Cultural And Structural Features Of Zero-Burnout Primary Care Practices

Workplace burnout is typically defined as a psychological response to job stressors characterized by emotional exhaustion, detachment, and a sense of ineffectiveness.¹ Investigations of burnout in primary care have usually focused on factors associated with burnout among individual practice members.² Personality traits such as low self-efficacy, anxiety, low self-esteem, and high conscientiousness have been found to be associated with burnout.^3–5 In addition, job-specific factors found to contribute to individual burnout include heavy workload,^6,7 lack of control,^8,9 and the use of electronic health records (EHRs).¹⁰ Many interventions to reduce burnout have focused on individuals, with a goal of improving resilience.¹¹

However, resilience is a collective as well as an individual attribute, and the communal and systemic factors affecting burnout are vital to understand,¹² to design interventions that move beyond victim blaming.¹³ Few studies examine burnout at the practice level, but conditions of work environments are known to drive burnout,^14–16 suggesting that burnout is a characteristic of practices and organizations and requires an organizational-level response.¹⁷ For example, recent work has demonstrated that solo and smaller primary care practices^18,19 with clinician ownership²⁰ have fewer burned-out members, which suggests that practice-level agency may be an important factor.²¹ We are not aware of any studies that have investigated whether there may be primary care practices with uniformly high or low burnout among practice members and whether practice-level characteristics contribute to that state.

The purpose of this exploratory study was to characterize the features of practices with little to no burnout in a large sample of primary care practices engaged in a national quality improvement initiative, which can inform interventions to reduce burnout in primary care.

Study Data And Methods

Setting

From 2015 to 2017 the Agency for Healthcare Research and Quality (AHRQ) funded EvidenceNOW, an initiative focused on increasing the implementation of evidence-based cardiovascular preventive care delivery in small-to-medium-size primary practices. Seven regional EvidenceNOW cooperatives recruited and provided external support to 1,716 practices (comprising more than 5,000 physicians and advanced practice clinicians) across twelve states. The study authors led the national evaluation of EvidenceNOW, “Evaluating System Change to Advance Learning and Take Evidence to Scale” (ESCALATES), and partnered with cooperatives that provided data for this study.²²

Sample

The original sample consisted of all practices and practice members that participated in EvidenceNOW. As small or solo practices have fewer respondents, they may have less reliable estimates of group-level measures²³ and be more likely to be classified as having zero burnout. To address this, we restricted our sample to practices that had at least one response from each of three roles: clinician, clinical staff member, and nonclinical staff member. We further restricted the sample to practices with at least a 50 percent response rate across all practice members. This approach helped ensure accurate measurement of burnout at the practice level, even in small or solo practices, and ensured representation across practice member roles, as position in the practice hierarchy may influence views on practice culture.²⁴ The practice member response rate was estimated by dividing the number of returned surveys by the number of practice members at the time of survey administration.

Participants And Procedures

Surveys were administered by EvidenceNOW cooperatives before practice interventions, from September 22, 2015, to June 19, 2017. A self-identified practice leader (for example, office manager or lead clinician) completed a practice survey eliciting practice characteristics, and all members of every participating practice were asked to complete a practice member survey to self-report burnout and perceptions of work environment. Cooperatives varied in the mode of survey administration (paper, web-based, or telephone) and incentive type (cash or gift card) and amount ($2–$75) for survey completion. All cooperatives ensured respondents’ confidentiality. A more detailed description of practice recruitment and survey data collection is provided elsewhere.²² This study was approved by the Institutional Review Board at Oregon Health and Science University and was registered as an observational study at ClinicalTrials.gov (NCT02560428).

Measures

The main outcome was burnout, aggregated to the practice level. Burnout was measured at the individual level by a single item: a five-point measure that correlates with the emotional exhaustion scale of the Maslach Burnout Inventory^25–27 and has been used in multiple studies.^{2,6,16,28–30} As defined in prior work, a person reporting a score of 3 or higher was considered to be experiencing burnout.⁶ At the practice level, we calculated the percentage of practice members who reported burnout.

