Using Sentiment Analysis to Understand What Patients Are Saying About Hand Surgeons Online

Tang, Justin E.; Arvind, Varun; White, Christopher A.; Dominy, Calista; Kim, Jun; Walsh, Amanda

doi:10.1177/15589447211060439

Cited by 10 publications

(18 citation statements)

References 18 publications

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“…Age analysis determined that younger ophthalmologists received higher star ratings and sentiment analysis scores than older ophthalmologists, a fnding that was in accordance with those of Smith et al [11]. Tis trend does not appear to be unique to the feld of ophthalmology, as Tang et al found similar results among reviews of hand and spine surgeons [14,15]. However, the geographic location of practice did not yield any statistically signifcant diferences with respect to star ratings or sentiment analysis scores.…”

Section: Discussionsupporting

confidence: 67%

“…Te R-squared value of this model was also used to optimize the minimum number of reviews required per physician to be included in this study. Tis method in determining the exclusion criteria was also used in the studies by Tang et al [14][15][16]. Te Student's t-tests were used to evaluate relationships between gender and both sentiment analysis scores and star ratings.…”

Section: Discussionmentioning

confidence: 99%

“…Finally, the scores are normalized to a compound score that ranges from a negative one to a positive one. We selected VADER given its past uses in online patient review analysis as well as its sensitivity to the language and format of social media platforms [14][15][16].…”

Section: Natural Language Processing (Vader Sentiment Analysismentioning

confidence: 99%

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

Cheng

Ying

et al. 2023

Journal of Ophthalmology

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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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Section: Discussionsupporting

confidence: 67%

Section: Discussionmentioning

confidence: 99%

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

Cheng

Ying

et al. 2023

Journal of Ophthalmology

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“…Although natural language processing has been previously applied to quantitatively characterise online patient reviews in other fields, such as spine surgery and hand surgery, it has not been used to evaluate online reviews of otolaryngologists from physician rating websites. 12,13 The current study therefore aimed to use a natural language processing approach to analyse physician rating website reviews of otolaryngologists in the USA and quantitatively describe patients' perspectives from interactions with their physicians.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2023

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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.

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“…The nursing notes written by clinicians include important information on the health status of patients. Sentiment analysis, which is a technique that processes natural language, helps to identify the attitudes or impressions of clinicians to patients using computational algorithms for extracting subjective information in the written text and classifying subjective properties ( 13 ). In previous studies, nursing notes were applied in prediction model to improve the clinical outcome prediction of patients with various diseases ( 12 , 14 , 15 ).…”

Section: Introductionmentioning

confidence: 99%

Development and validation of prediction model using nursing notes on sentiment scores for prognosis of patients with severe acute kidney injury receiving continuous renal replacement therapy based on computational intelligence algorithms

Zha¹,

Yang²,

Hai-fen³

et al. 2022

Ann Transl Med

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Background: Currently, the prediction values of models for the prognosis of acute kidney injury (AKI) receiving continuous renal replacement therapy (CRRT) were ordinary and establishing a better prediction model is necessary. Nursing notes are an important predictor of in-hospital mortality in intensive care unit (ICU) patients. This study established prognostic prediction models for AKI patients receiving CRRT especially using nursing notes.Methods: Totally, 682 AKI patients undergoing CRRT were included. AKI was diagnosed based on Kidney Disease: Improving Global Outcomes (KDIGO) criteria. Four hundred and twelve patients lacking nursing notes data were excluded. Finally, 270 patients were included and randomly divided into a training set (n=189) and a testing set (n=81) at a ratio of 7:3. Univariate analysis explored the possible predictors of mortality in AKI patients receiving CRRT. Random forest models and broad learning system (BLS) models (with or without sentiment scores) were respectively constructed in the training set and verified in the testing set. The performances of the models were assessed by the sensitivity, specificity, and area under the curve (AUC).Results: For the random forest model including the sentiment scores, the AUC was 0.86 (95% CI: 0.81-0.91), the sensitivity was 0.72 (95% CI: 0.63-0.80), and the specificity was 0.87 (95% CI: 0.80-0.94) in the training set and the AUC was 0.78 (95% CI: 0.68-0.88), the sensitivity was 0.65 (95% CI: 0.49-0.80), and the specificity was 0.75 (95% CI: 0.62-0.88) in the testing set. For the BLS model including the sentiment scores, the AUC was 0.87 (95% CI: 0.82-0.92), the sensitivity was 0.95 (95% CI: 0.91-0.99) and the specificity was 0.48 (95% CI: 0.38-0.59) in the training set and the AUC was 0.82 (95% CI: 0.73-0.91), the sensitivity was 0.41 (95% CI: 0.25-0.56) and the specificity was 0.98 (95% CI: 0.93-1.00) in the testing set. Conclusions:The BLS models including the sentiment scores might offer a tool for quickly identifying patients AKI patients receiving CRRT with high risk of mortality and providing timely interventions to them for improving their prognosis.

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Using Sentiment Analysis to Understand What Patients Are Saying About Hand Surgeons Online

Cited by 10 publications

References 18 publications

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

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

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

Development and validation of prediction model using nursing notes on sentiment scores for prognosis of patients with severe acute kidney injury receiving continuous renal replacement therapy based on computational intelligence algorithms

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