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Figure.
Conceptual Framework of Association of Work Conditions and Electronic Health Record (EHR) Design and Use Factors With Clinician Outcomes
Conceptual Framework of Association of Work Conditions and Electronic Health Record (EHR) Design and Use Factors With Clinician Outcomes
Table 1.  
Respondent Demographic Characteristics
Respondent Demographic Characteristics
Table 2.  
Design and Use Factors of EHRs Associated With Stress and Burnout
Design and Use Factors of EHRs Associated With Stress and Burnout
Table 3.  
Univariate and Multivariable Models for Stress
Univariate and Multivariable Models for Stress
Table 4.  
Univariate and Multivariable Models for Burnout
Univariate and Multivariable Models for Burnout
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Shanafelt  TD, West  CP, Sinsky  C,  et al.  Changes in burnout and satisfaction with work-life integration in physicians and the general US working population between 2011 and 2017.  Mayo Clin Proc. 2019;S0025-6196(18)30938-8.PubMedGoogle Scholar
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Shanafelt  TD, Dyrbye  LN, Sinsky  C,  et al.  Relationship between clerical burden and characteristics of the electronic environment with physician burnout and professional satisfaction.  Mayo Clin Proc. 2016;91(7):836-848. doi:10.1016/j.mayocp.2016.05.007PubMedGoogle ScholarCrossref
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Young  RA, Burge  S, Kumar  KA, Wilson  J.  The full scope of family physicians’ work is not reflected by current procedural terminology codes.  J Am Board Fam Med. 2017;30(6):724-732. doi:10.3122/jabfm.2017.06.170155PubMedGoogle ScholarCrossref
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Kroth  PJ, Morioka-Douglas  N, Veres  S,  et al. MS-Squared Survey Instrument V 2.0. https://digitalrepository.unm.edu/ms2/3/. Accessed June 19, 2019.
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Motowidlo  SJ, Packard  JS, Manning  MR.  Occupational stress: its causes and consequences for job performance.  J Appl Psychol. 1986;71(4):618-629. doi:10.1037/0021-9010.71.4.618PubMedGoogle ScholarCrossref
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Williams  ES, Konrad  TR, Linzer  M,  et al; SGIM Career Satisfaction Study Group. Society of General Internal Medicine.  Refining the measurement of physician job satisfaction: results from the Physician Worklife Survey.  Med Care. 1999;37(11):1140-1154. doi:10.1097/00005650-199911000-00006PubMedGoogle ScholarCrossref
16.
Babbott  S, Manwell  LB, Brown  R,  et al.  Electronic medical records and physician stress in primary care: results from the MEMO Study.  J Am Med Inform Assoc. 2014;21(e1):e100-e106. doi:10.1136/amiajnl-2013-001875PubMedGoogle ScholarCrossref
17.
Linzer  M, Poplau  S, Grossman  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):1105-1111. doi:10.1007/s11606-015-3235-4PubMedGoogle ScholarCrossref
18.
Linzer  M, Poplau  S, Brown  R,  et al.  Do work condition interventions affect quality and errors in primary care? results from the Healthy Work Place Study.  J Gen Intern Med. 2017;32(1):56-61. doi:10.1007/s11606-016-3856-2PubMedGoogle ScholarCrossref
19.
Linzer  M, Gerrity  M, Douglas  JA, McMurray  JE, Williams  ES, Konrad  TR.  Physician stress: results from the Physician Worklife Study.  Stress Health. 2002;18(1):37-42. doi:10.1002/smi.917Google ScholarCrossref
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Perez  HR, Beyrouty  M, Bennett  K,  et al.  Chaos in the clinic: characteristics and consequences of practices perceived as chaotic.  J Healthc Qual. 2017;39(1):43-53. doi:10.1097/JHQ.0000000000000016PubMedGoogle ScholarCrossref
21.
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):75-79. doi:10.1002/smi.1002Google ScholarCrossref
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Eckleberry-Hunt  J, Kirkpatrick  H, Barbera  T.  The problems with burnout research.  Acad Med. 2018;93(3):367-370. doi:10.1097/ACM.0000000000001890PubMedGoogle ScholarCrossref
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Schwenk  TL, Gold  KJ.  Physician burnout: a serious symptom, but of what?  JAMA. 2018;320(11):1109-1110. doi:10.1001/jama.2018.11703PubMedGoogle ScholarCrossref
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Friedberg  MW, Chen  PG, Van Busum  KR,  et al.  Factors affecting physician professional satisfaction and their implications for patient care, health systems, and health policy.  Rand Health Q. 2014;3(4):1.PubMedGoogle Scholar
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Sinsky  CA, Beasley  JW, Simmons  GE, Baron  RJ.  Electronic health records: design, implementation, and policy for higher-value primary care.  Ann Intern Med. 2014;160(10):727-728. doi:10.7326/M13-2589PubMedGoogle ScholarCrossref
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Downing  NL, Bates  DW, Longhurst  CA.  Physician burnout in the electronic health record era: are we ignoring the real cause?  Ann Intern Med. 2018;169(1):50-51. doi:10.7326/M18-0139PubMedGoogle ScholarCrossref
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Fridsma  DB. Comments to the ONC and CMS. American Medical Informatics Association. https://www.amia.org/sites/default/files/AMIA-Response-to-ONC-HIT-Burden-Reduction-Strategy.pdf. Accessed February 26, 2019.
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Novacek  P., Design displays for better pilot reaction. http://aea.net/AvionicsNews/ANArchives/DesignDisplayOct03.pdf. Accessed July 9, 2019.
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Beasley  JW, Wetterneck  TB, Temte  J,  et al.  Information chaos in primary care: implications for physician performance and patient safety.  J Am Board Fam Med. 2011;24(6):745-751. doi:10.3122/jabfm.2011.06.100255PubMedGoogle ScholarCrossref
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Gidwani  R, Nguyen  C, Kofoed  A,  et al.  Impact of scribes on physician satisfaction, patient satisfaction, and charting efficiency: a randomized controlled trial.  Ann Fam Med. 2017;15(5):427-433. doi:10.1370/afm.2122PubMedGoogle ScholarCrossref
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Milford  J, Strasser  MR, Sinsky  CA.  TEAM approach reduced wait time, improved “face” time.  J Fam Pract. 2018;67(8):E1-E8.PubMedGoogle Scholar
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Zahabi  M, Kaber  DB, Swangnetr  M.  Usability and safety in electronic medical records interface design: a review of recent literature and guideline formulation.  Hum Factors. 2015;57(5):805-834. doi:10.1177/0018720815576827PubMedGoogle ScholarCrossref
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Alkureishi  MA, Lee  WW, Lyons  M,  et al.  Impact of electronic medical record use on the patient-doctor relationship and communication: a systematic review.  J Gen Intern Med. 2016;31(5):548-560. doi:10.1007/s11606-015-3582-1PubMedGoogle ScholarCrossref
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Stewart  RF, Kroth  PJ, Schuyler  M, Bailey  R.  Do electronic health records affect the patient-psychiatrist relationship? a before & after study of psychiatric outpatients.  BMC Psychiatry. 2010;10:3. doi:10.1186/1471-244X-10-3PubMedGoogle ScholarCrossref
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Wenzel  RP.  RVU medicine, technology, and physician loneliness.  N Engl J Med. 2019;380(4):305-307. doi:10.1056/NEJMp1810688PubMedGoogle ScholarCrossref
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 Ergonomic considerations loom large as hospitals and other health care organizations rapidly adopt IT tools.  ED Manag. 2013;25(3):31-32.PubMedGoogle Scholar
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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):1344-1351. doi:10.1007/s11606-018-4507-6PubMedGoogle ScholarCrossref
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    3 Comments for this article
    EXPAND ALL
    RE JAMA Network article by Kroth et al published on August 15, 2019
    John Peter Melrose, BA | CHARTSaaS, LLC
    The August 16, 2019, JAMA Network article by Philip Kroth MD MS et al (1) provides statistically significant substantiation of the thesis that cognitive overload, created and compounded by clinical chaos and complexity including electronic health record system (EHR) features and functions, is the root cause of medical mistakes leading to clinician stress, moral injury (2) and burnout. Therefore, the urgent need for mitigation of medical mistakes, long recognized but underestimated in the Institute of Medicine 1999 report “To err is human …” and now the third leading cause of preventable patient deaths in the USA (3), can be met with appropriate cognitive support. However, mitigation methods promulgated by reputable patient safety organizations (4) and in the literature (5) leverage human factors such as culture of safety, heuristics, rules of thumb and process improvement; all of which exacerbate cognitive overload.
    Combatting cognitive overload cost-effectively requires the real-time cognitive support that only state-of-the-art information technology deployed as mobile applications a.k.a. “apps” can deliver. For maximum economy, efficiency and effectiveness, these apps should be created and operated by healthcare provider clinical and administrative subject matter experts without technical expertise by using a software solution now commercially available – the mobile app development platform –to design, develop, deploy, operate and optimize mobile apps. These apps could automate problematic use cases such as differential diagnosis, treatment planning, sepsis management and handoff communication. Mobile apps can mitigate medical mistakes by integrating the logical requirements defined by the provider with functional capabilities of the mobile device such as photography, bar code interpretation, and geographic location; which cognitive support will minimize medical mistakes and optimize case outcomes, yielding much-improved physician and patient health.

