Physician review websites have significant 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. The objective of this study is to quantitatively analyze the written reviews of members of the Scoliosis Research Society (SRS) through sentiment analysis.
Online written reviews and star-rating reviews of SRS surgeons were obtained from healthgrades.com, and a sentiment analysis package was used to obtain compound scores of each physician’s reviews. A t test and ANOVA was performed to determine the relationship between demographic variables and average sentiment score of written reviews. Positive and negative word and word-pair frequency analysis was performed to provide context to words used to describe surgeons.
Seven hundred and twenty-one SRS surgeon’s reviews were analyzed. Analysis showed a positive correlation between the sentiment scores and overall average star-rated reviews (r2 = 0.5, p < 0.01). There was no difference in review sentiment by provider gender. However, the age of surgeons showed a significant difference as younger surgeons, on average, had more positive reviews (p < 0.01).
The most frequently used word pairs used to describe top-rated surgeons describe compassionate providers and efficiency in pain management. Conversely, those with the worst reviews are characterized 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 to properly manage pain expectations.
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All data are publicly available online review data.
Publicly available python packages and third-party web scrapers were used. Custom code was also used.
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Tang, J.E., Arvind, V., White, C.A. et al. What are patients saying about you online? A sentiment analysis of online written reviews on Scoliosis Research Society surgeons. Spine Deform (2021). https://doi.org/10.1007/s43390-021-00419-y
- Natural language processing
- Online reviews
- Patient satisfaction
- Machine learning