Online Health Communities Research Articles

Determining factors affecting the user's intention to disclose privacy in online health communities: a dual-calculus model.

As a new type of medical service application for doctor-patient interaction, online health communities (OHCs) have alleviated the imbalance between the supply and demand of medical resources in different regions and the problems of "difficult and expensive access to medical care", but also raised the concern of patients about the risk of disclosure of their health privacy information. In this study, a dual-calculus model was developed to explore users' motivation and decision-making mechanism in disclosing privacy information in OHCs by combining risk calculus and privacy calculus theories. In OHCs, users' trust in physicians and applications is a prerequisite for their willingness to disclose health information. Meanwhile, during the privacy calculation, users' perceived benefits in OHCs had a positive effect on both trust in doctors and trust in applications, while perceived risks had a negative effect on both trusts in doctors and trust in applications. Furthermore, in the risk calculation, the perceived threat assessment in OHCs had a significant positive effect on perceived risk, while the response assessment had a significant negative effect on perceived risk, and the effect of users' trust in physicians far exceeded the effect of trust in applications. Finally, users' trust in physicians/applications is a mediating effect between perceived benefits/risks and privacy disclosure intentions. We combine risk calculus and privacy calculus theories to construct a dual-calculus model, which divides trust into trust in physicians and trust in applications, in order to explore the intrinsic motivation and decision-making mechanism of users' participation in privacy disclosure in OHCs. On the one hand, this theoretically compensates for the fact that privacy computing often underestimates perceived risk, complements the research on trust in OHCs, and reveals the influencing factors and decision transmission mechanisms of user privacy disclosure in OHCs. On the other hand, it also provides guidance for developing reasonable privacy policies and health information protection mechanisms for platform developers of OHCs.

Investigating physician churn in online health communities from a social influence perspective

PurposeThe success of online health communities (OHCs) depends on maintaining long-term relationships with physicians and preventing churn. Even so, the reasons for physician churn are poorly understood. In this study, an empirical model was proposed from a social influence perspective to explore the effects of online social influence and offline social influence on physician churn, as well as the moderating effect of their online returns.Design/methodology/approachThe empirical data of 4,145 physicians from a Chinese OHC, and probit regression models were employed to verify the proposed theoretical model.FindingsThe results suggest that physicians' churn intention is influenced by online and offline social influences, and the offline social influence is more powerful. Physicians' reputational and economic returns could weaken the effect of online social influence on churn intention. However, physicians' economic returns could strengthen the effect of offline social influence on churn intention.Originality/valueThis research study is the first attempt to explore physician churn and divides the social influence into online and offline social influences according to the source of social relationship. The findings contribute to the literature on e-Health, user churn and social influence and provide management implications for OHC managers.

Understanding proactive knowledge sharing and knowledge withholding in physician-driven online health communities – a professional role identity perspective

PurposeWhile spontaneous and voluntary knowledge contribution in online communities promotes value co-creation, dysfunctional knowledge behaviors hamper the effectiveness and development of such communities. The study conceptualizes physicians' proactive knowledge sharing and knowledge withholding behaviors in physician-driven online health communities (OHCs) and integrates the theories of role identity as well as communal and exchange relationships to understand the root causes and motivations behind these two types of knowledge behaviors.Design/methodology/approachThe authors collected survey data from 166 users from one of the largest physician-driven OHCs in China and applied the covariance-based structural equation modeling approach to test the hypotheses.FindingsThe findings suggest that (1) physicians' professional role identity had a positive indirect effect on proactive knowledge sharing behaviors through communal motivation, and work pressure weakened this indirect effect; and (2) professional role identity had a negative indirect impact on knowledge withholding behaviors through exchange motivation.Originality/valueThis study extends proactive knowledge sharing and knowledge withholding behaviors from the organizational management domain to the online environment, exploring the underlying causes and motivations behind both behaviors in the unique context of physician-driven OHCs. The findings offer practical suggestions for the effective management of OHC platforms, as well as policy implications that respond to the workforce shortage of healthcare providers, a crisis that is unfolding globally.

