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Physician Review Websites: Understanding Patient Satisfaction with Ophthalmologists Using Natural Language Processing

INTRODUCTION: The presence and influence of physician review websites (PRW) have increased significantly in the field of medicine. This study aims to better understand determinants of patient satisfaction and the sentiment of ophthalmologists using natural language processing of Healthgrades reviews...

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Autores principales: Jo, Jason J., Cheng, Christopher P., Ying, Stephanie, Chelnis, James G.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10017208/
https://www.ncbi.nlm.nih.gov/pubmed/36938345
http://dx.doi.org/10.1155/2023/4762460
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author Jo, Jason J.
Cheng, Christopher P.
Ying, Stephanie
Chelnis, James G.
author_facet Jo, Jason J.
Cheng, Christopher P.
Ying, Stephanie
Chelnis, James G.
author_sort Jo, Jason J.
collection PubMed
description INTRODUCTION: The presence and influence of physician review websites (PRW) have increased significantly in the field of medicine. This study aims to better understand determinants of patient satisfaction and the sentiment of ophthalmologists using natural language processing of Healthgrades reviews. METHODS: Healthgrades is a PRW where patients submit verified reviews, containing a star rating and a narrative review, of US-based ophthalmologists. This was a quantitative observational study conducted on May 23, 2022. We identified associations between physician demographics and both the sentiment analysis scores of narrative reviews and star ratings using the Student's t-tests and one-way ANOVA tests. After natural language processing the reviews, a logistic regression explored the impacts of the most frequent words on the positivity of a given review. RESULTS: This study examined a total of 16700 reviews of 1125 ophthalmologists. Ophthalmologists of younger age and male gender received statistically significantly higher star ratings and sentiment analysis scores; analysis of location of practice did not affect scores. Textual analysis revealed that words describing the physician's personality, such as “friendly” and “caring,” increased the likelihood of reviews being positive more than descriptors of the visit's effectiveness, such as “results” and “efficient.” CONCLUSION: Younger and male ophthalmologists received higher star ratings and sentiment analysis scores. Additionally, results indicated that words describing the ophthalmologist's pleasant personality and the visit's effectiveness most positively impacted a review, whereas descriptors of a wait or an unpleasant personality most negatively impacted a review.
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spelling pubmed-100172082023-03-16 Physician Review Websites: Understanding Patient Satisfaction with Ophthalmologists Using Natural Language Processing Jo, Jason J. Cheng, Christopher P. Ying, Stephanie Chelnis, James G. J Ophthalmol Research Article INTRODUCTION: The presence and influence of physician review websites (PRW) have increased significantly in the field of medicine. This study aims to better understand determinants of patient satisfaction and the sentiment of ophthalmologists using natural language processing of Healthgrades reviews. METHODS: Healthgrades is a PRW where patients submit verified reviews, containing a star rating and a narrative review, of US-based ophthalmologists. This was a quantitative observational study conducted on May 23, 2022. We identified associations between physician demographics and both the sentiment analysis scores of narrative reviews and star ratings using the Student's t-tests and one-way ANOVA tests. After natural language processing the reviews, a logistic regression explored the impacts of the most frequent words on the positivity of a given review. RESULTS: This study examined a total of 16700 reviews of 1125 ophthalmologists. Ophthalmologists of younger age and male gender received statistically significantly higher star ratings and sentiment analysis scores; analysis of location of practice did not affect scores. Textual analysis revealed that words describing the physician's personality, such as “friendly” and “caring,” increased the likelihood of reviews being positive more than descriptors of the visit's effectiveness, such as “results” and “efficient.” CONCLUSION: Younger and male ophthalmologists received higher star ratings and sentiment analysis scores. Additionally, results indicated that words describing the ophthalmologist's pleasant personality and the visit's effectiveness most positively impacted a review, whereas descriptors of a wait or an unpleasant personality most negatively impacted a review. Hindawi 2023-03-08 /pmc/articles/PMC10017208/ /pubmed/36938345 http://dx.doi.org/10.1155/2023/4762460 Text en Copyright © 2023 Jason J. Jo et al. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
Jo, Jason J.
Cheng, Christopher P.
Ying, Stephanie
Chelnis, James G.
Physician Review Websites: Understanding Patient Satisfaction with Ophthalmologists Using Natural Language Processing
title Physician Review Websites: Understanding Patient Satisfaction with Ophthalmologists Using Natural Language Processing
title_full Physician Review Websites: Understanding Patient Satisfaction with Ophthalmologists Using Natural Language Processing
title_fullStr Physician Review Websites: Understanding Patient Satisfaction with Ophthalmologists Using Natural Language Processing
title_full_unstemmed Physician Review Websites: Understanding Patient Satisfaction with Ophthalmologists Using Natural Language Processing
title_short Physician Review Websites: Understanding Patient Satisfaction with Ophthalmologists Using Natural Language Processing
title_sort physician review websites: understanding patient satisfaction with ophthalmologists using natural language processing
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10017208/
https://www.ncbi.nlm.nih.gov/pubmed/36938345
http://dx.doi.org/10.1155/2023/4762460
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