CoChat
Sign Up
Cited by 169Electronic Health Records SystemsHealthcare professionals’ stress and burnoutTechnostress in Professional Settings

Electronic Health Record Alert-Related Workload as a Predictor of Burnout in Primary Care Providers

Open access
Russo, Elise, Singh, Hardeep, Gregory, Megan E.·2017·Applied Clinical Informatics
DOI 10.4338/aci-2017-01-ra-0003PMC PMC6220682
Open this paper in CoChat — free accountOpen PDF

Abstract

Summary: Background: Electronic health records (EHRs) have been shown to increase physician workload. One EHR feature that contributes to increased workload is asynchronous alerts (also known as inbox notifications) related to test results, referral responses, medication refill requests, and messages from physicians and other health care professionals. This alert-related workload results in negative cognitive outcomes, but its effect on affective outcomes, such as burnout, has been under-studied. Objectives: To examine EHR alert-related workload (both objective and subjective) as a predictor of burnout in primary care providers (PCPs), in order to ultimately inform interventions aimed at reducing burnout due to alert workload. Methods: A cross-sectional questionnaire and focus group of 16 PCPs at a large medical center in the southern United States. Results: Subjective, but not objective, alert workload was related to two of the three dimensions of burnout, including physical fatigue (p = 0.02) and cognitive weariness (p = 0.04), when controlling for organizational tenure. To reduce alert workload and subsequent burnout, participants indicated a desire to have protected time for alert management, fewer unnecessary alerts, and improvements to the EHR system. Conclusions: Burnout associated with alert workload may be in part due to subjective differences at an individual level, and not solely a function of the objective work environment. This suggests the need for both individual and organizational-level interventions to improve alert workload and subsequent burnout. Additional research should confirm these findings in larger, more representative samples. Citation: 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: 686–697 https://doi.org/10.4338/ACI-2017-01-RA-0003

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.4338/aci-2017-01-ra-0003

Cites

8 of 8 references verified0 citation errors · 0 not checkable

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • Existing approaches for evaluating the impact of CDS on provider EHR-use activities and clinical workflow heavily rely on manual data collected through surveys, qualitative interviews, direct observation, and audio/video recording.

    “Existing approaches for evaluating the impact of CDS on provider EHR-use activities and clinical workflow heavily rely on manual data collected through surveys, qualitative interviews, direct observation, and audio/video recording.10,13,16–19”

    From Electronic health record activity changes around new decision support implementation: monitoring using audit logs and topic modeling (2025) · cites this paper as [16]

    The Procedure section describes a self-report questionnaire and semi-structured discussions used to study EHR alert workload, supporting this paper as an example of manual data collection. It does not establish that existing CDS evaluations heavily rely on these approaches, and its discussions were not recorded; the paper does not report direct observation or audio/video recording as methods used in this study. Our copy provides no page numbers for these passages. (LLM, conf=0.84)

    Experimental — accuracy not yet measured

  • Alert burden can lead to high rates of alert overrides and physician burnout (both physical and cognitive).

    “Alert burden can lead to high rates of alert overrides and physician burnout (both physical and cognitive).9,16,64–66”

    From Electronic health record activity changes around new decision support implementation: monitoring using audit logs and topic modeling (2025) · cites this paper as [16]

    Section 4.1 (page not available in our copy) reports that perceived alert burden predicted physical fatigue and cognitive weariness among primary care providers; measured time spent managing alerts did not. The paper's limitations note that its cross-sectional design cannot establish the direction of the effect. The passages on alert workload do not support the sentence's additional claim about high rates of alert overrides. (LLM, conf=0.88)