    1) Kroth PL et al. Association of Electronic Health Record Design and Use Factors With Clinician Stress and Burnout. JAMA Network Open. 2019;2(8):e199609. doi:10.1001/jamanetworkopen.2019.9609. (jamanetwork.com)
    2) Talbot SG, Dean W. Physicians aren’t ‘burning out’ – They’re suffering from moral injury. Stat, Boston Globe Media, July 2018. (https://www.statnews.com/2018/07/26/physicians-not-burning-out-they-are-sufferingmoral-
    injury/)
    3) Makary MA, Daniel M. Medical error—the third leading cause of death in the US. BMJ;
    May 2016;353:i2139. (https://www.bmj.com/content/353/bmj.i2139)
    4) For example: Institute for Healthcare Improvement (http://www.ihi.org/), Plan-Do-Study-Act (PDSA); Joint Commission Center for Transforming Healthcare (https://www.centerfortransforminghealthcare.org/?_ga=2.173613731.1627866211.1566912294-1696677312.1559755943), Robust Process Improvement® (RPI®); Johns Hopkins Medicine Armstrong Institute for Patient Safety and Quality (https://www.hopkinsmedicine.org/armstrong_institute), Comprehensive Unit-Based Safety Program (CUSP); and Patient Safety Movement Foundation (https://patientsafetymovement.org/), Actionable Patient Safety Solution (APSS).
    5) Harry E et al. Cognitive Load and Its Implications for Health Care. NEJM Catalyst; March 14, 2018 (https://catalyst.nejm.org/cognitive-load-theory-implications-health-care/).
    CONFLICT OF INTEREST: None Reported
    READ MORE
    It's not the EHR
    Michael Roebuck, MD | University Hospital
    I found the conclusions interesting, especially in the context of the title... The title should be "everybody else's fingers in our EHRs is causing burnout". Here's the conclusions with my comments:

