With the rapid advancement of Web 2.0 technologies, Internet medicine, and mobile healthcare, the influence of the use of patient-oriented Mobile Internet-based Health Services (MIHS) on patient satisfaction and the electronic word-of-mouth (WOM) of health service agencies is becoming the focus of the academic research community. Many large hospitals, including some Internet hospitals, have provided various online healthcare service platforms that enable patients to expediently consult with physicians and obtain healthcare services in an online to offline format. The purpose of this study is to analyze the main mechanisms of how the features and users’ experiences of MIHS influenced patient satisfaction and continuous use behaviors of the system to generate additional WOM dissemination behaviors. Based on post-adoption behavior and Expectation Confirmation Model of Information Technology Continuance (ECM-IT), this study conducted an empirical study through data collection from users (patients) from a large hospital providing online healthcare services. A total of 494 pieces of data were collected and analyzed using SmartPLS2.0(SmartPLS GmbH, Hamburg, Gernmany). The results show that: (1) patient satisfaction with MIHS and their intentions to continue use of MIHS have significantly positive influences on WOM; (2) patient satisfaction with MIHS is positively influenced by perceived usefulness and confirmation of MIHS performance expectations; (3) and patient intentions to continue use of MIHS are also affected by some technology factors, such as facilitating conditions and perceived risk, as well as some subjective feelings, such as perceived usefulness and perceived interactivity. The results of this study provide important implications for both research and practice of public health.

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.

Purpose: With the popularity of the internet, access to health-related information has become more convenient. However, the easy acquisition of e-health information could lead to unfavorable consequences, such as health anxiety. The purpose of this paper is to explore a set of important influencing factors that lead to health anxiety.Design/methodology/approach: Based on the stimulus–organism–response (S-O-R) framework, we propose a theoretical model of health anxiety, with metacognitive beliefs and catastrophic misinterpretation as the mediators between stimulus factors and health anxiety. Using 218 self-reported data points, the authors empirically examine the research model and hypotheses.Findings: The study results show that anxiety sensitivity positively affects metacognitive beliefs. The severity of physical symptoms has a significant positive impact on catastrophic misinterpretation. Metacognitive beliefs and catastrophic misinterpretation have significant positive impacts on health anxiety.Originality/value: Based on the S-O-R model, this paper develops a comprehensive model to explain health anxiety and verifies the model using firsthand data.

With the rapid progress in mobile healthcare and Internet medicine, the impact of telehealth and telemedicine on the satisfaction of patients and their willingness to travel has become a focus of the academic research community. This study analyses the differences between telehealth and telemedicine and their role in medical tourism. We examine how the information quality and communication quality of telehealth and telemedicine influence patient satisfaction, and their effects on patients’ willingness to undertake medical travel and on their medical travel behaviours. We conducted an empirical study on the use of telehealth and telemedicine and on medical travel behaviour in Azerbaijan using a survey for data collection. A total of 500 results were collected and analysed using SmartPLS 3.0. Results show that (1) the communication quality and information quality of telehealth and telemedicine and their effects on satisfaction have significantly positive influences on willingness to undertake medical travel; (2) the psychological expectations of value and cost (perceived value and perceived cost) have a positive influence on medical travel; and (3) willingness to participate in medical travel positively influences medical travel behaviour. Moreover, results of this study have implications for research on, and the practice of, using telehealth and telemedicine as they relate to medical tourism. This research may help improve knowledge about telehealth and telemedicine and understand the differences between them in detail. This empirical research model may also be useful for researchers from other countries who wish to measure medical travel behaviour.

Background: With the continuous development of the internet and the explosive growth in data, big data technology has emerged. With its ongoing development and application, cloud computing technology provides better data storage and analysis. The development of cloud health care provides a more convenient and effective solution for health. Studying the evolution of knowledge and research hotspots in the field of cloud health care is increasingly important for medical informatics. Scholars in the medical informatics community need to understand the extent of the evolution of and possible trends in cloud health care research to inform their future research. Objective: Drawing on the cloud health care literature, this study aimed to describe the development and evolution of research themes in cloud health care through a knowledge map and common word analysis. Methods: A total of 2878 articles about cloud health care was retrieved from the Web of Science database. We used cybermetrics to analyze and visualize the keywords in these articles. We created a knowledge map to show the evolution of cloud health care research. We used co-word analysis to identify the hotspots and their evolution in cloud health care research. Results: The evolution and development of cloud health care services are described. In 2007-2009 (Phase I), most scholars used cloud computing in the medical field mainly to reduce costs, and grid computing and cloud computing were the primary technologies. In 2010-2012 (Phase II), the security of cloud systems became of interest to scholars. In 2013-2015 (Phase III), medical informatization enabled big data for health services. In 2016-2017 (Phase IV), machine learning and mobile technologies were introduced to the medical field. Conclusions: Cloud health care research has been rapidly developing worldwide, and technologies used in cloud health research are simultaneously diverging and becoming smarter. Cloud–based mobile health, cloud–based smart health, and the security of cloud health data and systems are three possible trends in the future development of the cloud health care field.