In addition to burnout, practice members reported a single-item measure of psychological safety.³¹ Also, adaptive reserve, a fourteen-item measure of a practice’s capacity for organizational learning and development, was assessed both overall³² and individually, within each of six domains: facilitative leadership, work environment, teamwork, sensemaking, culture of learning, and relationship infrastructure. The practice adaptive reserve measure was developed for the evaluation of the patient-centered medical home National Demonstration Project³² and has been used as a measure of work climate in primary care.³³ Higher adaptive reserve has been associated with meaningful practice changes and guideline implementation.^34,35 Last, respondents indicated their professional category (that is, clinician [for example, physician, nurse practitioner, or physician assistant], clinical staff [for example, registered nurse, medical assistant, or behavioral health provider], nonclinical staff [for example, receptionist or billing staff], office manager, or other).

The practice survey assessed structural characteristics (that is, practice size, ownership, rurality, EHR capabilities, registry use, and participation in accountable care organizations [ACOs]); participation in other practice change initiatives (for example, the State Innovation Models initiative, Comprehensive Primary Care initiative, or Transforming Clinical Practice Initiative); and recent changes in the practice, including employee turnover and EHR implementation. These respondents also completed the change strategies section of the Change Process Capability Questionnaire,³⁶ a measure of quality improvement strategies used for cardiovascular disease prevention. Practice location was classified using rural-urban commuting area codes as defined by the Census Bureau.

Statistical Analysis

We conducted descriptive analyses to explore and describe characteristics of practices and individuals; aggregate prevalence of burnout was computed for each practice that met inclusion criteria. Next, we calculated two versions of intraclass correlations to determine within- and between-practice variation in burnout reported by individuals. The intraclass correlation (1), which estimates the proportion of variation in individual burnout explained by practice membership, was 0.104 (95% confidence interval: 0.085, 0.123), indicating a moderate level of agreement. The intraclass correlation (2), which estimates the reliability of practice-level mean burnout, was 0.603 (95% CI: 0.554, 0.645), providing sufficient justification for analyzing burnout at the practice level.^37–39

Next, we visually inspected the distribution of practices based on the aggregate prevalence of burnout among practice members to determine cutoffs for practices with “high” versus “low” burnout. We defined zero-burnout practices as those in which no practice members reported burnout and high-burnout practices as those with 40 percent or more of practice members reporting burnout. We then defined our primary outcome as a binary indicator denoting a practice as a zero-burnout practice versus a high-burnout practice, and practices with 1–39 percent of practice members reporting burnout were excluded.

We considered three sets of multivariable logistic regression models. First, we performed a practice-level multivariable logistic regression model of zero burnout versus high burnout as a function of structural factors. We used robust standard errors to account for the clustering of clinics within EvidenceNOW cooperatives. The second set of analyses evaluated practice member–level associations between cultural factors and zero-burnout versus high-burnout practices, controlling for the structural characteristics in the first model. Each adaptive reserve item and the single psychological safety item was dichotomized to collapse responses as “strongly agree or agree” versus “neutral or disagree or strongly disagree.” For each item in this set, we performed a logistic regression model with robust standard errors that accounted for clustering of practice members within practices and included fixed effects for cooperatives. The third set of analyses evaluated practice-level associations between the use of quality improvement strategies, EHR capabilities, and satisfaction and zero-burnout versus high-burnout practice status, using a multivariable logistic regression model with clustered standard errors, controlling for the structural characteristics included in the first model.

Although missing data on practice characteristics among our study sample was generally low (the average missingness across all practice characteristics was 7.7 percent), for all models we used multiple imputation by chained equations to account for missing data in practice-level characteristics.⁴⁰ Final estimated odds ratios of being in a zero-burnout practice and their corresponding 95% confidence intervals across fifty imputed data sets were combined using Rubin’s rules.

As sensitivity analyses, we compared our sample with excluded practices, estimated models without the requirement of a 50 percent response rate, and used an alternative definition of low burnout (0–10 percent). Out of concern that our definition of zero practice-level burnout would overidentify solo practices, we performed a descriptive sensitivity analysis of solo practices alone. All sensitivity analyses are in the online appendix.⁴¹ All analyses were conducted using R, version 4.0.3, and statistical significance was set at $p<0.05$.