    1. The most prevalent concerns about EHR design and use were excessive data entry requirements (245 [86.9%]) -- this "excessive data entry requirement" is driven by somebody other than the EHR. These requirements are driven by CMS, OIG, Revenue Cycle, Legal protection, MIPS, MACRA, etc.... These requirements are not inherent to the EHR.
    1. Long cut-and-pasted notes (212 [75.2%]) -- providers are the ones who do this. Again,
    copy/paste isn't required to use an EHR.
    3. Inaccessibility of information from multiple institutions (206 [73.1%]) -- Typically EHRs are very available, unless IT departments make the decision to block access. Again, not the EHR.
    4. Notes geared toward billing (206 [73.1%]) -- not driven by the EHR
    5. Interference with work-life balance (178 [63.1%]) -- not the EHR problem that your personal life is out of balance with your work life.
    6. Problems with posture (144 [51.1%]) and pain (134 [47.5%]) attributed to the use of EHRs -- really? It's EHR's fault that you stoop when you write a note?

    It's time we embrace the EHRs, embrace the information they inject into the care cycle, and stop blaming the EHRs for our issues. These issues come from regulatory bodies, oversight agencies, insurance companies, auditors, etc... They are not inherent in the EHRs.
    CONFLICT OF INTEREST: None Reported
    READ MORE
    Some Suggestions for Relieving EHR Hassles
    Edward Volpintesta, MD |
    Part of the problem is that as physicians we like to be thorough and EHR makes that ‘thoroughness’ quicker and more complete than we could do by hand; unfortunately this can lead to patient consultations and discharge summaries over-saturated with data so that while it may make sense to the writer, it often confuses and can even mislead the reader.

    Here are some suggestions that may help.

    First. Strive for clarity and brevity

    Second. With all reports strive to make them narrative: imagine that you are talking to a colleague.

    Third. In discharge summaries
    include only relevant data, including lab tests, imaging studies, procedures, consultations, and follow up.
    CONFLICT OF INTEREST: None Reported
    READ MORE
    Original Investigation
    Health Informatics
    August 16, 2019

    Association of Electronic Health Record Design and Use Factors With Clinician Stress and Burnout

    Author Affiliations
    • 1University of New Mexico, Albuquerque
    • 2Stanford University, Palo Alto, California
    • 3Centura Health, Westminster, Colorado
    • 4University of Virginia, Charlottesville
    • 5Minneapolis Medical Research Foundation, Minneapolis, Minnesota
    • 6University of Utah, Salt Lake City
    • 7Uniformed Services University of the Health Sciences, Bethesda, Maryland
    • 8Hennepin County Medical Center, Minneapolis, Minnesota
    JAMA Netw Open. 2019;2(8):e199609. doi:10.1001/jamanetworkopen.2019.9609
    Key Points español 中文 (chinese)

    Question  Which electronic health record (EHR) design and use factors are associated with clinician stress and burnout?

    Findings  In this survey study of 282 clinicians, clinician stress and burnout were associated with 7 EHR design and use factors. These 7 plus 2 other design and use factors collectively accounted for a modest amount of the variance in stress (12.5%) and burnout (6.8%); models incorporating other work conditions (such as chaotic work atmosphere and workload control) accounted for considerably more of the variance in stress (58.1%) and burnout (36.2%).

    Meaning  While EHR design and use factors may appropriately be targeted by health systems and EHR designers to address stress and burnout, other non-EHR issues, especially clinician work conditions, appear to play a substantial role in adverse clinician outcomes.