General knowledge-sharing and patient engagement in online health communities: an inverted U-shaped relationship

PurposeEffectively engaging patients is critical for the sustainable development of online health communities (OHCs). Although physicians’ general knowledge-sharing, which is free to the public, represents essential resources of OHCs that have been shown to promote patient engagement, little is known about whether such knowledge-sharing can backfire when superfluous knowledge-sharing is perceived as overwhelming and anxiety-provoking. Thus, this study aims to gain a comprehensive understanding of the role of general knowledge-sharing in OHCs by exploring the spillover effects of the depth and breadth of general knowledge-sharing on patient engagement.Design/methodology/approachThe research model is established based on a knowledge-based view and the literature on knowledge-sharing in OHCs. Then the authors test the research model and associated hypotheses with objective data from a leading OHC.FindingsAlthough counterintuitive, the findings revealed an inverted U-shape relationship between general knowledge-sharing (depth and breadth of knowledge-sharing) and patient engagement that is positively associated with physicians’ number of patients. Specifically, the positive effects of depth and breadth of general knowledge-sharing increase and then decrease as the quantity of general knowledge-sharing grows. In addition, physicians’ offline and online professional status negatively moderated these curvilinear relationships.Originality/valueThis study further enriches the literature on knowledge-sharing and the operations of OHCs from a novel perspective while also offering significant specific implications for OHCs practitioners.

SenRev: Measurement of Personal Information Disclosure in Online Health Communities

With life style shifting during the pandemic, online health communities start to attract more users (including healthcare workers and patients) to discuss health-related questions. While such online platforms provide convenience to users, with health-related information shared broadly over text and images (e.g., X-Ray scans, photocopies of documents), they also raise questions regarding privacy. In this paper, we propose SenRev to systematically measure the leakages of sensitive information in those publicly available discussions. We use SenRev to analyze 1,894,900 multi-modal and multi-lingual data elements from four different online health communities. We find that sensitive data leakages are common; overall 1,324,064 (69.88%) pieces of evidence of data leakages are detected, with 23,587 (1.78%) of them involving identifiers and 1,300,477 (98.22%) involving quasi-identifiers. Surprisingly, leakages through medical images occur more frequently in the community of healthcare professionals compared with the other communities. Finally, based on our results, we discuss the potential directions for countermeasures.

Non-randomised feasibility study testing a primary care intervention to promote engagement in an online health community for adults with troublesome asthma: protocol

IntroductionIn the UK, approximately 4.3 million adults have asthma, with one-third experiencing poor asthma control, affecting their quality of life, and increasing their healthcare use. Interventions promoting emotional/behavioural self-management can...

Forecasting User Interests Through Topic Tag Predictions in Online Health Communities.

The increasing reliance on online communities for healthcare information by patients and caregivers has led to the increase in the spread of misinformation, or subjective, anecdotal and inaccurate or non-specific recommendations, which, if acted on, could cause serious harm to the patients. Hence, there is an urgent need to connect users with accurate and tailored health information in a timely manner to prevent such harm. This article proposes an innovative approach to suggesting reliable information to participants in online communities as they move through different stages in their disease or treatment. We hypothesize that patients with similar histories of disease progression or course of treatment would have similar information needs at comparable stages. Specifically, we pose the problem of predicting topic tags or keywords that describe the future information needs of users based on their profiles, traces of their online interactions within the community (past posts, replies) and the profiles and traces of online interactions of other users with similar profiles and similar traces of past interaction with the target users. The result is a variant of the collaborative information filtering or recommendation system tailored to the needs of users of online health communities. We report results of our experiments on two unique datasets from two different social media platforms which demonstrates the superiority of the proposed approach over the state of the art baselines with respect to accurate and timely prediction of topic tags (and hence information sources of interest).

Determinants of patients' service choice in online health communities: an elaboration likelihood model perspective

PurposeThe purpose of this research paper is to explore the determinants of patients' service choices between telephone consultation and text consultation in online health communities (OHCs).Design/methodology/approachThis study utilized an empirical model based on the elaboration likelihood model and examined the effect of information, regarding service quality (the central route) and service price (the peripheral route), using online health consultation data from one of the largest OHCs in China.FindingsThe logistic regression results indicated that both physician- and patient-generated information can influence the patients' service choices; service price signals will lead patients to cheaper options. However, individual motivations, disease risk and consulting experience change a patients' information processing regarding central and peripheral cues.Originality/valuePrevious researchers have investigated the mechanism of patient behavior in OHCs; however, the researchers have not focused on the patients' choices regarding the multiple health services provided in OHCs. The findings of this study have theoretical and practical implications for future researchers, OHC designers and physicians.

