent to leave has correlated with actual departure at rates ranging from 16% to 55% in prior research.42–44

Data Collection

Data came from a convenience sample of clinicians that responded to the anonymous survey administered using SurveyMonkey45 from 03/01/2022 to 03/22/2022 in a health care system quality improvement project on clinician burnout. Approximately 1772 clinicians (all doctoral-level clinicians, advanced practice NPs, and PAs practicing in the health care system at the time of the survey) were invited by e-mail, and 1118 responded (63% response rate), including 33 psychologists, chiropractors, or other unidentified clinicians not included in this study. No payments were made for participation. Several communications were sent before the survey launch. A total of 6 communications, including reminder e-mails, were sent to clinicians while the survey was open.

Dependent and Independent Variables

The primary dependent variable in this study was the dichotomized responses to the Mini Z burnout item (I am definitely burning out and have 1 or more symptoms of burnout, eg, emotional exhaustion/The symptoms of burnout that I am experiencing will not go away. I think about work frustrations a lot/I feel completely burned out. I am at the point where I may need to seek help = 1 vs I enjoy my work. I have no symptoms of burnout/I am under stress, and do not always have as much energy as I did, but I do not feel burned out = 0) (Appendix 1, Table A1). Other dichotomized variables included other core Mini Z items: high satisfaction, high values alignment, and high stress (strongly agree/agree = 1 vs neither agree nor disagree/disagree/strongly disagree = 0), poor workload control and EMR time pressure (poor/marginal = 1 vs satisfactory/good/optimal = 0), high home EMR time (excessive/moderately high = 1 vs satisfactory/modest/minimal/none = 0), teamwork (optimal/good/satisfactory = 1 vs marginal/poor = 0), and work atmosphere (hectic, chaotic/very busy = 1 vs busy, but reasonable/not too busy/calm = 0); high 2-year intent to leave work (moderate/likely/definite = 1 vs slight/none = 0); negative workplace experiences based on race or gender (frequent/fairly often = 1 vs infrequently/rarely/never = 0); clinician role (physician = 0 vs APC = 1); employment type (part-time = 0 vs full-time = 1); clinic rurality (urban = 0 vs rural = 1); and race (white = 1 vs Black, Indigenous, and people of color [BIPOC] and multiracial = 0) (Appendix 1, Table A1). Gender was used as a 3-category variable (male = 0 vs female = 1 vs binary/transgender/other/prefer not to specify = 2) (Appendix 1, Table A1). Rurality was coded following the US Department of Veterans Affairs using respondents’ facility zip code and Rural-Urban Commuting Area codes: urban = 1, rural = 2 to 10.46–48 Fifteen respondents who reported a practice location as being too small for identification were included with rural respondents.

Data Analysis

We described the data with univariate statistics, compared rates of dichotomized study variables with Chi-Square cross tabulations (Fisher’s exact tests where expected cell counts were < 5), and reported Spearman’s rho correlation coefficients (rs) for the core Mini Z items, where statistically significant rs = 0.30–0.59 represented fair, 0.60–0.79 moderately strong, and ≥ 0.80 very strong correlations.49 We also tested 2 multivariate, binary logistic regression models adjusted for gender and race with separate subgroups (eg, rural and urban clinicians, physicians and APCs, and family practice clinicians). These adjusted multivariate models included: 1) Mini Z items alone; and 2) Mini Z items along with other independent variables. We reported McKelvey and Zavoina’s pseudo R2, which approximates the amount of variance in the dependent variable explained by binary logistic regression models.50 We employed listwise deletion. The lead author conducted analyses in Stata BE 17.51 P < .05 was considered statistically significant.

Results

Of the 1085 physician and APC respondents, most were female (56%), physicians (54%), white (86%), and full-time employees (84%) (Table 1). The largest discipline that respondents associated with was family practice (21%). Physicians were more likely to identify as male (49%) than female (39%), whereas APCs were more likely to identify as female (76%). Thirty-two percent (n = 349) of respondents reported a primary rural practice setting. Other respondent demographics are shown in Table 1.

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Table 1.

