Evidence map›Paper›PMID 36449288›Full record

ArticleJAMA network open2022

Association of Electronic Health Record Inbasket Message Characteristics With Physician Burnout.

Sally L Baxter, Bharanidharan Radha Saseendrakumar, Michael Cheung, Thomas J Savides, Christopher A Longhurst, Christine A Sinsky, Marlene Millen, Ming Tai-Seale

Abstract read
In one paragraph

Article in JAMA network open, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
32citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

32 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Effect of digital tools to promote hospital quality and safety on adverse events after discharge.Journal of the American Medical Informatics Association : JAMIA · 2024
    Trial
  3. Article
  4. Article
  5. Article
  6. Article
  7. Article
  8. Review
  9. Article
  10. Patient E-Message Character Limit Impact on Primary Care Clinician Burnout.Journal of the American Board of Family Medicine : JABFM · 2026
    Article
  11. Article
  12. Article
  13. Review
  14. Article
  15. Article
  16. Article
  17. Article
  18. Article
  19. Article
  20. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Sally L BaxterDivision of Ophthalmology Informatics and Data Science, Viterbi Family Department of Ophthalmology and Shiley Eye Institute, University of California, San Diego, La Jolla.
Bharanidharan Radha SaseendrakumarDivision of Ophthalmology Informatics and Data Science, Viterbi Family Department of Ophthalmology and Shiley Eye Institute, University of California, San Diego, La Jolla.
Michael CheungDepartment of Family Medicine, University of California, San Diego, La Jolla.
Thomas J SavidesDepartment of Medicine, University of California, San Diego, La Jolla.
Christopher A LonghurstDepartment of Medicine, University of California, San Diego, La Jolla.
Christine A SinskyAmerican Medical Association, Chicago, Illinois.
Marlene MillenDepartment of Medicine, University of California, San Diego, La Jolla.
Ming Tai-SealeDepartment of Medicine, University of California, San Diego, La Jolla.

Funding

Vision BiostatisticsP30EY022589 · NEI · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI Derek Stuart Welsbie · 2012 to 2026
$10.3M
Multi-modal Health Information Technology Innovations for Precision Management of GlaucomaDP5OD029610 · OD · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI BAXTER, SALLY LIU · 2020 to 2024
$2.1M
NEI NIH HHS P30 EY022589NIH HHS DP5 OD029610
6 · The paper itself

Abstract

Importance: Physician burnout is an ongoing epidemic; electronic health record (EHR) use has been associated with burnout, and the burden of EHR inbasket messages has grown in the context of the COVID-19 pandemic. Understanding how EHR inbasket messages are associated with physician burnout may uncover new insights for intervention strategies. Objective: To evaluate associations between EHR inbasket message characteristics and physician burnout. Design, Setting, and Participants: Cross-sectional study in a single academic medical center involving physicians from multiple specialties. Data collection took place April to September 2020, and data were analyzed September to December 2020. Exposures: Physicians responded to a survey including the validated Mini-Z 5-point burnout scale. Main Outcomes and Measures: Physician burnout according to the self-reported burnout scale. A sentiment analysis model was used to calculate sentiment scores for EHR inbasket messages extracted for participating physicians. Multivariable modeling was used to model risk of physician burnout using factors such as message characteristics, physician demographics, and clinical practice characteristics. Results: Of 609 physicians who responded to the survey, 297 (48.8%) were women, 343 (56.3%) were White, 391 (64.2%) practiced in outpatient settings, and 428 (70.28%) had been in medical practice for 15 years or less. Half (307 [50.4%]) reported burnout (score of 3 or higher). A total of 1 453 245 inbasket messages were extracted, of which 630 828 (43.4%) were patient messages. Among negative messages, common words included medical conditions, expletives and/or profanity, and words related to violence. There were no significant associations between message characteristics (including sentiment scores) and burnout. Odds of burnout were significantly higher among Hispanic/Latino physicians (odds ratio [OR], 3.44; 95% CI, 1.18-10.61; P = .03) and women (OR, 1.60; 95% CI, 1.13-2.27; P = .01), and significantly lower among physicians in clinical practice for more than 15 years (OR, 0.46; 95% CI, 0.30-0.68; P < .001). Conclusions and Relevance: In this cross-sectional study, message characteristics were not associated with physician burnout, but the presence of expletives and violent words represents an opportunity for improving patient engagement, EHR portal design, or filters. Natural language processing represents a novel approach to understanding potential associations between EHR inbasket messages and physician burnout and may also help inform quality improvement initiatives aimed at improving patient experience.

Indexed as

COVID-19Electronic Health RecordsBurnout, PsychologicalCross-Sectional StudiesFemaleHumansMalePandemics

Identifiers

PMID36449288
PMCPMC9713605

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.