Limitations

First, because this was a cross-sectional study, we cannot comment on causation or directionality. Second, the measure of burnout we used is a dichotomized single-item measure that correlates with the emotional exhaustion subscale of the Maslach Burnout Inventory,²⁷ not with the depersonalization scale. Although the use of multiple measures is a challenge to the field,⁴² our measure has been used in a similar manner in other large studies.^6,29 Third, we did not assess features of working conditions shown to have associations with burnout. Fourth, although we had a large and varied sample of primary care practices, our sample size was not large enough to support one model with many variables. Fifth, our sample consisted of practices participating in a large quality improvement initiative. Practices that choose to participate in an initiative such as EvidenceNOW might not be representative of all practices in the US. Finally, we limited our study to practices with at least three respondents that had a practice-level response rate of at least 50 percent. Although practice characteristics varied only slightly between included and excluded practices (appendix exhibit 1),⁴¹ practices with low response rates may suffer more from burnout, leading to underreporting of our outcome.

Study Results

Of 1,716 practices enrolled in the EvidenceNOW initiative, 1,495 submitted practice surveys at baseline. Among those that submitted baseline surveys, 715 practices (47.8 percent) met the criteria for inclusion. These practices, in aggregate, generated 7,740 completed individual-level surveys; the average response rate within each practice was 86.1 percent, with a mean of 10.8 surveys per practice.

Practice Characteristics

The percentage of practice members who reported burnout varied across practices (exhibit 1), with the average clinic reporting 18.6 percent of its practice members as being burned out. In 30 percent of practices ($n=214$), no practice members reported burnout (“zero-burnout” practices), whereas 13 percent of practices ($n=94$) had more than 40 percent of practice members report burnout.

SOURCE Authors’ analysis of survey data from practices participating in EvidenceNOW, 2017. NOTE $N=715$.

Practice-level characteristics are presented in exhibit 2. More than half of practices had two to five clinicians, and 17.3 percent were solo practices. More than 40 percent of practices were clinician owned, 26.3 percent were owned by hospitals or health systems, and 18.7 percent were federally qualified health centers. Practices were located within diverse settings, with 20.7 percent of practices found in rural areas and 42.5 percent located in a Medically Underserved Area. Practices reported a high level of participation in transformation initiatives, including 41.1 percent with patient-centered medical home recognition, 40.6 percent participating in an ACO, and 30.1 percent participating in other transformation initiatives. A comparison of practices included and excluded from the study is in appendix exhibit 1.⁴¹

Characteristics And Measures Associated With Zero-Burnout Practices

Adjusted odds ratios reflecting associations between practice-level characteristics and being a zero-burnout practice are presented in exhibit 3. Zero-burnout practices more commonly were solo and clinician owned and less commonly had participated in ACOs or other demonstration projects. Recent practice events, such as change in EHRs and loss of clinicians, were not associated with practice-level burnout. In addition, neither the number of patients seen per clinician per day nor the practice-level proportion of patients with Medicaid insurance was associated with practice-level burnout.

For every adaptive reserve item, practice members who reported “agree or strongly agree” had significantly higher odds of being in zero-burnout practices than in high-burnout practices (exhibit 4). Items related to leadership were most highly associated with zero-burnout practices (for example, “leadership in this practice creates an environment where things can be accomplished”), followed by both items related to work environment; adjusted odds ratios for these items all exceeded 5.0. All other items were higher in zero-burnout practices, including those related to teamwork, sensemaking, culture of learning, and relationship infrastructure. Finally, higher psychological safety was associated with zero-burnout practices (aOR: 3.18).

Zero-burnout practices reported using more quality improvement strategies than high-burnout practices (Change Process Capability Questionnaire strategies score mean: 10.8 versus 7.0). In multivariable models, a small increase in quality improvement strategies (2.3 Change Process Capability Questionnaire points) is associated with 1.08 odds of being a zero-burnout practice. EHR capabilities, such as using standard orders and using guideline-driven prompts and reminders, and satisfaction with the EHR were not different between zero- and high-burnout practices (appendix exhibit 2).⁴¹ Sensitivity analyses showed similar results with the restriction of 50 percent response rate removed and with low burnout defined as 0–10 percent burnout (appendix exhibits 3–5).⁴¹ In our sensitivity analysis of solo practices, characteristics associated with zero-burnout status were similar to those in the main analysis (appendix exhibit 6).⁴¹

Discussion

Zero-burnout practices, taken as a group, can be described as having a strong practice culture.