March 2022 Anonymous Clinician Burnout Quality Improvement Survey Respondent Demographics for the Full Sample and Family Practice Clinicians by Clinician Role for the Urban and Rural Subgroups

In testing our first aim, Table 2 presents intercorrelation matrices for the urban (lower diagonal) and rural (upper diagonal) subgroups of physicians and APCS. In the rural subgroup, most work condition items showed fair correlations with burnout (satisfaction, stress, workload control, EMR time pressure, chaotic work atmospheres, values alignment, and teamwork, P’s < 0.001), demonstrating the importance of modifiable rural practice work conditions in their relationship to burnout. For rural clinicians, several work conditions (stress, workload control, EMR time pressure, teamwork, and values alignment, P’s < 0.001) also had fair correlations with satisfaction, a known predictor of intent to stay. Likewise, in the urban subgroup, many work conditions exhibited fair to moderately strong correlations with burnout (satisfaction, stress, workload control, EMR time pressure, values alignment, teamwork, and chaotic work atmospheres, P’s < 0.001) and fair correlations with satisfaction (stress, workload control, EMR time pressure, teamwork, and values alignment, P’s < 0.001).

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Table 2.

Spearman’s Rho Correlation Matrices for the Mini Z 1.0 in Urban (Lower Diagonal) and Rural (Upper Diagonal) Subgroups of Physician and Advanced Practice Clinicians

In further exploring if and how traditional burnout factors differed between rural and urban clinicians and physicians and APCs (Aim 1), dichotomized Mini Z item prevalence are shown in Table 3 first for the full sample, then comparing rural with urban clinicians, and finally comparing urban and rural physicians and APCs separately. Burnout was seen in 46% of the full sample, with 39% intending to leave. Burnout aggravators for the full sample included stress (60%), chaotic work atmospheres (48%), poor workload control (43%), and EMR time pressure (39%). In bivariate analyses, rural clinicians had significantly lower rates of chaotic work atmospheres than urban clinicians (41% vs 51%, P = .003). Compared with urban physicians, rural physicians had significantly lower rates of burnout (39% vs 49%, P = .035) and chaotic work atmospheres (40% vs 52%, P = .013). However, no differences were seen in burnout odds between urban and rural physicians in adjusted, multivariate models (Table 4), and no differences were found between rural and urban APCs, including in bivariate models for all variables (Table 3) or multivariate analysis exploring burnout factors (Table 4). In the final adjusted multivariate model for physicians (Table 4), high satisfaction (OR: 0.16, 95% CI: 0.08-0.32, P < .001) and negative experiences due to race (OR: 0.12, 95% CI: 0.02–0.74, P = .023) were significantly associated with deceased burnout odds, and high stress (OR: 6.36, 95% CI: 3.60–11.24, P < .001), lack of workload control (OR: 2.42, 95% CI: 1.43–4.09, P = .001), and intent to leave (OR: 2.36, 95% CI: 1.39–4.02, P = .002) with increased burnout odds. For APCs in the final adjusted model, high satisfaction (OR: 0.46, 95% CI: 0.21–0.99, P = .049) and values alignment (OR: 0.48, 95% CI: 0.26–0.88, P = .018) were significantly associated with decreased burnout odds, and high stress (OR: 6.32, 95% CI: 3.63-11.02, P < .001), lack of workload control (OR: 1.98, 95% CI: 1.14-3.45, P = .015), and intent to leave (OR: 2.38, 95% CI: 1.32-4.28, P = .004) with increased burnout odds.

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Table 3.

Survey Responses to Mini Z 1.0 Items, Work Intentions, and Negative Work Experiences Due to Race or Gender: Full Sample and Rural versus Urban Comparisons, Including by Clinician Type

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Table 4.

Factors Related to Clinician Burnout: Adjusted Binary Logistic Regression Models for Physician and APC Subgroups in the Full Sample

