我国公众对远程医疗的态度变化研究——基于多时段微博文本数据分析
Research on the Evolution of Public Attitudes towards Telemedicine in China—A Multi-Period Analysis of Weibo Text Data
DOI: 10.12677/orf.2024.146548, PDF,    科研立项经费支持
作者: 倪载健, 钱 颖:上海理工大学管理学院,上海
关键词: 远程医疗文本数据主题挖掘情感分析COVID-19Telemedicine Text Data Topic Mining Sentiment Analysis COVID-19
摘要: 目的/意义:远程医疗在疫情期间发挥了重要作用,避免了公众在医院聚集而引起交叉感染,为隔离中的人们提供医疗服务,得到了大量使用。然而公众对远程医疗的态度发生了何种变化,尚未得到深入研究。方法/过程:我们以“远程医疗”、“在线问诊”和“互联网医院”为关键词,收集了2018年1月1日至2024年4月30日期间的微博数据。利用融合word2vec的LDA主题模型和Bi-LSTM-Attention情感分析方法,探讨了疫情前、疫情中、疫情后公众对远程医疗的态度变化。结果/结论:结果显示,公众对远程医疗的态度由疫情前的“不信任”演化为“在疫情中有重要作用”,进而到疫情后仍然“推荐使用”远程医疗。公众对远程医疗的积极情绪占比不断提升,消极情绪占比稳步下降。经历新冠疫情,公众普遍认识到远程医疗的便利性,愿意将远程医疗作为问诊的辅助手段,疫情提高了公众对远程医疗的信任度。此外,公众隐私焦虑和医保报销是目前远程医疗面临的主要问题,公众希望政府能出台相关政策法规,支持远程医疗的进一步发展。
Abstract: Purpose/Significance: Telemedicine has been extensively utilized during the pandemic, preventing cross-infections due to public gatherings in hospitals and providing medical services to isolated individuals. However, the evolution of public attitudes towards telemedicine has not been thoroughly investigated. Method/Process: We collected Weibo data from January 1, 2018, to April 30, 2024, using “telemedicine”, “online consultation”, and “Internet hospital” as keywords. Employing a LDA topic model combined with word2vec and a Bi-LSTM-Attention sentiment analysis technique, this study examines the changes in public attitudes towards telemedicine before, during, and after the pandemic. Result/Conclusion: The findings indicate that public attitudes towards telemedicine shifted from mistrust prior to the pandemic to recognition of its importance during the pandemic, and continued to favor its use post-pandemic. The proportion of positive sentiments has risen consistently, while negative sentiments have decreased steadily. The COVID-19 pandemic has led the public to appreciate the convenience of telemedicine, making it a preferred consultation method. The experience of using medicine during the pandemic has enhanced public trust in telemedicine. Moreover, concerns about privacy and medical insurance reimbursement are the primary challenges currently faced by telemedicine. The public calls for government policies and regulations to support its further development.
文章引用:倪载健, 钱颖. 我国公众对远程医疗的态度变化研究——基于多时段微博文本数据分析[J]. 运筹与模糊学, 2024, 14(6): 466-477. https://doi.org/10.12677/orf.2024.146548

参考文献

[1] Combi, C., Pozzani, G. and Pozzi, G. (2016) Telemedicine for Developing Countries. Applied Clinical Informatics, 7, 1025-1050. [Google Scholar] [CrossRef] [PubMed]
[2] Shen, Y., Chen, L., Yue, W. and Xu, H. (2021) Digital Technology-Based Telemedicine for the COVID-19 Pandemic. Frontiers in Medicine, 8, Article 646506. [Google Scholar] [CrossRef] [PubMed]
[3] 蒋帅, 孙东旭,翟运开, 等. 远程医疗在新冠肺炎疫情防控中的实践与探索[J]. 中国数字医学, 2021, 16(3): 109-113.
[4] 李陈晨, 王振博, 黄国书, 等. 我国医疗机构远程医疗运营管理模式现状研究[J]. 中国医院统计, 2023, 30(4): 248-252.
[5] CNNIC. CNNIC发布第53次《中国互联网络发展状况统计报告》 [EB/OL].
https://cnnic.cn/6/86/88/index.html, 2023-03-23.
[6] 金智旸. 互联网医院用户满意度影响因素研究[D]: [博士学位论文]. 武汉: 华中科技大学, 2020.
[7] 翟运开, 路薇, 赵杰, 等. 基于结构方程模型的远程会诊患者满意度研究[J]. 中国卫生政策研究, 2018, 11(9): 64-70.
[8] 张利萍. 远程医疗对患者满意度的影响因素调查[J]. 中国药物与临床, 2020, 20(1): 37-39.
[9] Sun, S., Zhang, J., Zhu, Y., Jiang, M. and Chen, S. (2022) Exploring Users’ Willingness to Disclose Personal Information in Online Healthcare Communities: The Role of Satisfaction. Technological Forecasting and Social Change, 178, Article 121596. [Google Scholar] [CrossRef
[10] 李旭丹, 龚泽鹏, Reinhardt, J.D. 基于收益-风险模型分析高校学生在线健康咨询服务使用意愿的影响因素[J]. 暨南大学学报(自然科学与医学版), 2020, 41(3): 253-259.
