Temporal Associations Between EHR-Derived Workload, Burnout, and Errors: a Prospective Cohort Study
Open accessAbstract
Background The temporal progression and workload-related causal contributors to physician burnout are not well-understood. Objective To characterize burnout's time course and evaluate the effect of time-varying workload on burnout and medical errors. Design Six-month longitudinal cohort study with measurements of burnout, workload, and wrong-patient orders every 4 weeks. Participants Seventy-five intern physicians in internal medicine, pediatrics, and anesthesiology at a large academic medical center. Main measures Burnout was measured using the Professional Fulfillment Index survey. Workload was collected from electronic health record (EHR) audit logs and summarized as follows: total time spent on the EHR, after-hours EHR time, patient load, inbox time, chart review time, note-writing time, and number of orders. Wrong-patient orders were assessed using retract-and-reorder events. Key results Seventy-five of 104 interns enrolled (72.1%) in the study. A total of 337 surveys and 8,863,318 EHR-based actions were analyzed. Median burnout score across the cohort across all time points was 1.2 (IQR 0.7-1.7). Individual-level burnout was variable (median monthly change 0.3, IQR 0.1-0.6). In multivariable analysis, increased total EHR time (β=0.121 for an increase from 54.5 h per month (25th percentile) to 123.0 h per month (75th percentile), 95%CI=0.016-0.226), increased patient load (β=0.130 for an increase from 4.9 (25th percentile) to 7.1 (75th percentile) patients per day, 95%CI=0.053-0.207), and increased chart review time (β=0.096 for an increase from 0.39 (25th percentile) to 0.59 (75th percentile) hours per patient per day, 95%CI=0.015-0.177) were associated with an increased burnout score. After adjusting for the total number of ordering sessions, burnout was not statistically associated with an increased rate of wrong-patient orders (rate ratio=1.20, 95%CI=0.76-1.89). Conclusions Burnout and recovery were associated with recent clinical workload for a cohort of physician trainees, highlighting the elastic nature of burnout. Wellness interventions should focus on strategies to mitigate sustained elevations of work responsibilities.
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The 5-minute idle time threshold has been used in several studies analyzing raw log data.
“Notably, this threshold has been used in several studies analyzing raw log data.24,25,31,46,47”
From Electronic health record activity changes around new decision support implementation: monitoring using audit logs and topic modeling (2025) · cites this paper as [31]
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Kim, S., Lou, S., Cobb, A., Jha, S., & Kannampallil, T. (2026) · medRxiv · doi:10.64898/2026.06.24.26356449
Self-citationAtakan, K. B. M. (2024) · Özgür Yayınları eBooks · doi:10.58830/ozgur.pub599.c2571
Gender differences in secure chat and electronic health records use among hospital‐based physicians
Witte, M., McDermott, C., Levy, K. L., Nelsen, K., Shrestha, S., Tian, S., Paje, D. G., & Sheffield, V. M. (2025) · Journal of Hospital Medicine · doi:10.1002/jhm.70236
Association Between EHR-based Nursing Workload and Turnover: A Retrospective Cohort Study (Preprint)Preprint
Xia, L., Lew, D., Tessmer, L., Eiden, E., Lou, S. S., & Kannampallil, T. (2025) · doi:10.2196/preprints.89645
Self-citationWell-Being in GME: Finding Meaning in Medicine
Duncan, J. G., & Seashore, E. L. (2026) · Springer eBooks · doi:10.1007/978-3-032-12952-9_26
Self-citationXia, L., Lew, D., Tessmer, L., Eiden, E., Lou, S. S., & Kannampallil, T. (2026) · JMIR Nursing · doi:10.2196/89645
Self-citationAlmodallal, Y. A., Ramsy, N., Knake, L. A., & Schmitz, A. (2026) · Applied Clinical Informatics · doi:10.1055/a-2856-4821
Administrative Harm and Workforce Strain Indicators
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Salah, M., Karim, Y., Nabil, A., Adel, M., & Hassan, K. (2026) · Journal of Health Informatics and Digital Systems · doi:10.68159/n429666118
Kim, S., Lou, S. S., Cobb, A. D., Jha, S., & Kannampallil, T. (2026) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocag163
Self-citationWaken, R. J., Lou, S. S., Hofford, M. R., Eiden, E., Burk, C., Kim, S., Esker, J., Zhang, L., Maddox, T. M., Abraham, J., et al. (2026) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocag151
Self-citation
- Authors
- Lou, S. S., Lew, D., Harford, D. R., Lu, C., Evanoff, B. A., Duncan, J. G., & Kannampallil, T.
- Affiliations
- Washington University in St. Louis (US)
- Corresponding author
- Kannampallil, Thomas
- ORCID
- Venue
- Journal of General Internal Medicine
- Published
- 2022-06-16
- Volume
- 37
- Issue
- 9
- Pages
- 2165-2172
- ISSN
- 0884-8734 (ISSN-L)
- 1525-1497
- PMC
- PMC9296727
- OpenAlex
- W4282979369
- Cited by
- 46
- References
- 48
- Citations by year
- 2026: 10 · 2025: 13 · 2024: 12 · 2023: 6 · 2022: 5
- FWCI
- 10.33
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- Journal of General Internal Medicine (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Funders
- washington university/bjc healthcare (Big Ideas Healthcare Innovation Award)
- national institute of general medical sciences (5T32GM108539-07)
- Topics
- Health Sciences › Health Professions › General Health Professions › Healthcare professionals’ stress and burnout
- Health Sciences › Health Professions › Health Information Management › Electronic Health Records Systems
- Social Sciences › Psychology › Social Psychology › Stress and Burnout Research
- Keywords
- physician burnout, physician workload, medical errors, electronic health records, medical interns, patient load, burnout prevention, burnout treatment, burnout screening
- MeSH terms
- Burnout, Professional, Child, Humans, Longitudinal Studies, Prospective Studies, Workload, Electronic Health Records
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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.
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