    Abstract

    Importance  Many believe a major cause of the epidemic of clinician burnout is poorly designed electronic health records (EHRs).

    Objectives  To determine which EHR design and use factors are associated with clinician stress and burnout and to identify other sources that contribute to this problem.

    Design, Setting, and Participants  This survey study of 282 ambulatory primary care and subspecialty clinicians from 3 institutions measured stress and burnout, opinions on EHR design and use factors, and helpful coping strategies. Linear and logistic regressions were used to estimate associations of work conditions with stress on a continuous scale and burnout as a binary outcome from an ordered categorical scale. The survey was conducted between August 2016 and July 2017, with data analyzed from January 2019 to May 2019.

    Main Outcomes and Measures  Clinician stress and burnout as measured with validated questions, the EHR design and use factors identified by clinicians as most associated with stress and burnout, and measures of clinician working conditions.

    Results  Of 640 clinicians, 282 (44.1%) responded. Of these, 241 (85.5%) were physicians, 160 (56.7%) were women, and 193 (68.4%) worked in primary care. The most prevalent concerns about EHR design and use were excessive data entry requirements (245 [86.9%]), long cut-and-pasted notes (212 [75.2%]), inaccessibility of information from multiple institutions (206 [73.1%]), notes geared toward billing (206 [73.1%]), interference with work-life balance (178 [63.1%]), and problems with posture (144 [51.1%]) and pain (134 [47.5%]) attributed to the use of EHRs. Overall, EHR design and use factors accounted for 12.5% of variance in measures of stress and 6.8% of variance in measures of burnout. Work conditions, including EHR use and design factors, accounted for 58.1% of variance in stress; key work conditions were office atmospheres (β̂ = 1.26; P < .001), control of workload (for optimal control: β̂ = −7.86; P < .001), and physical symptoms attributed to EHR use (β̂ = 1.29; P < .001). Work conditions accounted for 36.2% of variance in burnout, where challenges included chaos (adjusted odds ratio, 1.39; 95% CI, 1.10-1.75; P = .006) and physical symptoms perceived to be from EHR use (adjusted odds ratio, 2.01; 95% CI, 1.48-2.74; P < .001). Coping strategies were associated with only 2.4% of the variability in stress and 1.7% of the variability in burnout.

    Conclusions and Relevance  Although EHR design and use factors are associated with clinician stress and burnout, other challenges, such as chaotic clinic atmospheres and workload control, explain considerably more of the variance in these adverse clinician outcomes.

    Introduction

    The adoption of the electronic health record (EHR) has occurred alongside the dramatic and troubling rise in clinician stress and burnout.1-3 This association has fueled the debate over the extent to which EHRs are associated with the epidemic of clinician stress and burnout. Technostress (ie, the stress related to technological tools in numerous industries) is real,4 but the degree to which it is a factor in medicine is largely unknown.

    The introduction of EHRs has resulted in shifting many clerical tasks to clinicians (eg, billing, coding, and quality control) as well as creating new tasks to be performed during clinical encounters (eg, data entry, computerized decision support, computerized order entry, and electronic prescribing). These new tasks have increased the cognitive and physical load on the clinician in many ways.5,6 For example, e-prescribing, which has benefits, has also created an additional burden by requiring clinicians to know where to route prescriptions at the time they prescribe. This may be a relatively small burden, but repeated multiple times per day and added to the myriad other tasks shifted to clinicians, these technology-enabled tasks have considerably increased clinician workload. In fact, an entirely new medical scribe industry has arisen in order to ameliorate the additional workload.7

    We designed this study (Minimizing Stress, Maximizing Success of the Electronic Health Record) to identify the relative contribution of aggregated EHR burdens compared with other burdens (ie, workplace chaos, control of workload) associated with clinician stress and burnout. This work is based on a conceptual framework derived from prior work (Figure).8 Our hypothesis was that EHR-associated stress adds to overall stress and could lead to burnout—which may play a role in the quality of patient care. In this study, we aim to understand which EHR design and use factors are associated with stress and burnout. The potentially challenging EHR design and use factors included in the survey instrument were identified through physician focus groups conducted in the first phase of the study.9 The design and use factors studied were intentionally limited to those over which clinicians and their institutions might have some control. This in no way minimizes other societal factors, such as governmental regulation and malpractice, that could be associated with clinician stress and burnout.10-12 This survey phase of our study quantifies the association of these EHR design and use factors with clinician stress and burnout to address the following questions: (1) what specific EHR design and use factors are most strongly associated with clinician stress and burnout? (2) What amount of overall stress and burnout is associated with EHRs? And, (3) what coping strategies or organizational solutions did respondents feel are important in addressing stress and burnout?

    Methods
    Identification of Challenging EHR Design and Use Factors

    The methods for this study have been previously reported.9 In brief, physician focus groups at 3 institutions (Stanford Hospital and Clinics, Stanford, California; University of New Mexico, Albuquerque; and Centura Health Physician Group, Westminster, Colorado) identified EHR design and use factors that were perceived as successful and those that were associated with user stress, burnout, or unintended physical symptoms. We also identified commonly used coping strategies by the clinicians.

