The Mobile Chronic Disease Management Service (MCDMS) is an emerging medical service for chronic disease prevention and treatment, but limited attention has been paid to the factors that affect... Show More

The Mobile Chronic Disease Management Service (MCDMS) is an emerging medical service for chronic disease prevention and treatment, but limited attention has been paid to the factors that affect users’ intention to adopt the service. Based on the unified theory of acceptance and use of technology 2 and the protection motivation theory, the authors built an MCDMS adoption model. The authors also verified the differentiating age effect on the service adoption intention from experiential distance perspective of the construal level theory. Empirical results showed that the young group focused more on the impact of effort expectancy, whereas the elderly group focused more on performance expectancy, imitating others, and perceived severity. Furthermore, the young group, however, focused more on the impact of perceived vulnerability, and offline medical habits showed no significant influence on either group’s intention to adopt, which were not consistent with the original hypotheses. The findings can aid MCDMS providers in selecting marketing strategies targeted toward different age groups.

Due to the increasing ageing population, how can caregivers effectively provide long-term care services to meet the older adults’ needs with finite resources is emerging. In addressing this issue... Show More

Due to the increasing ageing population, how can caregivers effectively provide long-term care services to meet the older adults’ needs with finite resources is emerging. In addressing this issue, nursing homes are striving to adopt smart health with the internet of things and artificial intelligence to improve the efficiency and sustainability of healthcare. This study proposed a two-echelon responsive health analytic model (EHAM) to deliver appropriate healthcare services in nursing homes under the Internet of Medical Things environment. A novel care plan revision index is developed using a dual fuzzy logic approach for multidimensional health assessments, followed by care plan modification using case-based reasoning. The findings reveal that EHAM can generate patient-centred long-term care solutions of high quality to maximise the satisfaction of nursing home residents and their families. Ultimately, sustainable healthcare services can be within the communities.

eHealth service has received increasing attention. Patients can consult online doctors via the Internet, and then physically visit the doctors for further diagnosis and treatments. Although extant... Show More

eHealth service has received increasing attention. Patients can consult online doctors via the Internet, and then physically visit the doctors for further diagnosis and treatments. Although extant research has focused on the adoption of eHealth services, the decision-making process from online to offline health services still remains unclear. This study aims to examine patients’ decisions to use online and offline health services by integrating the extended valence framework and the halo effect. By analyzing 221 samples with online consultation experiences, the results show that trust significantly influences perceived benefits and perceived risks, while trust, perceived benefits, and perceived risks significantly influence the intention to consult. The intention to consult positively influences the intention to visit. Considering the moderating effects of payment types, the influence of perceived risks on the intention to consult is larger for the free group than for the paid group. The findings are useful to better understand patients’ decisions to use eHealth.

This article reports on an investigation into how to improve problem formulation and ideation in Design Science Research (DSR) within the mHealth domain. A Systematic Literature Review of problem... Show More

This article reports on an investigation into how to improve problem formulation and ideation in Design Science Research (DSR) within the mHealth domain. A Systematic Literature Review of problem formulation in published mHealth DSR papers found that problem formulation is often only weakly performed, with shortcomings in stakeholder analysis, patient-centricity, clinical input, use of kernel theory, and problem analysis. The study proposes using Coloured Cognitive Mapping for DSR (CCM4DSR) as a tool to improve problem formulation in mHealth DSR. A case study using CCM4DSR found that using CCM4DSR provided a more comprehensive problem formulation and analysis, highlighting aspects that, until CCM4DSR was used, weren’t apparent to the research team and which served as a better basis for mHealth feature ideation.

Medical sensors are implanted within the vital organs of human body to record and monitor the vital signs of pulse rate, heartbeat, electrocardiogram, body mass index, temperature, blood pressure... Show More

Medical sensors are implanted within the vital organs of human body to record and monitor the vital signs of pulse rate, heartbeat, electrocardiogram, body mass index, temperature, blood pressure, etc. to ensure their effective functioning. These are monitored to detect patient’s health from anywhere and at any time. The Wireless Sensor Networks are embedded in the form of Body Area Nets and are capable of sensing and storing the information on a digital device. Later this information could be inspected or even sent to a remotely located storage device specifically (server or any public or private cloud for analysis) so that a medical doctor can diagnose the present medical condition of a person or a patient. Such a facility would be of immense help in the event of an emergency such as a sudden disaster or natural calamity where communication is damaged, and the potential sources become inaccessible. The aim of this paper is to create a mobile platform using Mobile Ad hoc Network to support healthcare connectivity and treatment in emergency situations.

Patients’ emotions toward health IT can play an important role in explaining their usage of it. One form of health IT is self-managing care IT, such as activity trackers that can be used by chronic... Show More

Patients’ emotions toward health IT can play an important role in explaining their usage of it. One form of health IT is self-managing care IT, such as activity trackers that can be used by chronic patients to adopt a healthy lifestyle. The goal of this study is to understand the factors that influence the arousal of emotions in chronic patients while using these tools. Past studies, in general, tend to emphasize how IT shapes emotions, underplaying the role of the individual user’s identity and, specifically, how central health is to the user’s self in shaping emotions. In this research, the authors argue that patients’ health identity centrality (i.e., the extent to which they consider health as central to their sense of self) can play an important role in forming their dependence on health IT by affecting their use of it directly and shaping their emotions around it.

