The Voice of Chinese Health Consumers: A Text Mining Approach to Web-Based Physician Reviews

Hao, Haijing; Zhang, Kunpeng

doi:10.2196/jmir.4430

Cited by 99 publications

(96 citation statements)

References 24 publications

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Section: Introductionmentioning

confidence: 97%

“…However, there are several differences between this study and that by Hao and Zhang [9]. First, in the study conducted by Hao and Zhang [9], only the data-driven approach was used to derive the topics. However, in this study, we use a mixed-methods approach consisting of a literature review, data-driven-based topic discovery, and human annotation approaches to rigorously and systematically develop a physician review taxonomy.…”

Section: Introductionmentioning

confidence: 97%

See 1 more Smart Citation

Developing Embedded Taxonomy and Mining Patients’ Interests From Web-Based Physician Reviews: Mixed-Methods Approach

Jia¹,

Liu²,

Li³

et al. 2018

J Med Internet Res

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BackgroundWeb-based physician reviews are invaluable gold mines that merit further investigation. Although many studies have explored the text information of physician reviews, very few have focused on developing a systematic topic taxonomy embedded in physician reviews. The first step toward mining physician reviews is to determine how the natural structure or dimensions is embedded in reviews. Therefore, it is relevant to develop the topic taxonomy rigorously and systematically.ObjectiveThis study aims to develop a hierarchical topic taxonomy to uncover the latent structure of physician reviews and illustrate its application for mining patients’ interests based on the proposed taxonomy and algorithm.MethodsData comprised 122,716 physician reviews, including reviews of 8501 doctors from a leading physician review website in China (haodf.com), collected between 2007 and 2015. Mixed methods, including a literature review, data-driven-based topic discovery, and human annotation were used to develop the physician review topic taxonomy.ResultsThe identified taxonomy included 3 domains or high-level categories and 9 subtopics or low-level categories. The physician-related domain included the categories of medical ethics, medical competence, communication skills, medical advice, and prescriptions. The patient-related domain included the categories of the patient profile, symptoms, diagnosis, and pathogenesis. The system-related domain included the categories of financing and operation process. The F-measure of the proposed classification algorithm reached 0.816 on average. Symptoms (Cohen d=1.58, Δu=0.216, t=229.75, and P<.001) are more often mentioned by patients with acute diseases, whereas communication skills (Cohen d=−0.29, Δu=−0.038, t=−42.01, and P<.001), financing (Cohen d=−0.68, Δu=−0.098, t=−99.26, and P<.001), and diagnosis and pathogenesis (Cohen d=−0.55, Δu=−0.078, t=−80.09, and P<.001) are more often mentioned by patients with chronic diseases. Patients with mild diseases were more interested in medical ethics (Cohen d=0.25, Δu 0.039, t=8.33, and P<.001), operation process (Cohen d=0.57, Δu 0.060, t=18.75, and P<.001), patient profile (Cohen d=1.19, Δu 0.132, t=39.33, and P<.001), and symptoms (Cohen d=1.91, Δu=0.274, t=62.82, and P<.001). Meanwhile, patients with serious diseases were more interested in medical competence (Cohen d=−0.99, Δu=−0.165, t=−32.58, and P<.001), medical advice and prescription (Cohen d=−0.65, Δu=−0.082, t=−21.45, and P<.001), financing (Cohen d=−0.26, Δu=−0.018, t=−8.45, and P<.001), and diagnosis and pathogenesis (Cohen d=−1.55, Δu=−0.229, t=−50.93, and P<.001).ConclusionsThis mixed-methods approach, integrating literature reviews, data-driven topic discovery, and human annotation, is an effective and rigorous way to develop a physician review topic taxonomy. The proposed algorithm based on Labeled-Latent Dirichlet Allocation can achieve impressive classification results for mining patients’ interests. Furthermore, the mining results reveal marked differences in patients’ inter...

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Section: Introductionmentioning

confidence: 97%

Section: Introductionmentioning

confidence: 97%

Developing Embedded Taxonomy and Mining Patients’ Interests From Web-Based Physician Reviews: Mixed-Methods Approach

Jia¹,

Liu²,

Li³

et al. 2018

J Med Internet Res

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“…Similar to the reviews of other products, patients share their experience in OHCs and such shared information is influential in other people in the community [8,61]. Recent work also has investigated OHC reviews and found that patients care about the qualities of clinicians [35], treatment effects [21], attitudes [21,34], and technical skills [21,22,34]. Additionally, doctors' ratings in OHCs are important for them to gain establish reputation [12] and improve their services [60].…”

Section: Online Health Communitiesmentioning

confidence: 99%

“…Examples include categorizing themes of the comments of social media users [4,27] and analyzing policy documents [19]. Recent work in Healthcare IS uses topic modeling to extract concepts from electronic health records [3,41], and analyze online reviews and user comments [21,22].…”