In the final rural clinician subgroup multivariate regression model (Table 5), which included both physician and APCs, high satisfaction (OR: 0.26, 95% CI: 0.11-0.65, P = .004), values alignment (OR: 0.42, 95% CI: 0.19–0.90, P = .026), part-time work (OR: 0.30, 95% CI: 0.11–0.81, P = .017), and being BIPOC or multiracial (OR: 0.11, 95% CI: 0.02–0.58, P = .008) were associated with lower burnout odds in rural practice, whereas high stress (OR: 8.53, 95% CI: 4.09–17.78, P < .001), lack of workload control (OR: 3.06, 95% CI: 1.47–6.36, P = .003), chaotic work atmospheres (OR: 2.53, 95% CI: 1.29–4.99, P = .007), and intent to leave (OR: 2.18, 95% CI: 1.06–4.45, P = .033) were associated with higher burnout odds. Of note, in the model including core Mini Z items adjusted for gender and race, we found that pseudo R2 was 61%, increasing to 64% when adding several other potential burnout factors into the model. In the final urban clinician subgroup model (Table 5), high satisfaction (OR: 0.24, 95% CI: 0.13–0.44, P < .001) and negative experiences due to race (OR: 0.09, 95% CI: 0.01–0.67, P = .019) were associated with lower odds of burnout, and high stress (OR: 7.55, 95% CI: 4.60–12.41, P < .001), lack of workload control (OR: 1.81, 95% CI: 1.15–2.83, P = .010), and intent to leave (OR: 2.48, 95% CI: 1.54–3.98, P < .001) were associated with higher burnout odds. The Mini Z core items adjusted for race and gender in the urban subgroup of physicians and APCs explained 50% of burnout variance, increasing to 54% in the final model.

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Table 5.

Factors Related to Clinician Burnout: Adjusted Binary Logistic Regression Models for the Rural and Urban Subgroups of Physician and Advanced Practice Clinician Survey Respondents

In the 223 family practice clinicians (Aim 2), the burnout rate was 51%. Adjusted, multivariate binary logistic regression analyses showed no significant differences in burnout based on gender, clinician role, or rurality among family practice clinicians in the final model (Table 6). However, part-time work (OR: 0.24, 95% CI: 0.07–0.81, P = .022) and high satisfaction (OR: 0.18, 95% CI: 0.05–0.62, P = .007) were significantly associated with lower burnout odds, whereas high stress was a significant burnout driver (OR: 13.43, 95% CI: 4.90–36.79, P < .001). Along with gender and race, the Mini Z core items explained 59% of burnout variance in family practice clinicians, increasing to 65% of burnout variance in the final model with additional independent variables.

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Table 6.

Factors Related to Clinician Burnout: Adjusted Binary Logistic Regression Models for Family Practice Physicians and Advanced Practice Clinicians

Discussion

Burnout among clinicians is a complex problem where screening and intervention may allay the deleterious impacts on the health care workforce, systems, and patients. In this large sample of physicians and APCs from an integrated health care system serving a mixed rural and urban population across a primarily rural geographic area of the Upper Midwest, we found a burnout rate of 46% in surveyed clinicians, lower than national levels (>50%) in a similar time frame.7 Burnout rates among rural clinicians were comparable to those in urban practice. Part-time practice was a protective factor in most models. Challenging rural work conditions associated with higher burnout odds in multivariate models included high stress, lack of workload control, and fast-paced, chaotic environments, similar to those in urban practice, confirming that addressing modifiable work conditions identifiable with the Mini Z or similar worklife measures may improve burnout. A strong link was found in adjusted models for burnout and intent to leave for all clinicians, including in rural practitioners, suggesting that the consequences of allowing burnout to remain at current levels may negatively impact the rural workforce.

The Mini Z measure had excellent performance overall, and in rural clinicians generally (α: 0.81, 61% variance in burnout explained), comparable to or better than its performance with urban clinicians in this study and in other settings.34 Our findings on amount of burnout variance explained are among the highest documented using the Mini Z (eg, vs 50% of variance explained in an earlier study).52 Our findings may also have considerable importance for the recruitment of clinicians to rural and family practice, and for the measurement and improvement of work conditions that merit additional study (eg, workplace stress) to further improve rural and family practice clinicians’ worklives.

Our findings add substantively to the existing literature on worklife in rural practice, which has suggested challenges in small group practices.53 More favorable findings for some smaller practices in more current research suggests this might be changing.54,55 Like some recent research,28 we found further evidence that burnout rates may be comparable between rural-practicing physicians and their more urban contemporaries. A study in PAs has shown a higher rate of burnout (64%).29 Similar to our findings, there was a fair correlation of burnout in the prior study of PAs with lack of control of workload (r = 0.40), further strengthening the importance of addressing this variable to improve worklife in rural practice.29 Thus, our large and diverse study is concordant with recent findings in the literature on rural clinicians suggesting comparable, or in the case of physicians, more favorable,29 unadjusted burnout rates and remediable predictors. This is a new and encouraging picture of rural practice.