    Survey and Sampling

    The EHR design and use factors identified in prior clinician focus groups informed the design of the survey instrument, which is freely available.13 The instrument included questions from previously validated instruments to measure stress, burnout, and other challenges identified by Motowidlo,14 the Physician Worklife Survey,15 the Minimizing Error, Maximizing Outcome Study,16 and the Healthy Work Place Study.17,18 Questions also focused on workplace characteristics such as workload control19 and work atmosphere (a single item measure from the Minimizing Error, Maximizing Outcome Study)20 as well as patient complexity and organizational culture, including value alignment between leaders and clinicians. This survey study complied with the American Association for Public Opinion Research (AAPOR) reporting guideline.

    The study survey instrument was pilot tested on 10 clinicians at Hennepin County Medical Center (Minneapolis, Minnesota). We then deployed the finalized instrument in 2 waves at the 3 focus group sites from August 9, 2016, through July 7, 2017. The institutional review boards at all participating institutions approved the study, and completing the survey was considered providing consent.

    We used REDCap version 8.10.7 (Vanderbilt University) to deploy an electronic version of the instrument. Nonresponders to the REDCap electronic survey were mailed paper instruments. The electronic instrument used continuous slider bars for respondents to indicate a score from 0 to 100, where 0 indicated not at all and 100 indicated to a great extent. The paper instrument used Likert scales mapped to the scale of 0 to 100 for analysis (ie, 1, not at all, mapped to 15; 2 mapped to 40; 3 mapped to 60; and 4, to a great extent, mapped to 85).

    The survey’s design attempted to determine the following: (1) perceived EHR successes, (2) EHR design and use factors associated with clinician stress and burnout, (3) perceived adverse personal outcomes (eg pain or anxiety), (4) things that could improve the EHR experience (eg, greater staff support, scribes, or fewer clicks per task), and (5) coping strategies (eg, exercise or setting boundaries). We sampled clinicians (physicians and advanced practice clinicians, including nurse practitioners and physician assistants) at 3 institutions from 5 disciplines: general internal medicine, medical subspecialties, general pediatrics, pediatric subspecialties, and family medicine. We excluded residents, as we thought they could have dissimilar experiences of stress and burnout than practicing clinicians. We determined respondent stress levels using the 4-item validated measures from Motowidlo,14 a continuous measure that ranges from 4 to 20, and burnout using the single-item validated measure from the Physician Worklife Study, in which a score of 3 or more indicates burnout.21 While a binary approach to burnout has been controversial,22,23 this measure has been used and validated in many settings and among thousands of respondents for 20 years, and it is associated with adverse work conditions and adverse clinician outcomes, such as intent to leave the practice. We ran additional analyses using the 5-choice measure of burnout as an ordered categorical (as opposed to binary) outcome and found no substantive differences between the 2 methods.

    Statistical Analysis

    Answers to survey questions were analyzed as standard summary statistics. We reported continuous variables as mean and SD and categorical variables as number of respondents and percentages of total sample.

    Linear regression was used to determine the association of focus group–identified variables (eg, work conditions, EHR design and use factors, and coping strategies) with clinician-reported stress, which we scored according to the Motowidlo 4-item measure,14 and burnout. β̂ was used to estimate the magnitude and direction of association, and it was calculated using the least-square estimation technique. We used logistic regression with stepwise selection, which is a combination of the forward and backward selection techniques, to estimate the association of focus group–identified variables with the odds of clinician-reported burnout, which we measured as a binary outcome based on a single question (with burnout representing endorsement of any choice with the word burnout in it).14 We used construct variables created to summarize the associations of variables within the same domain with stress and burnout. To develop the final regression model for stress, variables with R2 greater than 0.10 in the univariate analysis or that were determined to be of special interest were considered candidate variables for the multivariable model. The final logistic regression model for burnout used a stepwise selection technique, which was determined to be the most comprehensive method because it combines both forward and backward selection. To justify lumping together different types of clinicians and specialties, 1-way analysis of variance was used to examine if statistically significant differences existed in the means of outcome measures across clinician type (ie, MD, DO, nurse practitioner, or physician assistant) or specialty (ie, primary care, nonprocedural specialist, or procedural specialist). Diagnostics done on the regression and logistic models were the Breusch-Pagan test for constant variance and the Hosmer-Lemeshow test for goodness of fit, noting that P > .05 indicates having constant variance for the regression model and correct fit for the logistic model respectively. (These showed that the models were well calibrated.) Finally, we performed a statistical factor analysis using the varimax rotation method on 9 EHR design and use items to summarize the association of EHRs with stress and burnout. We used SAS version 9.4 (SAS Institute, Inc) for all analyses. Statistical significance was set at P < .05, and all tests were 2-tailed.