[11] Herran, M., Cullere, M., Dezotti Atty, N.R. and Sarmiento, C.S. (2022) Patient’s Opinion on the Maintenance of the Telemedicine Modality in the Post-Pandemic Time. Fertility and Sterility, 118, e326. [Google Scholar] [CrossRef
[12] Jeraq, M.W., Mulder, M.B., Kaplan, D., Lew, J.I. and Farra, J.C. (2022) Telemedicine during COVID-19 Pandemic: Endocrine Surgery Patient Perspective. Journal of Surgical Research, 274, 125-135. [Google Scholar] [CrossRef] [PubMed]
[13] Massaad, E. and Cherfan, P. (2020) Social Media Data Analytics on Telehealth During the COVID-19 Pandemic. Cureus, 12, e7838.
[14] Holtz, B.E. (2021) Patients Perceptions of Telemedicine Visits before and after the Coronavirus Disease 2019 Pandemic. Telemedicine and e-Health, 27, 107-112. [Google Scholar] [CrossRef] [PubMed]
[15] Viteri Malone, M.A., Cabrera Chien, L., Pergolotti, M., Canin, B., Battisti, N.M.L., Krok-Schoen, J.L., et al. (2023) Evolving Oncology Care for Older Adults: Trends in Telemedicine Use after One Year of Caring for Older Adults with Cancer during Covid-19. Journal of Geriatric Oncology, 14, Article 101497. [Google Scholar] [CrossRef] [PubMed]
[16] 娄岩, 杨嘉林, 黄鲁成, 等. 基于网络问答社区的老年科技公众关注热点及情感分析——以“知乎”为例[J]. 情报杂志, 2020, 39(3): 115-122.
[17] 范昊, 庄逸彤. 基于知乎平台内容挖掘的元宇宙公众感知研究[J]. 现代情报, 2024, 44(2): 65-80.
[18] 秦琴, 柯青, 谢雨杉. 全球健康危机下公众的情感和认知——信息搜寻和加工行为视角下的探讨[J]. 现代情报, 2022, 42(4): 62-76.
[19] Jabalameli, S., Xu, Y. and Shetty, S. (2022) Spatial and Sentiment Analysis of Public Opinion toward COVID-19 Pandemic Using Twitter Data: At the Early Stage of Vaccination. International Journal of Disaster Risk Reduction, 80, Article 103204. [Google Scholar] [CrossRef] [PubMed]
[20] Wang, J., Zhou, Y., Zhang, W., Evans, R. and Zhu, C. (2020) Concerns Expressed by Chinese Social Media Users during the COVID-19 Pandemic: Content Analysis of Sina Weibo Microblogging Data. Journal of Medical Internet Research, 22, e22152. [Google Scholar] [CrossRef] [PubMed]
[21] 高芳芳, 林心婕. 围绕在线问诊的舆论焦点与网络情绪研究: 基于微博舆情的分析[J]. 未来传播, 2022, 29(2): 32-40.
[22] Pal, S., Biswas, B., Gupta, R., Kumar, A. and Gupta, S. (2023) Exploring the Factors That Affect User Experience in Mobile-Health Applications: A Text-Mining and Machine-Learning Approach. Journal of Business Research, 156, Article 113484. [Google Scholar] [CrossRef] [PubMed]
[23] Krittanawong, C., Narasimhan, B., Hahn, J., Narasimhan, H., Jneid, H., Virani, S.S., et al. (2022) Individual Sentiments on Telehealth in the COVID-19 Era: Insights from Twitter. Progress in Cardiovascular Diseases, 71, 100-102. [Google Scholar] [CrossRef] [PubMed]
[24] Arenas Gaitán, J. and Ramírez-Correa, P.E. (2023) COVID-19 and Telemedicine: A Netnography Approach. Technological Forecasting and Social Change, 190, Article 122420. [Google Scholar] [CrossRef] [PubMed]
[25] Blei, D.M., Ng, A.Y. and Jordan, M.I. (2003) Latent Dirichlet Allocation. Journal of Machine Learning Research, 3, 993-1022.
[26] 赵凯, 王鸿源. LDA最优主题数选取方法研究: 以CNKI文献为例[J]. 统计与决策, 2020, 36(16): 175-179.
[27] 李廷进. 基于主题模型的文本聚类研究与应用[D]: [硕士学位论文]. 太原: 山西大学, 2020.
[28] Mikolov, T., Sutskever, I., Chen, K., et al. (2013) Distributed Representations of Words and Phrases and Their Compositionality. Proceedings of the 26th International Conference on Neural Information Processing Systems, New York, 3-6 December 2012, 3111-3119.
[29] Ma, J., Wang, L., Zhang, Y., Yuan, W. and Guo, W. (2023) An Integrated Latent Dirichlet Allocation and Word2vec Method for Generating the Topic Evolution of Mental Models from Global to Local. Expert Systems with Applications, 212, Article 118695. [Google Scholar] [CrossRef