A Natural Language Processing Approach to Uncover Patterns among Online Ratings of Otolaryngologists

Background: Patients increasingly use physician rating websites (PRW) to evaluate and choose potential healthcare providers. A sentiment analysis and machine learning approach can uniquely analyze written prose to quantitatively describe patients' perspectives from interactions with their physicians. Methods: Online written reviews and star scores were analyzed 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: 18,546 online reviews of 1,240 otolaryngologists across the US were analyzed. Younger otolaryngologists (<40 years old) had higher sentiment and star scores compared to older otolaryngologists (p<0.001). Male otolaryngologists had higher sentiment and star scores compared to 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.