    Results
    Sample and Work-Life Balance Description

    Between August 2016 and July 2017, we surveyed 640 clinicians from 3 institutions, with 282 (44.1%) responding (208 [73.8%] electronically and 74 [26.2%] on paper); 160 (56.7%) were women, 241 (85.5%) were physicians (MDs and DOs), and 193 (68.4%) worked in primary care (Table 1). Overall, 256 respondents (90.8%) answered at least 95 of the 105 survey questions. The 1-way analysis of variance showed no significant difference in mean (SD) burnout between clinician types (DO, 2.33 [0.52]; MD, 2.54 [0.94]; nurse practitioner, 2.14 [0.53]; physician assistant, 2.45 [0.94]; P = .42) or between practice types (primary care, 2.51 [0.52]; nonprocedural specialist, 2.48 [0.82]; procedural specialist 2.59 [0.76]; P = .86). Therefore, neither of these components was controlled for in the analysis. Most participants noted stressful work conditions: 210 (74.5%) reported time pressure for documentation, and 170 (60.2%) spent moderately high or excessive time on the EHR at home (Table 1). Overall, 142 (50.4%) felt they had insufficient personal time, and 134 (47.5%) reported having minimal coverage for their EHR inboxes when needed. Only 95 (33.7%) reported that their practices emphasized work-life balance, while 215 (76.2%) said that productivity was overemphasized. Half (140 [49.6%]) reported marginal or poor control over workload, and 143 (50.7%) judged their office atmospheres as chaotic or tending toward chaotic. Almost half (127 [45.0%]) described symptoms of burnout, and 117 (41.5%) indicated they were moderately to definitely likely to leave their practices within 2 years (Table 1).

    Success and Challenges of the EHR

    The EHR successes participants identified included the ability to message colleagues electronically (197 [69.9%]), access to the EHR from home (213 [75.5%]), and the opportunity to share results with patients (136 [48.2%]). The most troublesome EHR design and use factors reported were excessive data entry requirements (245 [86.9%]), “note bloat” (unnecessarily long cut-and-pasted progress notes; 212 [75.2%]), inaccessible information from other institutions (206 [73.1%]), notes geared toward billing rather than patient care (206 [73.1%]), problems with work-life balance (178 [63.1%]), and 2 physical items that respondents attributed to EHR use: posture issues (144 [51.1%]) and pain (134 [47.5%]).

    Association of EHR Use and Design Factors With Stress and Burnout

    The EHR design and use factors significantly associated with high clinician stress were information overload (β̂ = 0.37; P < .001), slow system response times (β̂ = 0.42; P < .001), excessive data entry (β̂ = 0.43; P < .001), inability to navigate the system quickly (β̂ = 0.38; P < .001), note bloat (β̂ = 0.24; P = .01), fear of missing something (β̂ = 0.34; P < .001), interference with the patient-clinician relationship (β̂ = 0.29; P < .01), and notes geared toward billing (β̂ = 0.41; P < .001) (Table 2). In our analyses, burnout was used as a dichotomous as well as an ordered categorical variable, and there were no substantive differences between the 2 approaches. All of the previously listed EHR design and use factors were independently associated with burnout except fear of missing something. These factors collectively accounted for 12.5% and 6.8% of the variance in stress and burnout (as a binary outcome), respectively. Physical symptoms attributed to EHR use increased odds of burnout (adjusted odds ratio [aOR], 2.01; 95% CI, 1.48-2.75; P < .001)

    Other Factors Associated With Stress and Burnout

    Factors not related to EHRs associated with high levels of variance in stress were office atmospheres (β̂ = 1.26; P < .001), control of workload (for optimal control: β̂ = −7.86; P < .001), time for personal and family life (for disagree: β̂ = −2.30; P < .001), time for documentation at work (for satisfactory: β̂ = −2.93; P < .001), value alignment with leaders (for agree strongly: β̂ = −4.73; P < .001), professional and personal life balance (β̂ = −1.56; P < .001), physical symptoms attributed to EHR use (β̂ = 1.29; P < .001) and hours worked per week (β̂ = 0.78; P < .001). Within a multivariable linear regression model (Table 3), these variables, along with the EHR design and use factors listed in Table 2, consequences of EHR use, and EHR use at home, accounted for 58.1% of variance in clinician-reported stress and 36.2% of variance in burnout (Table 4). A chaotic work environment increased the odds of burnout (aOR, 1.39; 95% CI, 1.10-1.75; P = .006).

    Coping Strategies

    Coping strategies for reducing stress felt to be associated with the EHR included talking with others (194 [68.8%]), exercise (192 [68.1%]), setting work boundaries (161 [57.1%]), discussing EHR messages with others rather than pinging electronic messages back and forth (149 [52.8%]), and writing shorter notes (142 [50.4%]). As a combined variable, coping strategies accounted for only 2.4% and 1.7% of the variability in stress and burnout respectively (data not shown). Setting boundaries (β̂ = −0.02; P < .01) and taking breaks (β̂ = −0.02, P = .006) were independently associated with reductions in overall stress, while exercise (aOR, 0.99; 95% CI, 0.98-1.00; P = .04) and taking breaks (aOR, 0.99; 95% CI, 0.98-1.00; P = .003) were associated with reductions in the odds of burnout.

