The Voice of Chinese Health Consumers: A Text Mining Approach to Web-Based Physician Reviews.
The Voice of Chinese Health Consumers: A Text Mining Approach to Web-Based Physician Reviews.
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DOI：
10.2196/jmir.4430
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发表时间：
2016-05-10
影响因子：
7.4
通讯作者：
Zhang K
中科院分区：
文献类型：
--
作者：
Hao H;Zhang K
许多基于网络的医疗保健平台允许患者根据他们的经验发布开放式文本评论来评估医生。这些评论是其他患者选择高质量医生的有用资源，特别是在中国这样没有医生转诊系统的国家。分析如此大量的用户生成的内容，以了解健康消费者的声音，引起了医疗保健提供者和医疗保健研究人员的极大关注。本文的目的是自动提取隐藏的主题，基于Web的医生评论使用文本挖掘技术来检查中国患者说他们的医生，以及这些主题是否在不同的专业不同。这些知识将有助于医疗保健消费者，提供者和研究人员更好地了解这些信息。我们对从中国最大的在线健康社区“好医生在线”平台收集的数据进行了双重分析。首先，我们使用描述性统计分析了2006-2014年的所有综述。其次，我们将著名的主题提取算法Latent Dirichlet Allocation应用于来自四个主要专业领域的75，000多名中国医生的500，000多篇文本评论，以了解中国健康消费者在线对他们的医生访问的看法。在“好医生在线”平台上，截至2014年4月11日，314，624名医生中有112，873人至少接受过一次审查。在772，979篇文本评论中，我们选择关注收到评论最多的四个主要专业领域：内科，外科，妇产科和儿科以及中国传统医学。在接受这四个医学专科的审查的医生中，三分之二的人接受了两次以上的审查，在少数极端情况下，一些医生接受了500多次审查。在这四个主要领域，评论者发现最受欢迎的话题是找医生的经历、医生的技术技能和床边态度、患者的普遍赞赏以及对各种症状的描述。据我们所知，我们的工作是第一个研究使用自动文本挖掘方法来分析大量的基于Web的医生评论的非结构化文本数据在中国。根据我们的分析，我们发现，中国的评论家主要集中在几个热门话题。这与中国在线健康平台的目标是一致的，也体现了中国医疗体系的医疗重点。我们的文本挖掘方法揭示了一个新的研究领域，即如何使用大数据帮助医疗保健提供者、医疗保健管理者和政策制定者听到患者的声音，针对患者的担忧，并在这个以患者为中心的护理时代提高护理质量。此外，在医疗保健消费者方面，我们的文本挖掘技术可以帮助患者在不阅读阅读数千篇评论的情况下做出更明智的决定。此外，我们对中美两国网络医生评论的比较分析也表明了一些文化差异。
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. 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. 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. 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. 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.