How are Patients Describing You Online? A Natural Language Processing Driven Sentiment Analysis of Online Reviews on CSRS Surgeons

Justin Tang, Varun Arvind, Christopher A. White, Calista Dominy, Samuel Cho, Jun S. Kim

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

Study Design: A quantitative analysis of written, online reviews of Cervical Spine Research Society (CSRS) surgeons. Objective: This study quantitatively analyzes the written reviews of members of the CSRS to report biases associated with demographic factors and frequently used words in reviews to help aid physician practices. Summary of Background Data: Physician review websites have influence on a patient's selection of a provider, but written reviews are subjective. Sentiment analysis of writing through artificial intelligence can quantify surgeon reviews to provide actionable feedback. Methods: Online written and star-rating reviews of CSRS surgeons were obtained from healthgrades.com. A sentiment analysis package was used to obtain compound scores of each physician's reviews. The relationship between demographic variables and average sentiment score of written reviews were evaluated through t-tests. Positive and negative word and bigram frequency analysis was performed to indicate trends in the reviews' language. Results: In all, 2239 CSRS surgeon's reviews were analyzed. Analysis showed a positive correlation between the sentiment scores and overall average star-rated reviews (r 2=0.60, P<0.01). There was no difference in review sentiment by provider sex. However, the age of surgeons showed a significant difference as those <55 had more positive reviews (mean=+0.50) than surgeons >=55 (mean=+0.37) (P<0.01). The most positive reviews focused both on pain and behavioral factors, whereas the most negative focused mainly on pain. Behavioral attributes increased the odds of receiving positive reviews while pain decreased them. Conclusion: The top-rated surgeons were described as considerate providers and effective at managing pain in their most frequently used words and bigrams. However, the worst-rated ones were mainly described as unable to relieve pain. Through quantitative analysis of physician reviews, pain is a clear factor contributing to both positive and negative reviews of surgeons, reinforcing the need for proper pain expectation management. Level of Evidence: Level 4 - retrospective case-control study.

Original languageEnglish
Pages (from-to)E107-E113
JournalClinical Spine Surgery
Volume36
Issue number2
DOIs
StatePublished - 1 Mar 2023

Keywords

  • machine learning
  • online reviews
  • patient satisfaction
  • sentiment analysis

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