    Factor Analysis of EHR Stress Items

    We performed a statistical factor analysis using the varimax rotation method on the 9 EHR design and use factors listed in Table 2. We found that the first 2 statistical factors from the factor analysis accounted for 52.2% of the variability in EHR design and use items. We characterize these 2 factors as follows: (1) interference with patient care (eg, note bloat, interference with patient-clinician relationships, and notes geared toward billing) and (2) inefficient systems (eg, slow system response times, inability to navigate the system quickly, and excessive data entry). Thus, more than half of the variance in EHR issues associated with clinician stress and burnout stemmed from interference with patient care and inefficient EHR systems.

    Discussion

    In this cross-sectional survey of 282 clinicians from 3 health systems, we identified 7 EHR design and use factors associated with high stress and burnout. These were information overload, slow system response times, excessive data entry, inability to navigate the system quickly, note bloat, interference with the patient-clinician relationship, fear of missing something, and notes geared toward billing. While previous studies have identified several of these EHR design and use items as challenging to clinicians,9,24,25 we believe this study is the first to show an association between these factors and objectively validated stress and burnout scales.

    In this study, 45.0% of participants described symptoms of burnout, consistent with the findings of the national survey by Shanafelt et al2 in which 44% of physicians reported at least 1 symptom of burnout. The amounts of variation in stress and burnout associated with the EHR design and use factors listed in Table 2 were 12.5% and 6.8%, respectively. Thus, other sources of burnout aside from the EHR (such as lack of control of workload, chaotic environments, lack of attention to work-life balance, and ineffective teamwork) will also need to be addressed as medical practices seek to reduce burnout.

    Many of the identified EHR design and use factors may be remediable through a combination of improvements by EHR vendors, local improvements by information technology personnel, and training of clinicians in the clinical environment. However, some of the identified factors may require higher-level actions on the part of clinic or governmental policy makers, for example, by allowing notes to be more geared toward clinical care than billing practices. Documentation requirements for billing purposes is an EHR design characteristic associated with both stress and burnout. The length of clinical notes has essentially doubled since the enactment of the Health Information Technology for Economic and Clinical Health Act.26 Physicians outside the United States are more likely to report satisfaction with their EHRs, where clinical documentation is significantly shorter and contains much less information in support of billing and compliance.26 The American Medical Informatics Association has recently called for a long-term strategy from the US Department of Health and Human Services to decouple clinical documentation from billing, regulatory, and administrative compliance requirements.27

    Information overload may be associated with EHR design in which too much clinically unnecessary information is displayed. The aviation industry has a user interface design philosophy called quiet dark, where information is not displayed until something goes wrong or needs the pilot’s attention.28 In other words, the default state of all indicator lights is off during normal conditions. Applying this philosophy to EHR design could potentially reduce the amount of unnecessary data displayed based on particular users’ need and context, reducing the information overload problem. Arguably, the current state of EHR design is loud bright, where virtually all information, normal or otherwise, appears in relatively the same manner regardless of its importance to the clinician or patient. Although abnormal results from laboratory tests are highlighted, all normal values are typically displayed and occupy the same amount of space and are given the same prominence as abnormal results. Given the proliferation of standardized templates as a time-saving tool for data entry, the amount of unnecessary, repetitive, normal information (ie, note bloat) is increasing vs a design where an economy of information relevant to the patient’s current needs and context is used.29

    The data entry problem has created the scribe movement and produced promising results, at least in terms of clinician and patient satisfaction.30 However, scribes only help with data entry during office visits and not with EHR tasks at other times and in other venues. A more comprehensive approach is to use specially trained medical assistants (MAs) to relieve the clinician from clinic tasks (eg, responding to routine in-basket messages, refilling some prescriptions per protocol, completing paperwork). Before the clinician meets the patient, the MA completes prework (eg, medication reconciliation, review of systems, documentation of chief concern, and any protocolized clinical measurements, such as peak flows or pulse oximetry). The MA scribes during the clinician encounter, and after the clinician leaves the room, the MA can review the plan of care, deliver patient education, process referral requests, and schedule follow-up appointments.31

    Some of the troublesome EHR design and use factors, such as the inability to navigate the system quickly, are attributable to computer-human interaction problems. In fact, most of the current EHR user interface designs are still based on 2-dimensional paper metaphors (eg, tabs, flowsheets, tables, and forms) and do not take advantage of the potential of graphics capabilities now in the most basic computers.32 More research to determine what display metaphors beyond paper are most efficient could help. Complaints of interference with the patient-clinician relationship is evidence that clinicians are troubled by their excessive focus on the screen rather than the patient. While most studies have shown the presence of the EHR in the exam room does not adversely affect patient satisfaction,9,33,34 clinicians feel that EHRs requiring clinically irrelevant data entry take away from their relationships with their patients.35 Our study shows that this is significantly associated with clinician stress and burnout.

