Impacts of case-based health knowledge system in hospital management: The mediating role of group effectiveness

Gu, Dongxiao; Deng, Shuyuan; Zheng, Qinqin; Liang, Changyong; Wu, Jiao

doi:10.1016/j.im.2019.04.005

Cited by 45 publications

(33 citation statements)

References 82 publications

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“…OHC studies also found that feedback, rewards, and reputation systems of the community can encourage user participation and contribution [7]. Providing technical support for effectively storing, organizing, and modifying knowledge can lead to higher perceived usefulness and positive attitude to participate [48]. Successful community system design and policies guide users' interactions and facilitate the sense of togetherness [5].…”

Section: Perceived Community Support and User Interactionsmentioning

confidence: 99%

Perceived Community Support, Users’ Interactions, and Value Co-Creation in Online Health Community: The Moderating Effect of Social Exclusion

Liu

Fan

et al. 2019

IJERPH

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Online health communities (OHCs) face the same problem as other social media platforms in terms of decreasing activity and user attrition. Drawing upon organizational support theory, this study explores how perceived community support affects user interactions and value co-creation which in turn influence their continuous participation. OHCs act as both health knowledge-sharing platforms and important social media for patients, and thus, interpersonal interactions in OHCs are categorized into health-related and general topic interactions. Considering the identity of patients, this study also examines the moderating effect of user-perceived social exclusion on the relationship between community support and user interaction. A total of 292 valid samples from a diabetic patient community in China were used to examine the proposed hypotheses through structural equation modeling. The results show that: (1) Community support has a positive effect on health topic and general topic interactions; (2) both types of interactions have significant positive effects on users’ perceived functional and social values, while general topic interaction is also related positively to users’ perceived affective value; (3) perceived functional value can result directly in continuous participation, while perceived social value contributes indirectly to continuous participation intention through perceived affective value; and (4) users perceived higher social exclusion are more influenced by community support to participate in health topic interactions than those who perceived lower social exclusion, while no significant difference in general topic interactions between two groups. The results of this study can provide implications for both researchers and practitioners.

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Section: Perceived Community Support and User Interactionsmentioning

confidence: 99%

Perceived Community Support, Users’ Interactions, and Value Co-Creation in Online Health Community: The Moderating Effect of Social Exclusion

Liu

Fan

et al. 2019

IJERPH

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“…Smart homes can be a “family health consultant”. For example, the smart home system can realize the “alarm” of the elderly and children, notify the family and locate; The system will automatically start and shut down the air purifier according to the real-time air condition, without manual operation; In addition, the air purifier can be controlled based on the humidity level in the house and the PSI level of asthma and allergic rhinitis patients [ 45 ]. The smart screen in the kitchen can see the children in the living room through the security system, and is equipped with detection equipment for harmful gases such as gas to achieve the function of safety protection; The smart clothing care machine in the cloakroom has the functions of steam sterilization, shaping, drying and other functions to protect the health of users.…”

Section: Discussionmentioning

confidence: 99%

Exploring emerging IoT technologies in smart health research: a knowledge graph analysis

Yang

Wang

et al. 2020

BMC Med Inform Decis Mak

Self Cite

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Background At present, Internet of Things technology has been widely used in various fields, and smart health is also one of its important application areas. Methods We use the core collection of Web of Science as a data source, using tools such as CiteSpace and bibliometric methods to visually analyze 9561 articles published in the field of smart health research based on the Internet of things (IoT) in 2003–2019, including time distribution, spatial distribution, and literature co-citation analysis and keyword analysis. Results The field of smart health research based on IoT has developed rapidly since 2014, but has not yet formed a stable network of authors and institutions. In addition, the knowledge base in this field has been initially formed, and most of the published literatures are multi-theme research. Conclusions This study discusses the research status, research hotspots and future development trends in this field, and provides important knowledge support for subsequent research.

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“…The method of determination directly affects the final evaluation result [28]. It is difficult in eliciting appropriate attribute weights from human experts [29]. In extant literature, the most prevailing methods to calculate attributes' weights are: Delphi method [30], Analytic Hierarchy Process [31], Network Analysis [32], Entropy Method [33], Simos Procedure [34], Single Objective Optimization Model [35][36][37], Multi-Objective Optimization Model [38].…”

Section: Related Literaturementioning

confidence: 99%

Uncertain Multiplicative Language Decision Method Based on Group Compromise Framework for Evaluation of Mobile Medical APPs in China

Zhang

Ding

2020

IJERPH

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The mobile medical application (M-medical APP) can optimize medical service process and reduce health management costs for users, which has become an important complementary form of traditional medical services. To assist users including patients choose the ideal M-medical APP, we proposed a novel multiple attribute group decision making algorithm based on group compromise framework, which need not determine the weight of decision-maker. The algorithm utilized an uncertain multiplicative linguistic variable to measure the individual original preference to express the real evaluation information as much as possible. The attribute weight was calculated by maximizing the differences among alternatives. It determined the individual alternatives ranking according to the net flow of each alternative. By solved the 0–1 optimal model with the objective of minimizing the differences between individual ranking, the ultimate group compromise ranking was obtained. Then we took 10 well-known M-medical APPs in Chinese as an example, we summarized service categories provided for users and constructed the assessment system consisting of 8 indexes considering the service quality users are concerned with. Finally, the effectiveness and superiority of the proposed method and the consistency of ranking results were verified, through comparing the group ranking results of 3 similar algorithms. The experiments show that group compromise ranking is sensitive to attribute weight.

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Impacts of case-based health knowledge system in hospital management: The mediating role of group effectiveness

Cited by 45 publications

References 82 publications

Perceived Community Support, Users’ Interactions, and Value Co-Creation in Online Health Community: The Moderating Effect of Social Exclusion

Perceived Community Support, Users’ Interactions, and Value Co-Creation in Online Health Community: The Moderating Effect of Social Exclusion

Exploring emerging IoT technologies in smart health research: a knowledge graph analysis

Uncertain Multiplicative Language Decision Method Based on Group Compromise Framework for Evaluation of Mobile Medical APPs in China

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