How users adopt healthcare information: An empirical study of an online Q&amp;A community

Jin, Jiahua; Yan, Xiangbin; Li, Yijun; Li, Yumei

doi:10.1016/j.ijmedinf.2015.11.002

Cited by 105 publications

(114 citation statements)

References 40 publications

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“…Health professional moderators enable OHC users to actively confront their health issues, develop appropriate competences, and gain health-related knowledge and greater control over their own health situations or health situations of those they care for (Mo & Coulson, 2014;van Uden-Kraan et al, 2008). Indeed, Barak, Boniel-Nissim, and Suler (2008) use of health care services, and even public health care costs (Jin, Yan, Li, & Li, 2016;Nutbeam, 2008).…”

Section: Discussionmentioning

confidence: 99%

Exploring the benefits and challenges of health professionals’ participation in online health communities: Emergence of (dis)empowerment processes and outcomes

Atanasova

Kamin

Petrič

2017

International Journal of Medical Informatics

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This small study demonstrates that health professional participating in OHCs as moderators perceive themselves as facilitators of patients and other OHC's users empowering processes and outcomes, in which OHC's users improve their health literacy, develop skills, expand their social support, and gain other important resources necessary when dealing with health-related issues. Health professional moderator's role, however, also involves several duties, responsibilities and limitations that are often experienced as difficulties in providing patients and other users with adequate counselling and online medical service. OHCs also represent an important terrain for personal and professional empowerment of health professional moderators, although the presence of disempowering processes also needs to be noted.

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Section: Discussionmentioning

confidence: 99%

Exploring the benefits and challenges of health professionals’ participation in online health communities: Emergence of (dis)empowerment processes and outcomes

Atanasova

Kamin

Petrič

2017

International Journal of Medical Informatics

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“…Health care social media is not only used to exchange health information [18-20], but also provides a platform for emotional communication [21-23]. Some emotional expressions, such as venting, sending positive energy, and showing compassion or empathy, can help patients with serious diseases to cope.…”

Section: Introductionmentioning

confidence: 99%

Understanding Health Care Social Media Use From Different Stakeholder Perspectives: A Content Analysis of an Online Health Community

et al. 2017

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BackgroundHealth care social media used for health information exchange and emotional communication involves different types of users, including patients, caregivers, and health professionals. However, it is difficult to identify different stakeholders because user identification data are lacking due to privacy protection and proprietary interests. Therefore, identifying the concerns of different stakeholders and how they use health care social media when confronted with huge amounts of health-related messages posted by users is a critical problem.ObjectiveWe aimed to develop a new content analysis method using text mining techniques applied in health care social media to (1) identify different health care stakeholders, (2) determine hot topics of concern, and (3) measure sentiment expression by different stakeholders.MethodsWe collected 138,161 messages posted by 39,606 members in lung cancer, diabetes, and breast cancer forums in the online community MedHelp.org over 10 years (January 2007 to October 2016) as experimental data. We used text mining techniques to process text data to identify different stakeholders and determine health-related hot topics, and then analyzed sentiment expression.ResultsWe identified 3 significantly different stakeholder groups using expectation maximization clustering (3 performance metrics: Rand=0.802, Jaccard=0.393, Fowlkes-Mallows=0.537; P<.001), in which patients (24,429/39,606, 61.68%) and caregivers (12,232/39,606, 30.88%) represented the majority of the population, in contrast to specialists (2945/39,606, 7.43%). We identified 5 significantly different health-related topics: symptom, examination, drug, procedure, and complication (Rand=0.783, Jaccard=0.369, Fowlkes-Mallows=0.495; P<.001). Patients were concerned most about symptom topics related to lung cancer (536/1657, 32.34%), drug topics related to diabetes (1883/5904, 31.89%), and examination topics related to breast cancer (8728/23,934, 36.47%). By comparison, caregivers were more concerned about drug topics related to lung cancer (300/2721, 11.03% vs 109/1657, 6.58%), procedure topics related to breast cancer (3952/13,954, 28.32% vs 5822/23,934, 24.33%), and complication topics (4449/25,701, 17.31% vs 4070/31,495, 12.92%). In addition, patients (9040/36,081, 25.05%) were more likely than caregivers (2659/18,470, 14.39%) and specialists (17,943/83,610, 21.46%) to express their emotions. However, patients’ sentiment intensity score (2.46) was lower than those of caregivers (4.66) and specialists (5.14). In particular, for caregivers, negative sentiment scores were higher than positive scores (2.56 vs 2.18), with the opposite among specialists (2.62 vs 2.46). Overall, the proportion of negative messages was greater than that of positive messages related to symptom, complication, and examination. The pattern was opposite for drug and procedure topics. A trend analysis showed that patients and caregivers gradually changed their emotional state in a positive direction.ConclusionsThe hot topics of interest and sentiment expr...

