Exploring the social influence of multichannel access in an online health community

Luo, Peng; Chen, Kun; Wu, Chong; Li, Yongli

doi:10.1002/asi.23928

Cited by 40 publications

(27 citation statements)

References 42 publications

(62 reference statements)

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“…We tested the mediation effect of fear by following Luo et al (2018) in adopting the following steps, and the results are shown in Table 5 . We first checked the effects of peer communication and peer condition on rumor sharing behavior in Model 7, and the impacts were positive and significant (PO: β = 0.21, p < 0.001; PC: β = 0.42, p < 0.001).…”

Section: Discussionmentioning

confidence: 99%

Factors affecting individual online rumor sharing behavior in the COVID-19 pandemic

Luo

Wang

Guo

et al. 2021

Computers in Human Behavior

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With the outbreak of COVID-19, online sharing of rumors about the disease is of growing concern worldwide. Drawing on the stimulus–organism–response (S–O-R) framework, this study aims to explore the impacts of peer condition and peer communication on fear of COVID-19, and the impact of fear of COVID-19 on online rumor sharing behavior, by considering the contingency effect of health self-efficacy. Data from 1167 respondents to an online survey in China were adopted to test our research model. The results indicate that peer communication and peer condition induce fear of COVID-19, and fear of COVID-19 results in online rumor sharing. Fear of COVID-19 mediates the effects of peer communication and peer condition on online rumor sharing. Health self-efficacy alleviates the positive effect of peer communication on fear of COVID-19, and the positive effect of fear of COVID-19 on online rumor sharing. This study advances the literature on online rumor sharing and S–O-R, and provides practical implications to social media users and governments.

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Section: Discussionmentioning

confidence: 99%

Factors affecting individual online rumor sharing behavior in the COVID-19 pandemic

Luo

Wang

Guo

et al. 2021

Computers in Human Behavior

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“…As a type of virtual community, online health communities (OHCs) are developed with the Web 2.0 technology [1,2]. OHCs are platforms for people to communicate with one another regarding health-related topics, thereby promoting the interaction between physicians and patients [3].…”

Section: Introductionmentioning

confidence: 99%

Impact of Physician-Patient Communication in Online Health Communities on Patient Compliance: Cross-Sectional Questionnaire Study

Zhang

2019

J Med Internet Res

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Background In China, the utilization of medical resources is tense, and most hospitals are highly congested because of the large population and uneven distribution of medical resources. Online health communities (OHCs) play an important role in alleviating hospital congestions, thereby improving the utilization of medical resources and relieving medical resource shortages. OHCs have positive effects on physician-patient relationships and health outcomes. Moreover, as one of the main ways for patients to seek health-related information in OHCs, physician-patient communication may affect patient compliance in various ways. In consideration of the inevitable development of OHCs, although they have several shortcomings, identifying how physician-patient communication can impact patient compliance is important to improve patients’ health outcomes through OHCs. Objective This study aimed to investigate the impact of physician-patient communication on patient compliance in OHCs through the mediation of the perceived quality of internet health information, decision-making preference, and physician-patient concordance, using an empirical study based on the self-determination theory. Methods A research model was established, including 1 independent variable (physician-patient communication), 3 mediators (perceived quality of internet health information, decision-making preference, and physician-patient concordance), 1 dependent variable (patient compliance), and 4 control variables (age, gender, living area, and education level). Furthermore, a Web-based survey involving 423 valid responses was conducted in China to collect data, and structural equation modeling and partial least squares were adopted to analyze data and test the hypotheses. Results The questionnaire response rate was 79.2% (487/615) and the validity rate was 86.9% (423/487); reliability and validity are acceptable. The communication between physicians and patients in OHCs positively affects patient compliance through the mediation of the perceived quality of internet health information, decision-making preference, and physician-patient concordance. Moreover, physician-patient communication exhibits similar impacts on the perceived quality of internet health information, decision-making preference, and physician-patient concordance. Patients’ decision-making preference shows the weakest impact on patient compliance compared with the other 2 mediators. Ultimately, all 3 mediators play a partially mediating role between physician-patient communication and patient compliance. Conclusions We conclude that physician-patient communication in OHCs exhibits a positive impact on patient compliance; thus, patient compliance can be improved by guiding physician-patient communication in OHCs. Furthermore, our findings suggest that physicians can share high-quality health information with patients, discuss benefits, risks, and costs of tre...

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“…Although many features may impact payment, we are particularly interested in those, which are publicly visible on the platform, such as characteristics of physicians and their interaction with patients and patient feedback, rather than nonvisible features, such as patients' economic status and their general attitude toward technology. This is because publicly visible features contain information and signals that, through observational learning and social influence [20][21][22][23], may influence patient payment.…”

Section: Gaps and Objectivesmentioning

confidence: 99%

Analysis of Massive Online Medical Consultation Service Data to Understand Physicians’ Economic Return: Data Mining Study

2020

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Background Online health care consultation has become increasingly popular and is considered a potential solution to health care resource shortages and inefficient resource distribution. However, many online medical consultation platforms are struggling to attract and retain patients who are willing to pay, and health care providers on the platform have the additional challenge of standing out in a crowd of physicians who can provide comparable services. Objective This study used machine learning (ML) approaches to mine massive service data to (1) identify the important features that are associated with patient payment, as opposed to free trial–only appointments; (2) explore the relative importance of these features; and (3) understand how these features interact, linearly or nonlinearly, in relation to payment. Methods The dataset is from the largest China-based online medical consultation platform, which covers 1,582,564 consultation records between patient-physician pairs from 2009 to 2018. ML techniques (ie, hyperparameter tuning, model training, and validation) were applied with four classifiers—logistic regression, decision tree (DT), random forest, and gradient boost—to identify the most important features and their relative importance for predicting paid vs free-only appointments. Results After applying the ML feature selection procedures, we identified 11 key features on the platform, which are potentially useful to predict payment. For the binary ML classification task (paid vs free services), the 11 features as a whole system achieved very good prediction performance across all four classifiers. DT analysis further identified five distinct subgroups of patients delineated by five top-ranked features: previous offline connection, total dialog, physician response rate, patient privacy concern, and social return. These subgroups interact with the physician differently, resulting in different payment outcomes. Conclusions The results show that, compared with features related to physician reputation, service-related features, such as service delivery quality (eg, consultation dialog intensity and physician response rate), patient source (eg, online vs offline returning patients), and patient involvement (eg, provide social returns and reveal previous treatment), appear to contribute more to the patient’s payment decision. Promoting multiple timely responses in patient-provider interactions is essential to encourage payment.

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Exploring the social influence of multichannel access in an online health community

Cited by 40 publications

References 42 publications

Factors affecting individual online rumor sharing behavior in the COVID-19 pandemic

Factors affecting individual online rumor sharing behavior in the COVID-19 pandemic

Impact of Physician-Patient Communication in Online Health Communities on Patient Compliance: Cross-Sectional Questionnaire Study

Analysis of Massive Online Medical Consultation Service Data to Understand Physicians’ Economic Return: Data Mining Study

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