Section: Topic Modeling Analysis In Ismentioning

confidence: 99%

Why Do Consumers Review Doctors Online? Topic Modeling Analysis of Positive and Negative Reviews on an Online Health Community in China

Pang¹,

Liu²

2020

Proceedings of the Annual Hawaii International Conference on System Sciences

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Consumers often learn from others through a social learning process (e.g. electronic word of mouth) before making decisions. From the e-business perspective, online reviews have changed how people select products and services, and no doubt it is the same in the e-health sector. In this study, we examine online reviews of patients and health consumers for their doctors in an online health consultation platform in China. We combine machine learning and qualitative techniques to derive the themes of online reviews and the factors leading to positive and negative reviews. Our analysis demonstrates that service levels of hospitals, doctors' communication skills and their professional skills influence the sentiment of reviews. Our findings offer important insights into theories and practice for studying online reviews in the healthcare context.

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“…This phenomenon has received the attention of many researchers, and several studies of the OHC have been conducted focusing on various issues, such as how online physician reviews have been used in different countries [2][3][4][5][6][7]; what are the differences between the traditional and online physician reviews [8]; and whether differences in medical specialty areas affect these reviews [5,9]. Nevertheless, in China, even though the OHC might help consumers look for a good physician, the queues at Chinese hospitals are legendary [10], which means that it is not easy to make an appointment with a physician.…”

Section: Introductionmentioning

confidence: 99%

Understanding a Nonlinear Causal Relationship Between Rewards and Physicians� Contributions in Online Health Care Communities: Longitudinal Study (Preprint)

Wang¹,

Chiu²,

Yu³

et al. 2017

Preprint

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Background: The online health-care community (OHC) is not just a place for the public to share physician reviews or medical knowledge; it is also a physician-patient communication platform. The medical resources of developing countries are relatively inadequate, and the OHC is a potential solution to alleviate the phenomenon of long hospital queues and the lack of medical resources in rural areas. However, the success of the OHC depends on online contributions by physicians. Objective: The aim of this study is not only to understand physician online contribution behavior, but also to examine the helpfulness of incentive mechanisms. We addressed the following questions: (1) From which specialty area are physicians more likely to participate in OHC activities? (2) What are the factors affecting physician online contributions? (3) Do incentive mechanisms, including psychological and material rewards, result in differences of physician online contributions? (4) How can OHC managers exploit feasible incentive mechanisms to improve physician online contributions? Methods: We designed a longitudinal study involving a data sample in three waves. All data were collected from the Good Doctor website, which is the largest OHC in China. We first used descriptive statistics to investigate all of the physician online contribution behavior. Then multiple linear and quadratic regression models were applied to verify the causal relationship between rewards and physician online contribution. Results: Our sample includes 40,300 physicians from 3,607 different hospitals, 10 different major specialty areas, and 31 different provinces or municipalities. The average increments of physician online contribution scores in Gynecology-Obstetrics and Pediatrics specialty areas were 413.5 and 362.2, respectively, much higher than others. Based on the multiple quadratic regression model, we found that the coefficients of the control variables, past physician online contributions, doctor review rating, clinic title, hospital level, and city level, were 0.415, 0.189, -0.099, -0.106, and -0.143, respectively. For the psychological (material) rewards, the standardized coefficient of the main effect was 0.261 (0.688) and the standardized coefficient of the quadratic effect was -0.015 (-0.049). All estimates were statistically significantly at a 0.1% level. Conclusions: Our study discovered that the averages of physician online contribution were different in ten specialty areas. Physicians with more past physician online contribution, with higher review ratings, with lower clinic levels, not coming from tertiary hospitals, and not coming from big cities, were more willing to participate in OHC activities. To promote physician online contribution, it is necessary to establish an appropriate incentive mechanism including psychological and material rewards. Finally, our findings suggest two guidelines for designing a useful incentive mechanism to facilitate physician online contribution. First, material reward is more useful than psychological...

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The Voice of Chinese Health Consumers: A Text Mining Approach to Web-Based Physician Reviews

Cited by 99 publications

References 24 publications

Developing Embedded Taxonomy and Mining Patients’ Interests From Web-Based Physician Reviews: Mixed-Methods Approach

Developing Embedded Taxonomy and Mining Patients’ Interests From Web-Based Physician Reviews: Mixed-Methods Approach

Why Do Consumers Review Doctors Online? Topic Modeling Analysis of Positive and Negative Reviews on an Online Health Community in China

Understanding a Nonlinear Causal Relationship Between Rewards and Physicians� Contributions in Online Health Care Communities: Longitudinal Study (Preprint)

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