    Experimental — accuracy not yet measured

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Cites

Flagged first, then by citations

Research works printed but not linked: 28

  1. Poissant L, Pereira J, Tamblyn R, Kawasumi Y. The impact of electronic health records on time efficiency of physicians and nurses: A systematic review. J Am Med Inform Assoc 2005;12: 505-516.
  2. Murphy DR, Reis B, Sittig DF, Singh H. Notifications received by primary care practitioners in electronic health records: A taxonomy and time analysis. Am J Med 2012; 125: 209-e1-e7.
  3. Murphy DR, Meyer AN, Russo E, Sittig DF, Wei L, Singh H. The burden of inbox notifications in commer- cial electronic health records. JAMA Intern Med 2016; 176: 559-560.
  4. Singh H, Spitzmueller C, Petersen NJ, Sawhney MK, Smith MW, Murphy DR, Espadas D, Laxmisan A, Sit- tig DF. Primary care practitioners' views on test result management in EHR-enabled health systems: A national survey. J Am Med Inform Assoc 2012; 20: 727-735.
  5. Singh H, Spitzmueller C, Petersen NJ, Sawhney MK, Sittig DF. (2013). Information overload and missed test results in electronic health record-based settings. JAMA Intern Med 2013; 173: 702-704.
  6. Dunford BB, Shipp AJ, Boss RW, Angermeier I, Boss AD. Is burnout static or dynamic? A career transition perspective of employee burnout trajectories. J Appl Psychol 2012; 97: 637-650.
  7. Toker S, Biron M. Job burnout and depression: Unraveling their temporal relationship and considering the role of physical activity. J Appl Psychol 2012; 97: 699-710.
  8. Cordes CL, Dougherty TW. A review and an integration of research on job burnout. Acad Manage Rev 1993; 18: 621-656.
  9. Maslach C, Schaufeli WB, Leiter MP. Job burnout. Annu Rev Psychol 2001; 52: 397-422.
  10. McManus IC, Keeling A, Paice E. Stress, burnout and doctors' attitudes to work are determined by person- ality and learning style: A twelve year longitudinal study of UK medical graduates. BMC Med 2004; 2: 1-12.
  11. Brill PL. The need for an operational definition of burnout. Fam Community Health 1984; 6: 12-24.
  12. Hallsten L. Burning out: A framework. In: Schaufeli WB, Maslach C, Marek T, editors. Professional burn- out: Recent developments in theory and research. Philadelphia, PA: Taylor & Francis; 1993. p. 95-113.
  13. Buchbinder SB, Wilson M, Melick CF, Powe NR. Estimates of costs of primary care physician turnover. Am J Manag Care 1999; 5: 1431-1438.
  14. Haas JS, Cook EF, Puopolo AL, Burstin HR, Cleary PD, Brennan TA. Is the professional satisfaction of general internists associated with patient satisfaction?. J Gen Intern Med 2000; 15: 122-128.
  15. Dyrbye LN, Thomas MR, Massie FS, Power DV, Eacker A, Harper W, Duming S, Moutier C, Szydlo DW, Novotny PJ, Sloan JA, Shanafelt TD. Burnout and suicidal ideation among US medical students. Ann In- tern Med 2008; 149: 334-341.
  16. Shanafelt TD, Boone S, Tan L, Dyrbye LN, Sotile W, Satele D, West CP, Sloan J, Oreskovich MR. Burnout and satisfaction with work-life balance among US physicians relative to the general US population. Arch Intern Med 2012; 172: 1377-1385.
  17. Mauthe AJ, Boudreau RA, Lalumiere ML. Physician burnout: A "meta-o-scopic" analysis. Poster presented at the 8 th International Conference on Occupational Stress and Health; Puerto Rico; 2009.
  18. Van Emmerik IH, Jawahar IM. The independent relationships of objective and subjective workload with couples' mood. Hum Relat 2006; 59: 1371-1392.
  19. Hoonakker P, Carayon P, Gurses AP, Brown R, McGuire K, Khunlertkit A, Walker JM. (2011). Measuring workload of ICU nurses with a questionnaire survey: The NASA Task Load Index (TLX). IEE Trans Healthc Syst Eng 2011; 1: 131-143.
  20. Matthews G, Reinerman-Jones LE, Barber DJ, Abich J. The psychometrics of mental workload multiple measures are sensitive but divergent. Hum Factors 2015; 57: 125-143.
  21. Shirom A. Burnout in work organizations. In: Cooper CL, Robertson I, editors. International review of in- dustrial and organization psychology. Chichester, U.K.: Wiley 1989; 25-48.
  22. Shirom A. Job-related burnout: A review. In: Quick JC, Tetrick LE, editors. Handbook of occupational health psychology.Washington, DC: American Psychological Association 2003; 245-265.
  23. Schaufeli W, Le Blanc P. Personnel. In: Miranda DR, Ryan DW, Schaufeli WB, Fidler V, editors. Organi- sation and Management of Intensive Care: A Prospective Study in 12 European Countries. Berlin, Heidel- berg: Springer 1998; 169-205.
  24. Singh H, Wilson L, Reis B, Sawhney MK, Espadas D, Sittig DF. Ten strategies to improve management of abnormal test result alerts in the electronic health record. J Patient Saf 2010; 6: 121-123.
  25. Sonnentag S, Frese M. Stress in organizations. In: Schmitt NW, Highhouse S, editors. Handbook of Psy- chology. 2 nd ed. Vol 12. Hoboken, NJ: John Wiley & Sons, Inc. 2013; 560-592.
  26. Lowry SZ, Ramaiah M, Patterson ES, Brick D, Gurses AP, Ozok A, Simmons D, Gibbons MC. Integrating electronic health records into clinical workflow: An application of human factors modeling methods to ambulatory care. Proceedings of the International Symposium on Human Factors and Ergonomics in Health Care 2014; 3: 170-177.
  27. Foster B, Williams RB. Substitution of self-reporting for observing time spent on work activities by mental health professionals. Psychol Rep 1989; 64: 945-946.
  28. Simon SR, Kaushal R, Cleary PD, Jenter CA, Volk LA, Poon EG, Orav J, Lo HG, Williams DH, Bates DW. Correlates of electronic health record adoption in office practices: A statewide survey. J Am Med Inform Assoc 2007; 14: 110-117.

