Extracting Additional Influences From Physician Profiles With Topic Modeling: Impact on Ratings and Page Views in Online Healthcare Communities

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

Introduction

Throughout 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.

Methods

This 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.

Results

Users 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.

Discussion

This study has reference value for assisting user choice regarding medical treatment, decision-making by doctors, and online medical platform design.

Promotion strategy for online healthcare platform during the COVID-19 pandemic: Evidence from Spring Rain Doctor in China

INTRODUCTION

Online healthcare platform (OHP) is a new form of medical treatment that solves the problems of an unbalanced distribution of medical resources in China. Especially during the COVID-19 pandemic, OHP has greatly reduced the medical pressure of the hospital and the risk of cross-infection.

METHODS

Based on self-determination theory (SDT) (Ryan and Deci, 2000), privacy calculus theory (PCT) (Culnan, 1999) and perceived value theory (PVT) (Choi, 2004), this study uses evolutionary game theory to analyze behavioral strategies and their dynamic evolution in the promotion of OHP. Moreover, we conduct numerical simulations with the help of program compilation.

RESULTS

The results demonstrate that (1) both the qualification inspection of doctors and the investment in information protection influence doctors' participation in and patients' usage of OHP; (2) both the initial probabilities of doctor participation and patient usage influence the multi-game results; (3) the trend of doctors joining OHP is affected by registration cost, time cost, and reputation loss; and (4) the trend of patients using online healthcare is mainly decided by the cost.

CONCLUSION

This study takes the Spring Rain Doctor as an example to verify the game results. To further popularize online medical treatment among patients, the platform should attach importance to the inspection of doctors and the protection of privacy information and strengthen its publicity in remote places.

The Impact of Data Vulnerability in Online Health Communities: An Institutional Assurance Perspective

In the last few years, online health communities (OHCs) have experienced rapid development due to advances in technology and the COVID-19 pandemic. However, the sensitive nature of medical information has also raised concerns from users about their privacy and reduced their intention to use OHCs. Considering the critical role of data privacy, this study explored the effect of data vulnerability on OHC users. Using online survey data collected from 438 OHC users in China, we found that data vulnerability significantly reduced psychological comfort, while the latter enhanced continuance intention. We also found that psychological comfort negatively mediated the impact of data vulnerability on continuance intention. Institutional assurance approaches, namely privacy policy, privacy protection technology, industry self-regulation, and government legislation, were also found to mitigate the negative impact of data vulnerability on psychological comfort. This study not only contributes to the data privacy, psychological comfort, and institutional assurance literature but also offers suggestions for OHC stakeholders.