In our study of burnout in primary care practices, we discovered that zero-burnout practices, taken as a group, can be described as having a strong practice culture—one in which teamwork, communication, psychological safety, mindfulness of others, facilitative leadership, and understanding that people make and can learn from mistakes were among the key attributes, as measured by adaptive reserve. Zero-burnout practices reported using more quality improvement strategies, suggesting that they are better able engage in quality improvement. Finally, we found that structural characteristics such as size (solo practice), ownership (clinician), and nonparticipation in external transformation initiatives such as ACOs are associated with zero-burnout status, suggesting that agency is a protective factor for organizational and professional well-being.

Our study is consistent with prior work and extends it in several ways. Although studies have demonstrated that organizational characteristics correlate with burnout⁴³ and the effectiveness of clinic-level efforts to mitigate burnout,⁴⁴ many interventions have focused on individuals, teaching wellness-based practices, or providing individual coaching.⁴⁵ Our study demonstrates the feasibility and importance of measuring burnout among clinicians and staff as a group-level measure and reaffirms the importance of addressing burnout with organizational-level interventions, instead of tacitly shifting blame for burnout from the system to the individual.^46–49

We found that zero-burnout practices have higher levels of adaptive reserve and psychological safety, corroborating prior studies demonstrating the relationship between practice culture and burnout. Eric Williams and colleagues demonstrated the relationship between burnout and cultural factors such as workplace cohesiveness, communication, and organizational trust,⁴³ whereas Rachel Willard-Grace and colleagues found that a scale measuring cultural factors such as communication, social support, and shared objectives was associated with burnout.⁵⁰ Adaptive reserve includes the nature of leadership; work environment; teamwork; a culture of learning; and a relationship infrastructure built on rich communication, mindfulness, and reflection.⁵¹ Similar to other studies of practice culture in primary care,^50,52 we found that all items were significantly higher in zero-burnout practices, reinforcing the interrelated nature of practice culture domains.

Among adaptive reserve domains, we observed a particularly strong relationship between facilitative leadership and practice burnout. It has been shown that physician burnout is correlated with leadership skills of supervisors⁵³ and that lower burnout is associated with participatory decision making.⁶ Facilitative leadership contrasts with traditional leadership models that are hierarchical and rely on command-and-control mechanisms⁵⁴ and instead prioritizes fostering relationships, enhancing communication, attending to social influence and power imbalances, assuring psychological safety, and cultivating teamwork.⁵⁵ Facilitative leadership can also be understood as a characteristic of practices⁵⁶ that fosters emerging leadership skills among all practice members. Notably, we observed an association of leadership with zero burnout when we controlled for practice size and ownership. Larger practices and health systems can promote leadership and agency by delegating decision making to the lowest possible level of their organizations, and practices of all configurations could benefit from interprofessional leadership development.⁵⁷

We identified several important findings regarding practice capabilities and work environment. We found that the use of quality improvement strategies was higher overall in zero-burnout practices. Although these associations are cross-sectional, it is possible that practices that are less burned out are more able engage in quality improvement or that a focus on quality improvement could engage employees and improve burnout. We found no association between patient volume, as measured by patients seen per clinician per day, or the proportion of patients with Medicaid insurance and burnout. This is similar to prior work that showed no relationship between self-reported workload or measures of patient complexity and job satisfaction and stress.⁴³ However, many studies have shown strong relationships between perceived time pressure, chaotic work environment, low job control, and burnout,^{6–9,29,43,44} suggesting that some practices are able to manage workload and patient complexity in such a way that minimizes impact on clinician and staff burnout. Finally, we did not observe an association between recent practice changes, nor did we find an association between EHR features or satisfaction with EHRs and practice-level burnout. EHRs are criticized as contributing to burnout for many reasons, often related to increased asynchronous administrative work.^10,58 As EHRs are typically structured to prioritize financial concerns and the interests of payers and health systems over those of clinicians, staff, and patients,⁵⁹ clinicians’ complaints regarding EHRs may be frustration over the loss of agency the EHR represents. The fact that EHR features and satisfaction were not related to burnout in our data may reflect the ubiquity of EHR use in our sample and practices’ adaptation to EHR use.