Burnout was moderately high at 46% in study respondents, though it was favorable in comparison to findings from December 2021 noted in a recent national study.7 Fair to moderately strong correlates of burnout in both the urban and rural subgroups included remediable worklife factors such as stress, workload control, values alignment, and chaotic workplaces. The meaningful correlation of burnout and workload control confirms and strengthens the finding in the study by Benson et al.29 Many of these factors (eg, workload control and values alignment) were also associated with job satisfaction, typically a strong predictor of intent to stay.56 These findings point the compass of where one might focus efforts to improve worklife in rural practice: control of one’s schedule; support for documentation (eg, remote or on-site scribes); sufficient clinical staffing; and aligning values to maintain a focus on the mission of rural practice.

Limitations

This study is limited in that the survey data came from clinicians at a single health care system that was collected as part of a quality improvement project. Although the survey was anonymous, practice location was not, which allowed for determining level of rurality based on respondents’ primary practice location. However, due to space constraints and relatively small samples in numerous specialties, we were not able to assess burnout correlates for all practice types. In addition, the impact of race and negative experiences at work due to race on burnout were tested in small samples; thus, these findings should be viewed with caution. Of note, listwise deletion was employed due to some data being not missing at random. Finally, although the overall sample size was large, only 349 clinicians in the survey reported practicing in a rural setting and only 223 worked in family practice. However, the rural sample was still a fair amount higher than another recent study of rural clinicians.28 Further research is needed with larger samples of rural and family practice clinicians, including from other geographic areas.

Conclusions

In this multi-state, Upper Midwestern, integrated health care system with a considerable number of clinicians practicing in rural settings and a high response rate to a quality improvement survey, burnout among rural clinicians was comparable to those practicing in urban settings. Although initiatives to decrease burnout among all clinicians must continue, our findings seem to refute the notion that practicing in a rural setting might lead to increased burnout compared with those in an urban setting. The Mini Z measure performed well in rural clinicians and can be considered an acceptable, brief, and meaningful adjunct to assess worklife and burnout in rural practice. Further studies should expand this work and confirm remediable contributors to enhancing worklife in rural settings, while confirming or denying the comparable burnout rates for rural and urban clinicians reported in this study. Part-time work continues to be associated with decreased burnout in family practice and rural, but not urban clinicians, which supports further attention and study.

Acknowledgments

The authors thank Dr. Erin E. Sullivan, Suffolk University and Harvard Medical School, for thoughtful review and revision recommendations.

Appendix

View this table:
Table A1.

Quality Improvement Survey Questions and Response Options Used in This Study

Notes

  • This article was externally peer reviewed.

  • Funding: Essentia Health.

  • Conflict of interest: Dr. Linzer is supported through his employer Hennepin Healthcare for work on burnout reduction projects with large healthcare organizations including the American Medical Association, the Institute for Healthcare Improvement, the American Board of Internal Medicine, Optum, Gillette Children’s Hospital and the California Area Health Education Center System. His work on this paper was supported through his employer by Essentia Health. Ms. Poplau is supported through her employer Hennepin Healthcare Research Institute for work on burnout reduction projects with large healthcare organizations including the American Medical Association, the Institute for Healthcare Improvement, the American College of Physicians, Optum, Gillette Children’s Hospital and the California Area Health Education Center System. Her work on this paper was supported through her employer by Essentia Health. Dr. Stillman is supported through his employer, Hennepin Healthcare, for work on burnout reduction projects with healthcare organizations including the American Medical Association, Optum, Inc., and Gillette Children’s Hospital. His work on this paper was supported through his employer by Essentia Health. Dr. Sudak, Ms. Engels, and Ms. Horn are supported by their employer, Essentia Health. Their work on this paper was supported by Essentia Health.

  • To see this article online, please go to: http://jabfm.org/content/37/1/43.full.

  • Received for publication June 15, 2023.
  • Revision received August 21, 2023.
  • Accepted for publication August 29, 2023.

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