A Study of Maiduguri Youths’ Perception of Online Shopping during the COVID-19 Lockdown

This research examines youths’ perception of online shopping during the COVID-19 lockdown in Maiduguri, Nigeria. This study adopts a descriptive research design to fill the knowledge gap on the current state of e-shopping during the COVID-19 pandemic, a global public health emergency. The objectives of study are to examine the perceived knowledge, behaviours, benefits and risks of online shopping among youths during this period. Their perceptions of online shopping during COVID-19 have implications towards online shopping, COVID-19 compliance and health communication during health emergencies. The researcher administered 200 copies of questionnaires with a 97% return rate. The data collected were analysed using SPSS 15.1 version. Findings from the study revealed that the perceived benefit factors motivated youths to shop online in order to stop the spread of the coronavirus disease. The knowledge of youths in Maiduguri about online shopping also increased due to the government-imposed lockdown. Furthermore, the study also revealed that the internet was the major source of information on online shopping and mobile phones were the major devices used for online shopping in Maiduguri during the period of lockdown. This study recommends that managers of online stores should employ the best approach to ensure the safety and security of e-shoppers’ personal information.

The role of social media content on value co-creation in the food sector: a strategic relationship perspective

PurposeCustomers' participation in the online health community to create value with the brand is growing research interest. In addition, customers are using social media platforms to create value in the food sector. This rises points to the need to study consumers' interactions with online communities and the role of social media content and customer satisfaction in such an environment.Design/methodology/approachThis research collects data using a survey approach. The data were analysed using a partial least square-structural equation modelling (PLS-SEM).FindingsThe findings indicated the impact of social media content and satisfaction on value co-creation in healthy food online communities. The study’s results provide significant new insights into the food sector during the pandemic.Originality/valueThis research enhances the knowledge of satisfaction and value co-creation in the social media context. The findings build on the previous literature on value co-creation, add to the food sector and explain the mediating role of satisfaction between social media content and value co-creation.

Configuration Path Study of Influencing Factors on Health Information-Sharing Behavior among Users of Online Health Communities: Based on SEM and fsQCA Methods.

This study examines the determinants that drive the behavior of sharing health information within online health communities. Leveraging the Theory of Planned Behavior, the Technology Acceptance Model, and the "Knowledge-Attitude-Practice" theory, a comprehensive model elucidating the key elements that sway the health information-sharing behavior among users of online health communities is designed. This model is validated through Structural Equation Modeling (SEM) and Fuzzy Set Qualitative Comparative Analysis (fsQCA). Findings derived from the SEM suggest that perceived ease of use, perceived usefulness, perceived trust, and perceived behavioral control exert a significant positive impact on attitudes towards health information sharing, the intention to share health information, and the actual health information-sharing behavior. The fsQCA unfolds two unique configuration path models that lead to the emergence of health information-sharing behavior: one predicated on perceived trust and sharing intention, and the other on perceived usefulness, behavioral control, and sharing attitude. This research provides invaluable insights, fostering a deeper comprehension of the dynamics involved in health information sharing within online communities, thereby directing the design of more effective health platforms to augment user engagement and enable informed health decisions.

The effects of factors on the motivations for knowledge sharing in online health communities: A benefit-cost perspective.

Online health communities (OHCs) provide knowledge for users, enabling conversations across a broad range of health topics. The development of OHCs depends on users' motivations to share health knowledge. Yet little literature has explored how perceived benefits and costs affect users' motivations for sharing both general and specific knowledge. Based on social exchange theory, we propose a research model that comprises intrinsic benefits (sense of self-worth, satisfaction), extrinsic benefits (social support, reputation, and online attention), cognitive cost, and executional cost to investigate the effects of these factors on users' motivations for general and specific knowledge sharing. We compare the different effects of these factors on users' motivations for knowledge sharing. Results demonstrate positive effects of intrinsic and extrinsic benefits on users' motivations for general and specific knowledge sharing. Differences exist in the negative effects of cognitive and executional costs on users' motivations for general and specific knowledge sharing. This study contributes to promoting the enrichment of online health knowledge and provides implications for the development of OHCs.

HealthE: Recognizing Health Advice & Entities in Online Health Communities

The task of extracting and classifying entities is at the core of important Health-NLP systems such as misinformation detection, medical dialogue modeling, and patient-centric information tools. Granular knowledge of textual entities allows these systems to utilize knowledge bases, retrieve relevant information, and build graphical representations of texts. Unfortunately, most existing works on health entity recognition are trained on clinical notes, which are both lexically and semantically different from public health information found in online health resources or social media. In other words, existing health entity recognizers vastly under-represent the entities relevant to public health data, such as those provided by sites like WebMD. It is crucial that future Health-NLP systems be able to model such information, as people rely on online health advice for personal health management and clinically relevant decision making. In this work, we release a new annotated dataset, HealthE, which facilitates the large-scale analysis of online textual health advice. HealthE consists of 3,400 health advice statements with token-level entity annotations. Additionally, we release 2,256 health statements which are not health advice to facilitate health advice mining. HealthE is the first dataset with an entity-recognition label space designed for the modeling of online health advice. We motivate the need for HealthE by demonstrating the limitations of five widely-used health entity recognizers on HealthE, such as those offered by Google and Amazon. We additionally benchmark three pre-trained language models on our dataset as reference for future research. All data is made publicly available.