Finally, we observed that structural factors were associated with zero-burnout practices. Specifically, solo practices, clinician-owned practices, and practices that did not participate in ACOs or other transformation initiatives were more commonly zero-burnout practices. Smaller practice arrangements with fewer employees may have better communication, stronger in-practice relationships, and increased agency, which together could contribute to less burnout. Although there has been a trend toward consolidation, smaller independent practices remain a critical component of primary care in the US,^60,61 with small practice models such as direct primary care emerging.⁶² Supporting solo and small clinician-owned practices could include modifying the policy and economic forces that have promoted consolidation of practices into larger systems,⁶³ such as funding primary care practice extension networks, which provide external technical support to practices, similar to the agricultural extension programs for farmers.⁶⁴

Conclusion

Zero-burnout practices had higher adaptive reserve with the highest scores for facilitative leadership; reported using more quality improvement strategies; and more commonly were solo, were clinician owned, and did not participate in ACOs or other transformation initiatives. These attributes are actionable and suggest that burnout improvement efforts should consider focusing on whole practices and the systems in which they are nested, on enhancing practice adaptive reserve and leadership development, and on supporting practice agency.

ACKNOWLEDGMENTS

All authors are supported by the Agency for Healthcare Research and Quality through Contract No. HHSA290201200019I, Grant No. R01 HS023940. Samuel Edwards was additionally supported by Department of Veterans Affairs Health Services Research & Development Grant No. CDA 16-152. Edwards is affiliated with the Center to Improve Veteran Involvement in Care, Veterans Affairs Portland Health Care System, in Portland, Oregon. The authors are grateful to the participating practices and EvidenceNOW Cooperatives for making this work possible. The ideas expressed in this article are solely those of the authors and do not represent any official position of the Department of Veterans Affairs.