Adoption and user perceptions are dominant on personal health records literature and have led to a better understanding of what individuals' behaviors and perceptions are about the adoption of... Show More

Adoption and user perceptions are dominant on personal health records literature and have led to a better understanding of what individuals' behaviors and perceptions are about the adoption of personal health records. However, these insights are descriptive and are not actionable to allow creating personal health records that will overcome the adoption problems identified by users. This study uses action design research to provide actionable knowledge regarding user perceptions and adoption and their application in the case of the digital allergy card. To achieve this, we conducted interviews with patients and physicians as part of the evaluation of the digital allergy card mock-up and the first prototype. As results, we provided some research proposals regarding the benefits of, levers for, and barriers to adoption of the digital allergy card that can be tested for several other personal health records.

Online medical communities have revolutionized the way patients obtain medical-related information and services. Investigating what factors might influence patients’ satisfaction with doctors and... Show More

Online medical communities have revolutionized the way patients obtain medical-related information and services. Investigating what factors might influence patients’ satisfaction with doctors and predicting their satisfaction can help patients narrow down their choices and increase their loyalty towards online medical communities. Considering the imbalanced feature of dataset collected from Good Doctor, we integrated XGBoost and SMOTE algorithm to examine what factors and these factors can be used to predict patient satisfaction. SMOTE algorithm addresses the imbalanced issue by oversampling imbalanced classification datasets. And XGBoost algorithm is an ensemble of decision trees algorithm where new trees fix errors of existing trees. The experimental results demonstrate that SMOTE and XGBoost algorithm can achieve better performance. We further analyzed the role of features played in satisfaction prediction from two levels: individual feature level and feature combination level.

Healthcare insurance fraud influences not only organizations by overburdening the already fragile healthcare systems, but also individuals in terms of increasing premiums in health insurance and... Show More

Healthcare insurance fraud influences not only organizations by overburdening the already fragile healthcare systems, but also individuals in terms of increasing premiums in health insurance and even fatalities. Identifying the behavioral characteristics of fraudulent claims can help shed light on the development of artificial intelligence and machine learning technologies to detect fraud in health information system research. In this paper, a theoretical model of medical insurance fraud identification is proposed, which characterizes the judgment variables of fraud from the three dimensions of time, quantity, and expenses. The model is verified with large-scale, real-world medical records. Our study shows that, in comparison with claims made by normal people, fraudulent claims usually have a greater frequency of hospital visits, and more medical bills, accompanied by higher amounts of medical expenses. An interesting discovery is that the price per bill for fraudulent cases is not statistically different from the normal cases.

This purpose of this study is to develop a research model by extending the theory of planned behavior in a new application context, and applies it to investigate the extrinsic factors influencing... Show More

This purpose of this study is to develop a research model by extending the theory of planned behavior in a new application context, and applies it to investigate the extrinsic factors influencing people’s attitude towards donating to medical crowdfunding projects appearing on mobile social networking sites (MSNS) and their intention to donate. A survey of 356 Chinese users was conducted and structural equation modeling was used to validate the proposed model and hypotheses. The results indicate that project information, retweeter information and MSNS information all have the significant effect on the general attitude towards donating to medical crowdfunding projects, and general attitude positively affects people’s donation intention. In addition, perceived behavioral control also has positive effect on people’s donation intention, while experienced donating to medical crowdfunding projects has negative effect on people’s donation intention. The research findings provide important theoretical and practical implications.

As M-Health apps become more popular, users can access more mobile health information (MHI) through these platforms. Yet one preeminent question among both researchers and practitioners is how to... Show More

As M-Health apps become more popular, users can access more mobile health information (MHI) through these platforms. Yet one preeminent question among both researchers and practitioners is how to bridge the gap between simply providing MHI and persuading users to buy into the MHI for health self-management. To solve this challenge, this study extends the Elaboration Likelihood Model to explore how to make MHI advice persuasive by identifying the important central and peripheral cues of MHI under individual difference. The proposed research model was validated through a survey. The results confirm that (1) both information matching and platform credibility, as central and peripheral cues, respectively, have significant positive effects on attitudes toward MHI, but only information matching could directly affect health behavior changes; (2) health concern significantly moderates the link between information matching and cognitive attitude and only marginally moderates the link between platform credibility and attitudes. Theoretical and practical implications are also discussed.

To reduce the incidence of cerebrovascular disease and mortality, identifying the risks of cerebrovascular disease in advance and taking certain preventive measures are significant. This article was... Show More

To reduce the incidence of cerebrovascular disease and mortality, identifying the risks of cerebrovascular disease in advance and taking certain preventive measures are significant. This article was aimed to investigate the risk factors of cerebrovascular disease (CVD) in the primary prevention, and to build an early warning model based on the existing technology. The authors use the information entropy algorithm of rough set theory to establish the index system suitable for early warning model. Then, using the limited Boltzmann machine and direction propagation algorithm, the depth trust network is established by building and stacking RBM, and the back propagation is used to fine-tune the parameters of the network at the top layer. Compared with the LM-BP early-warning model, the deep confidence network model is more effective than traditional artificial neural network, which can help to identify the risk of cerebrovascular disease in advance and promote the primary prevention.