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在线医疗社区中互动工具的有效性:社会交换理论视角。

Effectiveness of Interactive Tools in Online Health Care Communities: Social Exchange Theory Perspective.

机构信息

School of Management and Economics, Beijing Institute of Technology, Beijing, China.

出版信息

J Med Internet Res. 2021 Mar 12;23(3):e21892. doi: 10.2196/21892.

Abstract

BACKGROUND

Although the COVID-19 pandemic will have a negative effect on China's economy in the short term, it also represents a major opportunity for internet-based medical treatment in the medium and long term. Compared with normal times, internet-based medical platforms including the Haodf website were visited by 1.11 billion people, the number of new registered users of all platforms increased by 10, and the number of new users' daily consultations increased by 9 during the pandemic. The continuous participation of physicians is a major factor in the success of the platform, and economic return is an important reason for physicians to provide internet-based services. However, no study has provided the effectiveness of interactive tools in online health care communities to influence physicians' returns.

OBJECTIVE

The effect of internet-based effort on the benefits and effectiveness of interactive effort tools in internet-based health care areas remains unclear. Thus, the goals of this study are to examine the effect of doctors' internet-based service quality on their economic returns during COVID-19 social restrictions, to examine the effect of mutual help groups on doctors' economic returns during COVID-19 social restrictions, and to explore the moderating effect of disease privacy on doctors' efforts and economic returns during COVID-19 social restrictions.

METHODS

On the basis of the social exchange theory, this study establishes an internet-based effort exchange model for doctors. We used a crawler to download information automatically from Haodf website. From March 5 to 7, 2020, which occurred during the COVID-19 pandemic in China, cross-sectional information of 2530 doctors were collected.

RESULTS

Hierarchical linear regression showed that disease privacy (β=.481; P<.001), reputation (β=.584; P<.001), and service quality (β=.560; P<.001) had a significant positive effect on the economic returns of the physicians. The influence of mutual help groups on earnings increases with an increase in the degree of disease privacy (β=.189; P<.001), indicating that mutual help groups have a stronger effect on earnings when patients ask questions about diseases regarding which they desire privacy.

CONCLUSIONS

For platform operators, the results of this study can help the platform understand how to improve doctors' economic returns, especially regarding helping a specific doctor group improve its income to retain good doctors. For physicians on the platform, this study will help doctors spend their limited energy and time on tools that can improve internet-based consultation incomes. Patients who receive internet-based health care services extract information about a doctor based on the doctor's internet-based efforts to understand the doctor's level of professionalism and personality to choose the doctor they like the most. The data used in this study may be biased or not representative of all medical platforms, as they were collected from a single website.

摘要

背景

尽管 COVID-19 大流行将对中国经济产生短期负面影响,但从中长期来看,它也为互联网医疗带来了重大机遇。与正常时期相比,包括好大夫在内的互联网医疗平台访问量达 11.1 亿次,所有平台的新注册用户增加了 10%,疫情期间新用户每日咨询量增加了 9%。医生的持续参与是平台成功的重要因素,而经济回报是医生提供互联网服务的重要原因。然而,目前还没有研究提供在线健康社区中的互动工具对医生回报的有效性。

目的

互联网服务质量对互联网医疗领域中互动工具的收益和有效性的影响尚不清楚。因此,本研究的目的是检验医生在 COVID-19 社会限制期间的互联网服务质量对其经济回报的影响,检验互助组对 COVID-19 社会限制期间医生经济回报的影响,并探讨疾病隐私对 COVID-19 社会限制期间医生努力和经济回报的调节作用。

方法

基于社会交换理论,本研究建立了医生的互联网服务交换模型。我们使用爬虫程序自动从好大夫网站下载信息。2020 年 3 月 5 日至 7 日,在中国 COVID-19 大流行期间,我们收集了 2530 名医生的横截面信息。

结果

分层线性回归显示,疾病隐私(β=.481;P<.001)、声誉(β=.584;P<.001)和服务质量(β=.560;P<.001)对医生的经济回报有显著正向影响。互助组对收益的影响随着疾病隐私程度的增加而增加(β=.189;P<.001),这表明当患者询问他们希望保密的疾病相关问题时,互助组对收益的影响更大。

结论

对于平台运营商来说,本研究的结果可以帮助平台了解如何提高医生的经济回报,特别是帮助特定医生群体提高收入以留住优秀医生。对于平台上的医生来说,本研究将帮助医生将有限的精力和时间投入到可以提高互联网咨询收入的工具上。接受互联网医疗服务的患者根据医生的互联网服务来提取有关医生的信息,以了解医生的专业水平和个性,从而选择他们最喜欢的医生。本研究使用的数据可能存在偏差或不具有代表性,因为它们是从单个网站收集的。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9bad/7998319/b3441fcd56cd/jmir_v23i3e21892_fig1.jpg

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