    The proportion of clinicians reporting pain (47.5%) and posture issues (51.1%) attributed to EHR use was high. Ergonomics are rarely addressed in most clinical settings. Clinicians often must work at several workstations, with different heights and seat structures. Collaboration with employee health groups skilled at ergonomics could potentially have a substantive effect on the health outcomes of our clinician workforce.36 This is an area ripe for further quality improvement studies.

    Coping strategies clinicians suggested to reduce EHR-associated stress included exercise (used by 68.1% of our sample), verbally discussing issues with other clinicians (68.8%), and setting boundaries for work while at home (57.1%). Setting boundaries, exercise, and taking breaks were significantly associated with reductions in overall stress and burnout and may be useful components to incorporate into stress reduction interventions. It is not clear how many of these strategies clinicians actually used or how effective they were at using them.

    Strengths and Limitations

    The strengths of this study include surveying a diverse group of clinicians, including academic, community-based, and rural institutions and practices, physicians and advanced practice clinicians, and a mix of specialists and nonspecialty ambulatory care clinicians. In addition, the list of the EHR design and use factors the clinicians rated in the survey was defined by clinicians in multi-institutional focus groups.9 The survey response rate (44.1%) was reasonable for large clinician-based studies with no financial incentive. The design of the instrument included questions previously validated in studies of physicians about stress and burnout.

    This study has limitations, including its cross-sectional nature and the use of self-reported metrics. One needs to consider response bias, given the 44.1% response rate. The relatively modest sample size limits validity. As respondents came from only 3 institutions, these results may not be more widely generalizable. The mapping of the paper instrument’s Likert scales to the REDCap slider bars scale may have introduced some bias. Despite using validated instruments to measure burnout and stress, the survey relied on the respondents’ own definitions. Self-reported metrics may underrepresent the numbers at risk. As Knox et al37 found, a self-defined, single-item burnout measure identified significantly fewer physicians most at risk of burning out compared with the Maslach Burnout Inventory. All respondents were grouped together for this analysis, which does not account for possible intragroup differences, such as between physicians and advanced practice clinicians.37

    Conclusions

    Stress and burnout associated with EHRs is prevalent and may be at least partly remediable at the local level. The issues identified in our list of EHR-associated challenges may provide designers, government regulators, and clinical leaders with targets for improvement of EHR design. Other work conditions are associated with stress and burnout in clinicians and deserve equal attention.

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    Article Information

    Accepted for Publication: July 1, 2019.

    Published: August 16, 2019. doi:10.1001/jamanetworkopen.2019.9609

    Open Access: This is an open access article distributed under the terms of the CC-BY License. © 2019 Kroth PJ et al. JAMA Network Open.

    Corresponding Author: Philip J. Kroth, MD, MS, Health Sciences Library and Informatics Center, University of New Mexico, MSC09 5100, One University of New Mexico, Albuquerque, NM 87131-0001 (pkroth@salud.unm.edu).

    Author Contributions: Drs Kroth and Linzer had full access to all of the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis.

    Concept and design: Kroth, Morioka-Douglas, Veres, Linzer.

    Acquisition, analysis, or interpretation of data: All authors.

    Drafting of the manuscript: Kroth, Poplau, Parshall, Corrigan, Linzer.

    Critical revision of the manuscript for important intellectual content: Kroth, Morioka-Douglas, Veres, Babbott, Qeadan, Linzer.

    Statistical analysis: Qeadan, Parshall.

    Obtained funding: Kroth, Veres, Poplau, Linzer.

    Administrative, technical, or material support: Kroth, Veres, Corrigan, Linzer.

    Supervision: Kroth.

    Conflict of Interest Disclosures: Dr Linzer reported receiving support for wellness research, training wellness champions, and scientific oversight from the American Medical Association, the American College of Physicians, the Institute for Healthcare Improvement, and the Association of Chiefs and Leaders in General Internal Medicine; receiving funds paid for salary, which went to Hennepin County Medical Center; and honoraria from Brown University and the University of Chicago, which were donated to the Health Foundation at Hennepin County Medical Center. No other disclosures were reported.

    Funding/Support: This project was supported by grant number R18HS022065 from the Agency for Healthcare Research and Quality.

    Role of the Funder/Sponsor: The funder had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.

    Disclaimer: The content is solely the responsibility of the authors and does not necessarily represent the official views of the Agency for Healthcare Research and Quality.

    Additional Contributions: Gale Hannigan, PhD (New Mexico Health Sciences Library and Informatics Center, Albuquerque, New Mexico), assisted with the multiple literature searches and text editing of this work, and Jeremiah Menk, MS (University of Minnesota, Minneapolis), assisted with biostatistics in the early phases of this project. They were compensated for their time.

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