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在线健康社区中医生口碑对患者选择的影响研究——以好大夫在线为例
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  • 英文篇名:Exploring the Impact of Physicians’ Word of Mouth on Patients’ Selection in Online Health Community——Taking the Website of www.haodf.com as an Example
  • 作者:徐孝婷 ; 杨梦晴 ; 宋小康
  • 英文作者:Xu Xiaoting;Yang Mengqing;Song Xiaokang;School of Information Management,Nanjing University;School of Management & Engineering,Nanjing University;
  • 关键词:在线健康社区 ; 医生口碑 ; 患者选择 ; 线性回归 ; 好大夫在线
  • 英文关键词:online health community;;physicians' word of mouth;;patients' selection;;linear regression;;website of www.haodf.com
  • 中文刊名:XDQB
  • 英文刊名:Journal of Modern Information
  • 机构:南京大学信息管理学院;南京大学工程管理学院;
  • 出版日期:2019-08-01
  • 出版单位:现代情报
  • 年:2019
  • 期:v.39;No.338
  • 基金:国家社会科学基金重大项目“面向大数据的数字图书馆移动视觉搜索机制及应用研究”(项目编号:15ZDB126);; 南京大学研究生科研创新基金资助项目“面向老年群体的在线智慧社区健康服务体系构建研究”(项目编号:2018CW06)
  • 语种:中文;
  • 页:XDQB201908003
  • 页数:10
  • CN:08
  • ISSN:22-1182/G3
  • 分类号:22-30+38
摘要
[目的/意义]在线健康社区中用户参与对医生的评价/打分进而生成医生口碑相关数据信息,从一定程度上反映医生能力水平并给患者选择提供了参考。[方法/过程]以好大夫在线平台为例,抓取8 649位医生的有效面板数据,探索医生口碑与患者选择之间的关系、研究疾病风险的调节作用,建立线性回归模型利用最小二乘法(OLS)对模型拟合。[结果/结论]发现可以用来衡量医生口碑的指标有疗效(β_1=1.933,p<0.1)、态度(β_2=1.870,p<0.1)、感谢信(β_3=0.119,p<0.05)、评论数(β_5=0.253,p<0.05)以及评论情感倾向值(β_6=1.095,p<0.05)是显著的且正向影响患者选择,而礼物指标并不显著(p>0.1);疾病风险的调节作用是高风险疾病时显著(a_3=1.307,p<0.05)且具有正向调节作用。最后从医生和在线健康社区平台发展的角度提出建议。
        [Purpose/Significance]Users participate in the evaluation/scoring of doctors in online health community,which generate the data information related to doctors' reputation can reflect the ability of doctors and also provide a reference for other patients when they choose.[Method/Process]this paper took the website of www.haodf.com as an example,extracted the effective panel data of 8649 doctors to explore the relationship between physician reputation and patient choice and research the regulatory effect of disease disk.A linear regression model was established to fit the model using the least square method(OLS).[Result/Conclusion]This paper found that effect(β_1=1.933,p<0.1),attitude(β_2=1.870,p<0.1),thank-you letter(β_3=0.119,p<0.05),the number of comments(β_5=0.253,p<0.05)and emotional tendency of comments(β_6=1.095,p<0.05)all were significant and positively affected patient selection,but the gift was not significant(p>0.1).The regulatory effect on disease risk was significant in high-risk diseases(a_3=1.307,p<0.05)and had a positive regulatory effect.Finally,some Suggestions were put forward from the perspective of the development of doctors and online health communities.
引文
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