Cited by

Sorted by citations

Authors
Russo, E., Singh, H., & Gregory, M. E.
Affiliations
Michael E. DeBakey VA Medical Center (US)
Corresponding author
Gregory, Megan E.
ORCID
Venue
Applied Clinical Informatics
Publisher
Georg Thieme Verlag KG
Published
2017-07
Volume
08
Issue
03
Pages
686-697
ISSN
1869-0327 (ISSN-L)
DOI
10.4338/aci-2017-01-ra-0003
PMC
PMC6220682
OpenAlex
W2728189178
Cited by
169
References
8
Citations by year
2026: 8 · 2025: 22 · 2024: 12 · 2023: 26 · 2022: 23 · 2021: 26 · 2020: 26 · 2019: 20 · 2018: 6
FWCI
19.52
Type
journal article
Language
en
Open access
Locations
  • Applied Clinical Informatics (publishedVersion)
  • Europe PMC (PubMed Central) (publishedVersion)
  • PubMed Central (publishedVersion)
  • PubMed (publishedVersion)
Funders
VA National Center for Patient Safety, and the Agency for Health Care Research and Quality ((R01HS022087 and R21HS023602))
Topics
  • Health Sciences › Health Professions › Health Information Management › Electronic Health Records Systems
  • Health Sciences › Health Professions › General Health Professions › Healthcare professionals’ stress and burnout
  • Social Sciences › Psychology › Social Psychology › Technostress in Professional Settings
Keywords
electronic health records, clinical decision support alerts, burnout, primary care providers, physician fatigue, perceived workload, alert management, EHR usability
MeSH terms
Burnout, Professional, Humans, Time Factors, Workload, Electronic Health Records, Physicians, Primary Care

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review