Informal Payments and Doctor Engagement in an Online Health Community: An Empirical Investigation Using Generalized Synthetic Control

Recognizing the importance of doctor engagement in online health communities (OHCs), managers and platform owners seek to foster doctor-patient interactions and encourage doctors’ knowledge sharing by introducing informal payments. This study investigates how informal payments in the form of monetary gifts affect doctor engagement, using the launch of a gifting feature by a leading OHC as a natural experiment that exogenously provides doctors with extra monetary incentives. We find that informal payments can have a crowding-out effect on doctors’ intrinsic motivation to engage in medical consultations. We also find that monetary and nonmonetary gifts play distinct roles in motivating doctor responses, with nonmonetary gifts having a more significant carryover effect on follow-up interactions and better promoting the doctor-patient relationship. Our findings additionally suggest that social status moderates the impact of digital gifting on doctor engagement. These findings provide useful implications for online health communities that have implemented or are planning to implement digital gifting to stimulate user engagement.

Informal Payments and Doctor Engagement in an Online Health Community: An Empirical Investigation Using Generalized Synthetic Control

Recognizing the importance of doctor engagement in online health communities (OHCs), managers and platform owners seek to foster doctor-patient interactions and encourage doctors’ knowledge sharing by introducing informal payments. This study investigates how informal payments in the form of monetary gifts affect doctor engagement, using the launch of a gifting feature by a leading OHC as a natural experiment that exogenously provides doctors with extra monetary incentives. We find that informal payments can have a crowding-out effect on doctors’ intrinsic motivation to engage in medical consultations. We also find that monetary and nonmonetary gifts play distinct roles in motivating doctor responses, with nonmonetary gifts having a more significant carryover effect on follow-up interactions and better promoting the doctor-patient relationship. Our findings additionally suggest that social status moderates the impact of digital gifting on doctor engagement. These findings provide useful implications for online health communities that have implemented or are planning to implement digital gifting to stimulate user engagement.

Fear appeals and coping appeals for health product promotion: Impulsive purchasing or psychological distancing?

The prevalence of online health communities (OHCs) motivates health product companies to manipulate persuasion-based marketing strategies, including fear appeals and coping appeals, to promote health products. However, whether such a practice is effective remains unclear. Respondents may either impulsively purchase the recommended health products or simply distance themselves psychologically from the information. Building on protection motivation theory (PMT), this study uncovers the mechanisms behind these two coping responses to persuasive messages, namely, the impulsive purchasing and psychological distancing of OHC users. Correspondingly, we evaluated 366 valid questionnaires, and the results reveal that (1) when encountering fear appeals, perceived health threat can evoke cyberchondria, leading to psychological distancing; and (2) when encountering coping appeals, perceived response efficacy leads to product affection, fostering impulsive purchasing. This study provides new insights into the effects of persuasive messages for health product promotion in social commerce. Marketers should design appropriate persuasive messages to foster impulsive purchasing by generating individuals’ product affection and avoiding cyberchondria.

Named Entity Recognition of Diabetes Online Health Community Data Using Multiple Machine Learning Models.

The rising prevalence of diabetes and the increasing awareness of self-health management have resulted in a surge in diabetes patients seeking health information and emotional support in online health communities. Consequently, there is a vast database of patient consultation information in these online health communities. However, due to the heterogeneity and incompleteness of the content, mining medical information and patient health data from these communities can be a challenge. To address this issue, we built the RoBERTa-BiLSTM-CRF (RBC) model for identifying entities in the online health community of diabetes. We selected 1889 question-answer texts from the most active online health community in China, Good Doctor Online, and used these public data to identify five types of entities. In addition, we conducted a comparative evaluation with three other commonly used models to validate the performance of our proposed model, including RoBERTa-CRF (RC), BilSTM-CRF (BC), and RoBERTa-Softmax (RS). The results showed that the RBC model achieved excellent performance on the test set, with an accuracy of 81.2% and an F1 score of 80.7%, outperforming the performance of traditional entity recognition models in named entity recognition in online medical communities for doctors and diabetes patients. The high performance of entity recognition in online health communities will provide a crucial knowledge source for constructing medical knowledge graphs. This integration would help alleviate the growing demand for medical consultations and the strain on healthcare resources, while assisting healthcare professionals in making informed decisions and providing personalized services to patients.