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“…There are many studies on knowledge adoption of usergenerated content from Q&A OHCs: using collective intelligence for problem-solving (Wang, Liu, & Fan, 2011), predicting information-seeker satisfaction on a collaborative Q&A community (Liu, Bian, & Agichtein, 2008), evaluating information adoption from online consumer reviews (Cheung, Lee, & Rabjohn, 2008), examining factors affecting information adoption from Baidu Knows (Jin et al, 2016) and Wikipedia (Shen et al, 2013), choosing to adopt an authoritative expert for consultation (Cao, Liu, Zhu, Hu, & Chen, 2017), and so on. Factors that influence adoption in these studies are mainly related to either comment/reply content (readability, relevance, etc.)…”

Section: Knowledge Adoption In Qanda Communitymentioning

confidence: 99%

“…Research on OHCs has focused on community theme analysis, sentiment analysis; physician's service charges; value creation from urban to rural areas; and so on (Cohan, Young, Yates, & Goharian, 2017;Goh, Gao, & Agarwal, 2016;Wu & Lu, 2018). Knowledge adoption in Q&A communities in general has attracted widespread academic attention, such as the information adoption of Wikipedia adoption (Shen, Cheung, & Lee, 2013), Baidu Knows (Jin, Yan, Li, & Li, 2016), et al However, there is little or no research on how users adopt satisfactory health information from physician's online replies. Based on the classical elaboration likelihood model (ELM) (Petty & Cacioppo, 1986), the knowledge adoption model (KAM) (Sussman & Siegal, 2003), and the health information context, we propose novel influential features: physician's online experience and offline expertise, as well as information on the hospital where physicians work.…”

Section: Introductionmentioning

confidence: 99%

User adoption of physician's replies in an online health community: An empirical study

Zhang

Fan

2019

Asso for Info Science & Tech

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Online health question‐and‐answer consultation with physicians is becoming a common phenomenon. However, it is unclear how users identify the most satisfying reply. Based on the dual‐process theory of knowledge adoption, we developed a conceptual model and empirical method to study which factors influence adoption of a reply. We extracted 6 variables for argument quality (Ease of understanding, Relevance, Completeness, Objectivity, Timeliness, Structure) and 4 for source credibility (Physician's online experience, Physician's offline expertise, Hospital location, Hospital level). The empirical results indicate that both central and peripheral routes affect user's adoption of a response. Physician's offline expertise negatively affects user's adoption decision, while physician's online experience positively affects it; this effect is positively moderated by user involvement.

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How users adopt healthcare information: An empirical study of an online Q&A community

Cited by 105 publications

References 40 publications

Exploring the benefits and challenges of health professionals’ participation in online health communities: Emergence of (dis)empowerment processes and outcomes

Exploring the benefits and challenges of health professionals’ participation in online health communities: Emergence of (dis)empowerment processes and outcomes

Understanding Health Care Social Media Use From Different Stakeholder Perspectives: A Content Analysis of an Online Health Community

User adoption of physician's replies in an online health community: An empirical study

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