[1]黄雯婧,吴桂萍,石荣丽.在线医疗社区的马太效应研究[J].卫生经济研究,2025,42(07):72-75,80.
　HUANG Wenjing,WU Guiping,SHI Rongli.Study on the Matthew Effect of Online Medical Communities[J].Journal Press of Health Economics Research,2025,42(07):72-75,80.
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在线医疗社区的马太效应研究

卫生经济研究[ISSN:1004-7778/CN:33-1056/F]

卷:
42
期数:
2025年07期
页码:
72-75,80
栏目:
数智健康
出版日期:
2025-07-01

文章信息/Info

Title:
Study on the Matthew Effect of Online Medical Communities
作者:
黄雯婧1; 吴桂萍1; 石荣丽1
1.广东药科大学医药商学院/广东省药品监管科学研究基地/国家药品监督管理局药物警戒技术研究与评价重点实验室，广东 广州 510006
Author(s):
HUANG Wenjing;  WU Guiping;  SHI Rongli
Guangdong Medical Products Administration Research Base, Guangzhou Guangdong 510006, China
关键词:
在线医疗社区; 马太效应; 在线医疗资源
Keywords:
online medical communities;  Matthew effect;  online medical resources
分类号:
R197
文献标志码:
A
摘要:
目的：分析在线医疗社区的马太效应及其影响，提出破解之策。方法：以某知名在线医疗社区为研究对象，搜集3 400位儿科医生数据及2016—2023年医生与患者数量变化，采用独立样本秩和检验、时间序列分析及回归模型等统计方法进行分析。结果：在线医疗社区中排名前10%的医生贡献了超过74.51%的服务量，排名后10%的医生仅贡献了0.02%的服务量，医生资源增长滞后于患者需求增长，存在明显的马太效应。结论：为破解在线医疗社区的马太效应，需要采取更加积极的措施来吸引和留住医生，并通过优化平台激励机制、平衡新老医生的曝光度及优化医生算法推荐等方式，改善医生资源分配。
Abstract:
Objective To analyze the Matthew effect of online medical communities and its impact on community development, and to propose strategies to optimize the allocation of physician resources and improve the service quality. Methods Taking a well-known online medical community as the study object, the data of 3 400 pediatricians and the changes in the number of doctors and patients from 2016 to 2023 were collected were collected, and Mann-Whitney U test, time-series analysis and regression model were used for analysis. Results The top 10% of physicians in the online medical community contributed more than 74.51% of the service volume, and the bottom 10% contributed only 0.02% of the service volume. The growth of physicians' resources lagged behind the growth of patients' demand. There was an obvious Matthew effect. Conclusion In order to solve the Matthew effect in online medical communities, it is necessary to take more active measures to attract and retain physicians, and improve the distribution of physician resources by optimizing the incentive mechanism of the platform, balancing the exposure of new and old doctors, and optimizing algorithms for doctor recommendation.

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相似文献/References:

[1]郝 军,戚淼杰,韩优莉.在线医疗社区的发展现状及启示——以北京市为例[J].卫生经济研究,2020,(11):18.
　HAO Jun,QI Miao-jie,HAN You-li.The Status and Enlightenment of Online Medical Community——A Case Study of Beijing[J].Journal Press of Health Economics Research,2020,(07):18.

更新日期/Last Update: 2025-07-01