NOTES

• 1 Maslach C , Jackson SE . The measurement of experienced burnout. J Organ Behav. 1981;2(2):99– 113. Crossref, Google Scholar
• 2 Puffer JC , Knight HC , O’Neill TR , Rassolian M , Bazemore AW , Peterson LE et al. Prevalence of burnout in board certified family physicians. J Am Board Fam Med. 2017;30(2):125– 6. Crossref, Medline, Google Scholar
• 3 Maslach C , Schaufeli WB , Leiter MP . Job burnout. Annu Rev Psychol. 2001;52:397– 422. Crossref, Medline, Google Scholar
• 4 Alarcon G , Eschleman KJ , Bowling NA . Relationships between personality variables and burnout: a meta-analysis. Work Stress. 2009;23(3):244– 63. Crossref, Google Scholar
• 5 Goldberg DG , Soylu TG , Grady VM , Kitsantas P , Grady JD , Nichols LM . Indicators of workplace burnout among physicians, advanced practice clinicians, and staff in small to medium-sized primary care practices. J Am Board Fam Med. 2020;33(3):378– 85. Crossref, Medline, Google Scholar
• 6 Helfrich CD , Dolan ED , Simonetti J , Reid RJ , Joos S , Wakefield BJ et al. Elements of team-based care in a patient-centered medical home are associated with lower burnout among VA primary care employees. J Gen Intern Med. 2014;29(Suppl 2):S659– 66. Crossref, Medline, Google Scholar
• 7 Khanna N , Montgomery R , Klyushnenkova E . Joy in work for clinicians and staff: identifying remedial predictors of burnout from the Mini Z survey. J Am Board Fam Med. 2020;33(3):357– 67. Crossref, Medline, Google Scholar
• 8 Rabatin J , Williams E , Baier Manwell L , Schwartz MD , Brown RL , Linzer M . Predictors and outcomes of burnout in primary care physicians. J Prim Care Community Health. 2016;7(1):41– 3. Crossref, Medline, Google Scholar
• 9 Portoghese I , Galletta M , Coppola RC , Finco G , Campagna M . Burnout and workload among health care workers: the moderating role of job control. Saf Health Work. 2014;5(3):152– 7. Crossref, Medline, Google Scholar
• 10 Gregory ME , Russo E , Singh H . Electronic health record alert-related workload as a predictor of burnout in primary care providers. Appl Clin Inform. 2017;8(3):686– 97. Crossref, Medline, Google Scholar
• 11 Panagioti M , Panagopoulou E , Bower P , Lewith G , Kontopantelis E , Chew-Graham C et al. Controlled interventions to reduce burnout in physicians: a systematic review and meta-analysis. JAMA Intern Med. 2017;177(2):195– 205. Crossref, Medline, Google Scholar
• 12 West CP , Dyrbye LN , Sinsky C , Trockel M , Tutty M , Nedelec L et al. Resilience and burnout among physicians and the general US working population. JAMA Netw Open. 2020;3(7):e209385. Crossref, Medline, Google Scholar
• 13 Mittelmark MB . The psychology of social influence and healthy public policy. Prev Med. 1999;29(6 Pt 2):S24– 9. Crossref, Medline, Google Scholar
• 14 Sinsky CA , Willard-Grace R , Schutzbank AM , Sinsky TA , Margolius D , Bodenheimer T . In search of joy in practice: a report of 23 high-functioning primary care practices. Ann Fam Med. 2013;11(3):272– 8. Crossref, Medline, Google Scholar
• 15 Linzer M , Sinsky CA , Poplau S , Brown R , Williams E Healthy Work Place Investigators . Joy in medical practice: clinician satisfaction in the Healthy Work Place trial. Health Aff (Millwood). 2017;36(10):1808– 14. Go to the article, Google Scholar
• 16 Rassolian M , Peterson LE , Fang B , Knight HC Jr , Peabody MR , Baxley EG et al. Workplace factors associated with burnout of family physicians. JAMA Intern Med. 2017;177(7):1036– 8. Crossref, Medline, Google Scholar
• 17 Montgomery A , Panagopoulou E , Esmail A , Richards T , Maslach C . Burnout in healthcare: the case for organisational change. BMJ. 2019;366:l4774. Crossref, Medline, Google Scholar
• 18 Creager J , Coutinho AJ , Peterson LE . Associations between burnout and practice organization in family physicians. Ann Fam Med. 2019;17(6):502– 9. Crossref, Medline, Google Scholar
• 19 Dreher A , Theune M , Kersting C , Geiser F , Weltermann B . Prevalence of burnout among German general practitioners: comparison of physicians working in solo and group practices. PLoS One. 2019;14(2):e0211223. Crossref, Medline, Google Scholar
• 20 Cuellar A , Krist AH , Nichols LM , Kuzel AJ . Effect of practice ownership on work environment learning culture psychological safety and burnout. Ann Fam Med. 2018;16(Suppl 1):S44– 51. Crossref, Medline, Google Scholar
• 21 Edwards ST , Marino M , Balasubramanian BA , Solberg LI , Valenzuela S , Springer R et al. Burnout among physicians, advanced practice clinicians, and staff in smaller primary care practices. J Gen Intern Med. 2018;33(12):2138– 46. Crossref, Medline, Google Scholar
• 22 Cohen DJ , Balasubramanian BA , Gordon L , Marino M , Ono S , Solberg LI et al. A national evaluation of a dissemination and implementation initiative to enhance primary care practice capacity and improve cardiovascular disease care: the ESCALATES study protocol. Implement Sci. 2016;11(1):86. Crossref, Medline, Google Scholar
