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A natural language processing approach to uncover patterns among online ratings of otolaryngologists

Published online by Cambridge University Press:  20 March 2023

V Vasan Open the ORCID record for V Vasan [Opens in a new window] ,
Christopher P. Cheng ,
David K. Lerner ,
D Vujovic ,
M van Gerwen  and
A M Iloreta
V Vasan*
Affiliation:
Department of Otolaryngology-Head and Neck Surgery, Icahn School of Medicine at Mount Sinai, New York, NY, USA
Christopher P. Cheng
Affiliation:
Department of Otolaryngology-Head and Neck Surgery, Icahn School of Medicine at Mount Sinai, New York, NY, USA
David K. Lerner
Affiliation:
Department of Otolaryngology-Head and Neck Surgery, Icahn School of Medicine at Mount Sinai, New York, NY, USA
D Vujovic
Affiliation:
Department of Otolaryngology-Head and Neck Surgery, Icahn School of Medicine at Mount Sinai, New York, NY, USA
M van Gerwen
Affiliation:
Department of Otolaryngology-Head and Neck Surgery, Icahn School of Medicine at Mount Sinai, New York, NY, USA Institute for Translational Epidemiology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
A M Iloreta
Affiliation:
Department of Otolaryngology-Head and Neck Surgery, Icahn School of Medicine at Mount Sinai, New York, NY, USA
*
Corresponding author: Vikram Vasan; Email: vikram.vasan@icahn.mssm.edu
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Abstract

Background

Patients increasingly use physician rating websites to evaluate and choose potential healthcare providers. A sentiment analysis and machine learning approach can uniquely analyse written prose to quantitatively describe patients’ perspectives from interactions with their physicians.

Methods

Online written reviews and star scores were analysed from Healthgrades.com using a natural language processing sentiment analysis package. Demographics of otolaryngologists were compared and a multivariable regression for individual words was performed.

Results

This study analysed 18 546 online reviews of 1240 otolaryngologists across the USA. Younger otolaryngologists (aged less than 40 years) had higher sentiment and star scores compared with older otolaryngologists (p < 0.001). Male otolaryngologists had higher sentiment and star scores compared with female otolaryngologists (p < 0.001). ‘Confident’, ‘kind’, ‘recommend’ and ‘comfortable’ were words associated with positive reviews (p < 0.001).

Conclusion

Positive bedside manner was strongly reflected in better reviews, and younger age and male gender of the otolaryngologist were associated with better sentiment and star scores.

Keywords

Natural language processingonline ratingsotolaryngologyphysician rating websitessentiment analysis
Type
Main Article
Information
The Journal of Laryngology & Otology , Volume 137 , Issue 12 , December 2023 , pp. 1384 - 1388
Copyright
Copyright © The Author(s), 2023. Published by Cambridge University Press on behalf of J.L.O. (1984) LIMITED

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Footnotes

Mr V Vasan takes responsibility for the integrity of the content of the paper

*

Joint first authors

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