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Association of Electronic Health Record Inbasket Message Characteristics With Physician Burnout.
Baxter, Sally L; Saseendrakumar, Bharanidharan Radha; Cheung, Michael; Savides, Thomas J; Longhurst, Christopher A; Sinsky, Christine A; Millen, Marlene; Tai-Seale, Ming.
  • Baxter SL; Division of Ophthalmology Informatics and Data Science, Viterbi Family Department of Ophthalmology and Shiley Eye Institute, University of California, San Diego, La Jolla.
  • Saseendrakumar BR; Department of Medicine, University of California, San Diego, La Jolla.
  • Cheung M; Division of Ophthalmology Informatics and Data Science, Viterbi Family Department of Ophthalmology and Shiley Eye Institute, University of California, San Diego, La Jolla.
  • Savides TJ; Department of Family Medicine, University of California, San Diego, La Jolla.
  • Longhurst CA; Department of Medicine, University of California, San Diego, La Jolla.
  • Sinsky CA; Department of Medicine, University of California, San Diego, La Jolla.
  • Millen M; Department of Pediatrics, University of California, San Diego, La Jolla.
  • Tai-Seale M; American Medical Association, Chicago, Illinois.
JAMA Netw Open ; 5(11): e2244363, 2022 Nov 01.
Article in English | MEDLINE | ID: covidwho-2127464
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.
Subject(s)

Full text: Available Collection: International databases Database: MEDLINE Main subject: Electronic Health Records / COVID-19 Type of study: Experimental Studies / Observational study / Prognostic study / Randomized controlled trials Limits: Female / Humans / Male Language: English Journal: JAMA Netw Open Year: 2022 Document Type: Article

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Full text: Available Collection: International databases Database: MEDLINE Main subject: Electronic Health Records / COVID-19 Type of study: Experimental Studies / Observational study / Prognostic study / Randomized controlled trials Limits: Female / Humans / Male Language: English Journal: JAMA Netw Open Year: 2022 Document Type: Article