• 23 Marsden PV , Landon BE , Wilson IB , McInnes K , Hirschhorn LR , Ding L et al. The reliability of survey assessments of characteristics of medical clinics. Health Serv Res. 2006;41(1):265– 83. Crossref, Medline, Google Scholar
• 24 McIlvain H , Crabtree B , Medder J , Stange KC , Miller WL . Using practice genograms to understand and describe practice configurations. Fam Med. 1998;30(7):490– 6. Medline, Google Scholar
• 25 Dolan ED , Mohr D , Lempa M , Joos S , Fihn SD , Nelson KM et al. Using a single item to measure burnout in primary care staff: a psychometric evaluation. J Gen Intern Med. 2015;30(5):582– 7. Crossref, Medline, Google Scholar
• 26 Rohland BM , Kruse GR , Rohrer JE . Validation of a single-item measure of burnout against the Maslach Burnout Inventory among physicians. Stress Health. 2004;20(2):75– 9. Crossref, Google Scholar
• 27 Knox M , Willard-Grace R , Huang B , Grumbach K . Maslach Burnout Inventory and a self-defined, single-item burnout measure produce different clinician and staff burnout estimates. J Gen Intern Med. 2018;33(8):1344– 51. Crossref, Medline, Google Scholar
• 28 Linzer M , Konrad TR , Douglas J , McMurray JE , Pathman DE , Williams ES et al. Managed care, time pressure, and physician job satisfaction: results from the Physician Worklife Study. J Gen Intern Med. 2000;15(7):441– 50. Crossref, Medline, Google Scholar
• 29 Linzer M , Manwell LB , Williams ES , Bobula JA , Brown RL , Varkey AB et al. Working conditions in primary care: physician reactions and care quality. Ann Intern Med. 2009;151(1):28– 36. Crossref, Medline, Google Scholar
• 30 Helfrich CD , Simonetti JA , Clinton WL , Wood GB , Taylor L , Schectman G et al. The association of team-specific workload and staffing with odds of burnout among VA primary care team members. J Gen Intern Med. 2017;32(7):760– 6. Crossref, Medline, Google Scholar
• 31 Edmondson A . Psychological safety, trust, and learning in organizations: a group-level lens. In: Kramer RM, Cook KS, editors. The Russell Sage Foundation series on trust and distrust in organizations: dilemmas and approaches. New York (NY): Russell Sage Foundation; 2004. p. 239– 72. Google Scholar
• 32 Jaén CR , Crabtree BF , Palmer RF , Ferrer RL , Nutting PA , Miller WL et al. Methods for evaluating practice change toward a patient-centered medical home. Ann Fam Med. 2010;8(Suppl 1):S9– 20, S92. Crossref, Medline, Google Scholar
• 33 Friedberg MW , Reid RO , Timbie JW , Setodji C , Kofner A , Weidmer B et al. Federally qualified health center clinicians and staff increasingly dissatisfied with workplace conditions. Health Aff (Millwood). 2017;36(8):1469– 75. Go to the article, Google Scholar
• 34 Tu SP , Young VM , Coombs LJ , Williams RS , Kegler MC , Kimura AT et al. Practice adaptive reserve and colorectal cancer screening best practices at community health center clinics in 7 states. Cancer. 2015;121(8):1241– 8. Crossref, Medline, Google Scholar
• 35 Nutting PA , Crabtree BF , Miller WL , Stange KC , Stewart E , Jaén C . Transforming physician practices to patient-centered medical homes: lessons from the national demonstration project. Health Aff (Millwood). 2011;30(3):439– 45. Go to the article, Google Scholar
• 36 Solberg LI , Asche SE , Margolis KL , Whitebird RR . Measuring an organization’s ability to manage change: the Change Process Capability Questionnaire and its use for improving depression care. Am J Med Qual. 2008;23(3):193– 200. Crossref, Medline, Google Scholar
• 37 James LR . Aggregation bias in estimates of perceptual agreement. J Appl Psychol. 1982;67(2):219– 29. Crossref, Google Scholar
• 38 LeBreton JM , Senter JL . Answers to 20 questions about interrater reliability and interrater agreement. Org Res Meth. 2007;11(4):815– 52. Google Scholar
• 39 Glick WH . Conceptualizing and measuring organizational and psychological climate—pitfalls in multilevel research. Acad Manage Rev. 1985;10(3):601– 16. Crossref, Google Scholar
• 40 White IR , Royston P , Wood AM . Multiple imputation using chained equations: issues and guidance for practice. Stat Med. 2011;30(4):377– 99. Crossref, Medline, Google Scholar
• 41 To access the appendix, click on the Details tab of the article online.
• 42 Rotenstein LS , Torre M , Ramos MA , Rosales RC , Guille C , Sen S et al. Prevalence of burnout among physicians: a systematic review. JAMA. 2018;320(11):1131– 50. Crossref, Medline, Google Scholar
• 43 Williams ES , Konrad TR , Linzer M , McMurray J , Pathman DE , Gerrity M et al. Physician, practice, and patient characteristics related to primary care physician physical and mental health: results from the Physician Worklife Study. Health Serv Res. 2002;37(1):121– 43. Medline, Google Scholar