Achieving popularity to attract more patients via free knowledge sharing in the online health community

PurposeFree knowledge sharing in the online health community has been widely documented. However, whether free knowledge sharing can help physicians accumulate popularity and further the accumulated popularity can help physicians attract patients remain unclear. To unveil these gaps, this study aims to examine how physicians' popularity are affected by their free knowledge sharing, how the relationship between free knowledge sharing and popularity is moderated by professional capital, and how the popularity finally impacts patients' attraction.Design/methodology/approachThe authors collect a panel dataset from Hepatitis B within an online health community platform with 10,888 observations from April 2020 to August 2020. The authors develop a model that integrates free knowledge sharing, popularity, professional capital, and patients' attraction. The hierarchical regression model is used to for examining the impact of free knowledge sharing on physicians' popularity and further investigating the impact of popularity on patients' attraction.FindingsThe authors find that the quantity of articles acted as the heuristic cue and the quality of articles acted as the systematic cue have positive effect on physicians' popularity, and this effect is strengthened by physicians' professional capital. Furthermore, physicians' popularity positively influences their patients' attraction.Originality/valueThis study reveals the aggregation of physicians' popularity and patients' attraction within online health communities and provides practical implications for managers in online health communities.

What drives the adoption of online health communities? An empirical study from patient-centric perspective

BackgroundOnline health communities (OHCs) provide platforms for patients to seek advice from physicians and receive professional suggestions online. It can improve the efficiency of patients’ diagnosis of simple diseases and alleviate hospital congestion. However, few empirical studies have comprehensively explored the factors influencing patients’ intention to use OHCs through objective data. This study aims to fill this gap by identifying key factors that influence patients’ acceptance of OHCs and proposing effective ways to promote the applications of OHCs in China.MethodsBased on the Unified Theory of Acceptance and Usage of Technology (UTAUT), extended with additional constructs identified with patients’ information demands in OHCs, this study developed a research model and proposed nine hypotheses. An online survey involving 783 valid responses was conducted in China to collect data to validate the proposed model. Confirmatory factor analysis and partial least squares (PLS) path model were conducted for instrument validation and hypothesis testing.ResultsPrice value, eHealth literacy, and performance expectancy are the most prominent constructs in the study context. Interestingly, relation quality was also found to have a significant positive relationship with behavioral intention.ConclusionsBased on these findings, OHC operators need to create a user-friendly platform, improve information quality, set reasonable prices, and establish consummate security systems. Physicians and related organizations can raise awareness and assist patients in developing the skills to appropriately comprehend and utilize information in OHCs. This study contributes to both technology adoption theory and practice.

Knowledge transfer between physicians from different geographical regions in China's online health communities.

Online Health Communities (OHCs) are a type of self-organizing platform that provide users with access to social support, information, and knowledge transfer opportunities. The medical expertise of registered physicians in OHCs plays a crucial role in maintaining the quality of online medical services. However, few studies have examined the effectiveness of OHCs in transferring knowledge between physicians and most do not distinguish between the explicit and tacit knowledge transferred between physicians. This study aims to demonstrate the cross-regional transfer characteristics of medical knowledge, especially tacit and explicit knowledge. Based on data collected from 4716 registered physicians on Lilac Garden (DXY.cn), a leading Chinese OHC, Exponential Random Graph Models are used to (1) examine the overall network and two subnets of tacit and explicit knowledge (i.e., clinical skills and medical information), and (2) identify patterns in the knowledge transferred between physicians, based on regional variations. Analysis of the network shows that physicians located in economically developed regions or regions with sufficient workforces are more likely to transfer medical knowledge to those from poorer regions. Analysis of the subnets demonstrate that only Gross Domestic Product (GDP) flows are supported in the clinical skill network since discussions around tacit knowledge are a direct manifestation of physicians' professional abilities. These findings extend current understanding about social value creation in OHCs by examining the medical knowledge flows generated by physicians between regions with different health resources. Moreover, this study demonstrates the cross-regional transfer characteristics of explicit and tacit knowledge to complement the literature on the effectiveness of OHCs to transfer different types of knowledge.