Physician's online image and patient's choice in the online health community

Ouyang, Peng; Wang, Jian‐Jun

doi:10.1108/intr-04-2021-0251

Cited by 26 publications

(12 citation statements)

References 83 publications

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“…With the arrival of the post-epidemic era, the field of digital healthcare is experiencing rapid development, making online medical service platforms gradually become an essential way for people to obtain medical services. Accompanied by the development of Web 2.0 technology and the imminent arrival of Web 3.0, online medical service platforms will further integrate intelligent technology to provide users with a more personalized and intelligent medical service experience [ 1 ]. With its unique characteristics, the online health community has become one of the essential channels for providing medical services and redistributing medical resources [ 2 ].…”

Section: Introductionmentioning

confidence: 99%

How do patients' perceptions and doctors' images impact patient decisions? Deconstructing online physician selection using multimodal data

Bai,

Tan,

Zhao

et al. 2024

Heliyon

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Section: Introductionmentioning

confidence: 99%

How do patients' perceptions and doctors' images impact patient decisions? Deconstructing online physician selection using multimodal data

Bai,

Tan,

Zhao

et al. 2024

Heliyon

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“…Many online medical platforms have sprung up in China, such as Chunyuyisheng.com and Haodf. com, both of which help users obtain medical services in some capacity (2). According to the 50th Statistical Report released by the China Internet Network Information Center, the number of online medical users reached 300 million as of June 2022, marking an increase of 1.96 million from the previous year and accounting for 28.5% of Internet users.…”

Section: Introductionmentioning

confidence: 99%

Topic evolution and sentiment comparison of user reviews on an online medical platform in response to COVID-19: taking review data of Haodf.com as an example

Yang

et al. 2023

Front. Public Health

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IntroductionThroughout the COVID-19 pandemic, many patients have sought medical advice on online medical platforms. Review data have become an essential reference point for supporting users in selecting doctors. As the research object, this study considered Haodf.com, a well-known e-consultation website in China.MethodsThis study examines the topics and sentimental change rules of user review texts from a temporal perspective. We also compared the topics and sentimental change characteristics of user review texts before and after the COVID-19 pandemic. First, 323,519 review data points about 2,122 doctors on Haodf.com were crawled using Python from 2017 to 2022. Subsequently, we employed the latent Dirichlet allocation method to cluster topics and the ROST content mining software to analyze user sentiments. Second, according to the results of the perplexity calculation, we divided text data into five topics: diagnosis and treatment attitude, medical skills and ethics, treatment effect, treatment scheme, and treatment process. Finally, we identified the most important topics and their trends over time.ResultsUsers primarily focused on diagnosis and treatment attitude, with medical skills and ethics being the second-most important topic among users. As time progressed, the attention paid by users to diagnosis and treatment attitude increased—especially during the COVID-19 outbreak in 2020, when attention to diagnosis and treatment attitude increased significantly. User attention to the topic of medical skills and ethics began to decline during the COVID-19 outbreak, while attention to treatment effect and scheme generally showed a downward trend from 2017 to 2022. User attention to the treatment process exhibited a declining tendency before the COVID-19 outbreak, but increased after. Regarding sentiment analysis, most users exhibited a high degree of satisfaction for online medical services. However, positive user sentiments showed a downward trend over time, especially after the COVID-19 outbreak.DiscussionThis study has reference value for assisting user choice regarding medical treatment, decision-making by doctors, and online medical platform design.

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“…Online healthcare consultation has become an essential part of healthcare system. Online medical platforms provide patients with a channel that allows them to make an appointment, learn about a physician, understand their severity of illness, and ask for advice on the Internet without having to leave home [ 1 , 2 , 3 ].…”

Section: Introductionmentioning

confidence: 99%

“…The Information of physicians contains the self-disclosed information, online image, etc. [ 1 , 2 , 4 , 5 ]. In addition, consumer value was co-created by the online medical community [ 6 ].…”

Section: Introductionmentioning

confidence: 99%

A Research Based on Online Medical Platform: The Influence of Strong and Weak Ties Information on Patients’ Consultation Behavior

2022

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As an indispensable part of contemporary medical services, Internet-based medical platforms can provide patients with a full range of multi-disciplinary and multi-modal treatment services. Along with the emergence of many healthcare influencers and the increasing connection between online and offline consultations, the operation of individual physicians and their teams on Internet-based medical platforms has started to attract a lot of attention. The purpose of this paper is to, based on an Internet platform, study how the information on physicians’ homepages influences patients’ consultation behavior, so as to provide suggestions for the construction of physicians’ personal websites. We distinguish variables into strong- and weak-ties types, dependent on whether deep social interactions between physicians and patients have happened. If there exist further social interactions, we define the variable as the “strong ties” type, otherwise, “weak ties”. The patients’ consultation behavior will be expressed as the volume of online consultation, i.e., the number of patients. We obtained the strong and weak ties information of each physician based on EWM (entropy weight method), so as to establish a regression model with explained variable, i.e., the number of patients, and three explanatory variables, i.e., the strong and weak ties information, and their interaction term. The estimation results verified our hypotheses and proved to be robust. It showed that both strong and weak ties information can positively influence patients’ consultation behavior, and the influence of weak ties information is greater. Regarding the positive influence of strong and weak ties, we found a trade off effect between them. Based on the results, we finalize with some suggestions on how to improve a physician’s online medical consultation volume.

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Physician's online image and patient's choice in the online health community

Cited by 26 publications

References 83 publications

How do patients' perceptions and doctors' images impact patient decisions? Deconstructing online physician selection using multimodal data

How do patients' perceptions and doctors' images impact patient decisions? Deconstructing online physician selection using multimodal data

Topic evolution and sentiment comparison of user reviews on an online medical platform in response to COVID-19: taking review data of Haodf.com as an example

A Research Based on Online Medical Platform: The Influence of Strong and Weak Ties Information on Patients’ Consultation Behavior

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