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Association of Electronic Health Record Inbasket Message Characteristics With Physician Burnout

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 unco...

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Autores principales: Baxter, Sally L., Saseendrakumar, Bharanidharan Radha, Cheung, Michael, Savides, Thomas J., Longhurst, Christopher A., Sinsky, Christine A., Millen, Marlene, Tai-Seale, Ming
Formato: Online Artículo Texto
Lenguaje:English
Publicado: American Medical Association 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9713605/
https://www.ncbi.nlm.nih.gov/pubmed/36449288
http://dx.doi.org/10.1001/jamanetworkopen.2022.44363
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author Baxter, Sally L.
Saseendrakumar, Bharanidharan Radha
Cheung, Michael
Savides, Thomas J.
Longhurst, Christopher A.
Sinsky, Christine A.
Millen, Marlene
Tai-Seale, Ming
author_facet Baxter, Sally L.
Saseendrakumar, Bharanidharan Radha
Cheung, Michael
Savides, Thomas J.
Longhurst, Christopher A.
Sinsky, Christine A.
Millen, Marlene
Tai-Seale, Ming
author_sort Baxter, Sally L.
collection PubMed
description 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.
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spelling pubmed-97136052022-12-22 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 JAMA Netw Open Original Investigation 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. American Medical Association 2022-11-30 /pmc/articles/PMC9713605/ /pubmed/36449288 http://dx.doi.org/10.1001/jamanetworkopen.2022.44363 Text en Copyright 2022 Baxter SL et al. JAMA Network Open. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the CC-BY License.
spellingShingle Original Investigation
Baxter, Sally L.
Saseendrakumar, Bharanidharan Radha
Cheung, Michael
Savides, Thomas J.
Longhurst, Christopher A.
Sinsky, Christine A.
Millen, Marlene
Tai-Seale, Ming
Association of Electronic Health Record Inbasket Message Characteristics With Physician Burnout
title Association of Electronic Health Record Inbasket Message Characteristics With Physician Burnout
title_full Association of Electronic Health Record Inbasket Message Characteristics With Physician Burnout
title_fullStr Association of Electronic Health Record Inbasket Message Characteristics With Physician Burnout
title_full_unstemmed Association of Electronic Health Record Inbasket Message Characteristics With Physician Burnout
title_short Association of Electronic Health Record Inbasket Message Characteristics With Physician Burnout
title_sort association of electronic health record inbasket message characteristics with physician burnout
topic Original Investigation
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9713605/
https://www.ncbi.nlm.nih.gov/pubmed/36449288
http://dx.doi.org/10.1001/jamanetworkopen.2022.44363
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