• 44 Linzer M , Poplau S , Grossman E , Varkey A , Yale S , Williams E et al. A cluster randomized trial of interventions to improve work conditions and clinician burnout in primary care: results from the Healthy Work Place (HWP) Study. J Gen Intern Med. 2015;30(8):1105– 11. Crossref, Medline, Google Scholar
• 45 Dyrbye LN , Shanafelt TD , Gill PR , Satele DV , West CP . Effect of a professional coaching intervention on the well-being and distress of physicians: a pilot randomized clinical trial. JAMA Intern Med. 2019;179(10):1406– 14. Crossref, Medline, Google Scholar
• 46 Shanafelt T , Stolz S , Springer J , Murphy D , Bohman B , Trockel M . A blueprint for organizational strategies to promote the well-being of health care professionals. NEJM Catalyst [serial on the Internet]. 2020 Dec [cited 2021 Apr 15]. Available from: https://catalyst.nejm.org/doi/full/10.1056/CAT.20.0266 Google Scholar
• 47 Shanafelt TD , Noseworthy JH . Executive leadership and physician well-being: nine organizational strategies to promote engagement and reduce burnout. Mayo Clin Proc. 2017;92(1):129– 46. Crossref, Medline, Google Scholar
• 48 Shanafelt T , Goh J , Sinsky C . The business case for investing in physician well-being. JAMA Intern Med. 2017;177(12):1826– 32. Crossref, Medline, Google Scholar
• 49 Winston H , Fage B . Resilience, resistance: a commentary on the historical origins of resilience and wellness initiatives. Psychiatr Serv. 2019;70(8):737– 9. Crossref, Medline, Google Scholar
• 50 Willard-Grace R , Hessler D , Rogers E , Dubé K , Bodenheimer T , Grumbach K . Team structure and culture are associated with lower burnout in primary care. J Am Board Fam Med. 2014;27(2):229– 38. Crossref, Medline, Google Scholar
• 51 Nutting PA , Crabtree BF , Miller WL , Stewart EE , Stange KC , Jaén CR . Journey to the patient-centered medical home: a qualitative analysis of the experiences of practices in the National Demonstration Project. Ann Fam Med. 2010;8(Suppl 1):S45– 56, S92. Crossref, Medline, Google Scholar
• 52 Friedberg MW , Rodriguez HP , Martsolf GR , Edelen MO , Vargas Bustamante A . Measuring workplace climate in community clinics and health centers. Med Care. 2016;54(10):944– 9. Crossref, Medline, Google Scholar
• 53 Shanafelt TD , Gorringe G , Menaker R , Storz KA , Reeves D , Buskirk SJ et al. Impact of organizational leadership on physician burnout and satisfaction. Mayo Clin Proc. 2015;90(4):432– 40. Crossref, Medline, Google Scholar
• 54 Crabtree BF , Howard J , Miller WL , Cromp D , Hsu C , Coleman K et al. Leading innovative practice: leadership attributes in LEAP practices. Milbank Q. 2020;98(2):399– 445. Crossref, Medline, Google Scholar
• 55 Thygeson M , Morrissey L , Ulstad V . Adaptive leadership and the practice of medicine: a complexity-based approach to reframing the doctor-patient relationship. J Eval Clin Pract. 2010;16(5):1009– 15. Crossref, Medline, Google Scholar
• 56 Edmonstone J . Developing leaders and leadership in health care: a case for rebalancing? Lead Health Serv. 2011;24(1):8– 18. Crossref, Google Scholar
• 57 Geerts JM , Goodall AH , Agius S . Evidence-based leadership development for physicians: a systematic literature review. Soc Sci Med. 2020;246:112709. Crossref, Medline, Google Scholar
• 58 Agarwal SD , Pabo E , Rozenblum R , Sherritt KM . Professional dissonance and burnout in primary care: a qualitative study. JAMA Intern Med. 2020;180(3):395– 401. Crossref, Medline, Google Scholar
• 59 Hunt LM , Bell HS , Baker AM , Howard HA . Electronic health records and the disappearing patient. Med Anthropol Q. 2017;31(3):403– 21. Crossref, Medline, Google Scholar
• 60 Levine DM , Linder JA , Landon BE . Characteristics and disparities among primary care practices in the United States. J Gen Intern Med. 2018;33(4):481– 6. Crossref, Medline, Google Scholar
• 61 Liaw WR , Jetty A , Petterson SM , Peterson LE , Bazemore AW . Solo and small practices: a vital, diverse part of primary care. Ann Fam Med. 2016;14(1):8– 15. Crossref, Medline, Google Scholar
• 62 Eskew PM , Klink K . Direct primary care: practice distribution and cost across the nation. J Am Board Fam Med. 2015;28(6):793– 801. Crossref, Medline, Google Scholar
• 63 Kanter GP , Polsky D , Werner RM . Changes in physician consolidation with the spread of accountable care organizations. Health Aff (Millwood). 2019;38(11):1936– 43. Go to the article, Google Scholar
• 64 Ono SS , Crabtree BF , Hemler JR , Balasubramanian BA , Edwards ST , Green LA et al. Taking innovation to scale in primary care practices: the functions of health care extension. Health Aff (Millwood). 2018;37(2):222– 30. Go to the article, Google Scholar