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

Abstract

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.