Abstract
Online reviews generated by patients on physician rating Websites (PRWs) have recently received much attention from physicians and their patients. In these reviews, patients exchange opinions as a diverse set of topics regarding different aspects of healthcare quality. This study aimed to propose a novel service quality-based text analytics (SQTA) model with other qualitative methods to mine different aspects of physicians and their clinical relevance in choosing a good doctor. Data included 45,560 online reviews that the authors scraped from a U.S.-based PRW (Healthgrades.com). The resulting topics demonstrate excellent classification results across different disease ranks, with overall accuracy and recall of 98%. The proposed classifier’s performance was 3% better than the existing topic classification methods applied in previous studies. The resulting clinically informative topics could help patients and physicians to maximize the usefulness of online reviews for efficient clinical decisions and improving the quality of care.
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