title: The Voice of Chinese Health Consumers: A Text Mining Approach to Web-Based Physician Reviews.
abstract: BACKGROUND
  Many Web-based health care platforms allow patients to evaluate physicians by posting open-end textual reviews based on their experiences. These reviews are helpful resources for other patients to choose high-quality doctors, especially in countries like China where no doctor referral systems exist. Analyzing such a large amount of user-generated content to understand the voice of health consumers has attracted much attention from health care providers and health care researchers.
  
  
  OBJECTIVE
  The aim of this paper is to automatically extract hidden topics from Web-based physician reviews using text-mining techniques to examine what Chinese patients have said about their doctors and whether these topics differ across various specialties. This knowledge will help health care consumers, providers, and researchers better understand this information.
  
  
  METHODS
  We conducted two-fold analyses on the data collected from the "Good Doctor Online" platform, the largest online health community in China. First, we explored all reviews from 2006-2014 using descriptive statistics. Second, we applied the well-known topic extraction algorithm Latent Dirichlet Allocation to more than 500,000 textual reviews from over 75,000 Chinese doctors across four major specialty areas to understand what Chinese health consumers said online about their doctor visits.
  
  
  RESULTS
  On the "Good Doctor Online" platform, 112,873 out of 314,624 doctors had been reviewed at least once by April 11, 2014. Among the 772,979 textual reviews, we chose to focus on four major specialty areas that received the most reviews: Internal Medicine, Surgery, Obstetrics/Gynecology and Pediatrics, and Chinese Traditional Medicine. Among the doctors who received reviews from those four medical specialties, two-thirds of them received more than two reviews and in a few extreme cases, some doctors received more than 500 reviews. Across the four major areas, the most popular topics reviewers found were the experience of finding doctors, doctors' technical skills and bedside manner, general appreciation from patients, and description of various symptoms.
  
  
  CONCLUSIONS
  To the best of our knowledge, our work is the first study using an automated text-mining approach to analyze a large amount of unstructured textual data of Web-based physician reviews in China. Based on our analysis, we found that Chinese reviewers mainly concentrate on a few popular topics. This is consistent with the goal of Chinese online health platforms and demonstrates the health care focus in China's health care system. Our text-mining approach reveals a new research area on how to use big data to help health care providers, health care administrators, and policy makers hear patient voices, target patient concerns, and improve the quality of care in this age of patient-centered care. Also, on the health care consumer side, our text mining technique helps patients make more informed decisions about which specialists to see without reading thousands of reviews, which is simply not feasible. In addition, our comparison analysis of Web-based physician reviews in China and the United States also indicates some cultural differences.
authors.affiliation: Department of Management Science and Information Systems, University of Massachusetts Boston, Boston, MA, United States. haohaijing@gmail.com.
authors.forename: Haijing
authors.identifier: http://orcid.org/0000-0003-3071-2538
authors.initials: H
authors.lastname: Hao
authors.forename: Kunpeng
authors.identifier: http://orcid.org/0000-0002-1474-3169
authors.initials: K
authors.lastname: Zhang
mesh_terms: D002681:China; D057225:Data Mining / Q000379:methods*; D005260:Female; D006801:Humans; D020407:Internet / Q000706:statistics & numerical data*; D008297:Male; D010820:Physicians; D017216:Telemedicine / Q000706:statistics & numerical data*; D060145:Text Messaging / Q000706:statistics & numerical data*
keywords: China health consumers; online doctor review; physician ratings; text mining