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

Overview

Journal Front Public Health

Specialty Public Health

Date 2023 Jun 19

PMID 37333543

Authors

Chaoyang Li

Shengyu Li

Jianfeng Yang

Jingmei Wang

Yiqing Lv

Affiliations

Soon will be listed here.

Abstract

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.

References

Ding L, Guo N, Zhu J, Tuxunjiang D, Abudoukeremuahong A, Zuo C . Prevalence and Associated Factors of Depression, Anxiety and Insomnia Symptoms Among Patients Receiving Ophthalmic Consultation Online During the COVID-2019 Pandemic. Front Psychiatry. 2022; 13:855366. PMC: 8977446. DOI: 10.3389/fpsyt.2022.855366. View

Lu N, Wu H . Exploring the impact of word-of-mouth about Physicians' service quality on patient choice based on online health communities. BMC Med Inform Decis Mak. 2016; 16(1):151. PMC: 5124243. DOI: 10.1186/s12911-016-0386-0. View

Ren D, Ma B . Effectiveness of Interactive Tools in Online Health Care Communities: Social Exchange Theory Perspective. J Med Internet Res. 2021; 23(3):e21892. PMC: 7998319. DOI: 10.2196/21892. View

Jiang X, Xie H, Tang R, Du Y, Li T, Gao J . Characteristics of Online Health Care Services From China's Largest Online Medical Platform: Cross-sectional Survey Study. J Med Internet Res. 2021; 23(4):e25817. PMC: 8051434. DOI: 10.2196/25817. View

Liu J, Zhang W, Jiang X, Zhou Y . Data Mining of the Reviews from Online Private Doctors. Telemed J E Health. 2019; 26(9):1157-1166. DOI: 10.1089/tmj.2019.0159. View

Liu Q, Zheng Z, Zheng J, Chen Q, Liu G, Chen S . Health Communication Through News Media During the Early Stage of the COVID-19 Outbreak in China: Digital Topic Modeling Approach. J Med Internet Res. 2020; 22(4):e19118. PMC: 7189789. DOI: 10.2196/19118. View

Hao H, Zhang K, Wang W, Gao G . A tale of two countries: International comparison of online doctor reviews between China and the United States. Int J Med Inform. 2017; 99:37-44. DOI: 10.1016/j.ijmedinf.2016.12.007. View

Zhang W, Deng Z, Hong Z, Evans R, Ma J, Zhang H . Unhappy Patients Are Not Alike: Content Analysis of the Negative Comments from China's Good Doctor Website. J Med Internet Res. 2018; 20(1):e35. PMC: 5806007. DOI: 10.2196/jmir.8223. View

Shah A, Yan X, Ali Shah S, Shah S, Mamirkulova G . Exploring the impact of online information signals in leveraging the economic returns of physicians. J Biomed Inform. 2019; 98:103272. DOI: 10.1016/j.jbi.2019.103272. View

10.

Son C, Hegde S, Smith A, Wang X, Sasangohar F . Effects of COVID-19 on College Students' Mental Health in the United States: Interview Survey Study. J Med Internet Res. 2020; 22(9):e21279. PMC: 7473764. DOI: 10.2196/21279. View

11.

Hao H . The development of online doctor reviews in China: an analysis of the largest online doctor review website in China. J Med Internet Res. 2015; 17(6):e134. PMC: 4526894. DOI: 10.2196/jmir.4365. View

12.

Jiang S . The Relationship between Face-to-Face and Online Patient-Provider Communication: Examining the Moderating Roles of Patient Trust and Patient Satisfaction. Health Commun. 2019; 35(3):341-349. DOI: 10.1080/10410236.2018.1563030. View

13.

Ge F, Qian H, Lei J, Ni Y, Li Q, Wang S . Experiences and Challenges of Emerging Online Health Services Combating COVID-19 in China: Retrospective, Cross-Sectional Study of Internet Hospitals. JMIR Med Inform. 2022; 10(6):e37042. PMC: 9162135. DOI: 10.2196/37042. View

14.

Ramaswamy A, Yu M, Drangsholt S, Ng E, Culligan P, Schlegel P . Patient Satisfaction With Telemedicine During the COVID-19 Pandemic: Retrospective Cohort Study. J Med Internet Res. 2020; 22(9):e20786. PMC: 7511224. DOI: 10.2196/20786. View

15.

Mann D, Chen J, Chunara R, Testa P, Nov O . COVID-19 transforms health care through telemedicine: Evidence from the field. J Am Med Inform Assoc. 2020; 27(7):1132-1135. PMC: 7188161. DOI: 10.1093/jamia/ocaa072. View

16.

Sieck C, Rastetter M, McAlearney A . Could Telehealth Improve Equity During the COVID-19 Pandemic?. J Am Board Fam Med. 2021; 34(Suppl):S225-S228. DOI: 10.3122/jabfm.2021.S1.200229. View

17.

Xue J, Chen J, Chen C, Zheng C, Li S, Zhu T . Public discourse and sentiment during the COVID 19 pandemic: Using Latent Dirichlet Allocation for topic modeling on Twitter. PLoS One. 2020; 15(9):e0239441. PMC: 7518625. DOI: 10.1371/journal.pone.0239441. View

18.

Karmaliani R, Asad N, Bann C, Moss N, McClure E, Pasha O . Prevalence of anxiety, depression and associated factors among pregnant women of Hyderabad, Pakistan. Int J Soc Psychiatry. 2009; 55(5):414-24. PMC: 3875176. DOI: 10.1177/0020764008094645. View

19.

Crunelle C, Van Daele T, Van Laere S, Blancke S, Vanderbruggen N, Matthys F . Remote Consultations for Mental Health: Patient Experiences. J Community Health. 2022; 48(2):347-352. PMC: 9713736. DOI: 10.1007/s10900-022-01175-4. View

20.

Zhou Y, Chen S, Liao Y, Wu Q, Ma Y, Wang D . General Perception of Doctor-Patient Relationship From Patients During the COVID-19 Pandemic in China: A Cross-Sectional Study. Front Public Health. 2021; 9:646486. PMC: 8290183. DOI: 10.3389/fpubh.2021.646486. View