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This study aims to investigate the consumer behavior toward telemedicine services in India during the COVID-19 pandemic onset. With lockdown restrictions and safety concerns in visiting brick-and-mortar clinics or hospitals during the pandemic, Telemedicine had emerged as a potent alternative for seeking redressal to health issues. Based on theory and focus interviews with the telemedicine users, the researchers proposed a model to understand the intent and actual usage of telemedicine in India.

The cross-sectional study undertaken used a questionnaire designed on a seven-point Likert scale and administered to respondents with the objective of identifying the determinants of intent and actual usage of telemedicine services. Simple random sampling was used to collect primary data. The data was cleaned and finally a sample of 405 responses complete in all respects was considered for analysis. The questionnaire comprised of 34 items and following the recommendation of Hair et al. (2016), which says the minimum sample size in structural equation modeling should be ten times the number of indicator variables, a sample size of 405 was deemed adequate.

The research paper finds that performance expectancy, attitude, credibility and self-efficacy positively impact the intention of consumers to use telemedicine services. As the effort expectancy or risk perception toward telemedicine increases the intent and actual usage of telemedicine decreases. The intention to use telemedicine emerged as a strong predictor of the actual usage of telemedicine. Intent to use telemedicine was explained 81.4% by its predictors of performance expectancy, effort expectancy, attitude, risk, credibility and self-efficacy, and actual usage was explained 79.9% by its predictors. This study also reports that telemedicine was found to be popular among chronic as well as episodic patients though the preference was skewed in favor of the episodic patients. One of the advantages of telemedicine is its availability round the clock, and the study found that 8 a. m. to 12 noon time slot as the most preferred slot for seeking telemedicine services.

Chang (2004) opined that telemedicine can fulfill the needs of all stakeholders: citizens, health-care consumers, medical doctors and health-care professionals, policymakers, and so on. Considering the promise telemedicine holds, this realm must be studied and leveraged to the full potential. The study found that patients were using telemedicine even for their day-to-day aliments. This indicates a growing popularity of telemedicine and as such an opportunity for telemedicine companies to leverage it. In India, pharmaceutical companies cannot give commercial advertisements for medicines, and the same can only be sold through a registered medical practitioner’s prescription. As such there is total dependency on the medical practitioner for the sale of medicines. Telemedicine companies offer services of home delivering medicines clubbed with medical consultation thus giving them forward integration in their business models. Using telemedicine the patients had control over the timings of the services offered, and as such the waiting time to get a consultation and subsequent treatment was reduced considerably. Best medical advice from across the globe is available to the patient at less cost. Medical practitioners also stand to benefit as they can treat a variety of cases, collaborate among the medical fraternity and give consultation safely in case of fatal contagious diseases.

This study points to a definite growing popularity of telemedicine services not only in episodic patients but also chronic patients. Telemedicine with its unique advantages holds the promise to grow exponentially in the future and is a compelling health-care segment to focus on for delivering health-care solution to the geographically distant consumers.

The purpose of this paper is to understand the customer sentiment towards telemedicine apps and also to apply machine learning algorithms to analyse the sentiments in the adoption during the COVID-19 pandemic.

Text mining that uses natural language processing to extract insights from unstructured text is used to find out the customer sentiment towards the telemedicine apps during the COVID-19 pandemic. Machine learning algorithms like support vector machine (SVM) and Naïve Bayes classifier are used for classification, and their sensitivity and specificity are found using a confusion matrix.

The paper explores the customer sentiment towards telemedicine apps and their adoption during the COVID-19 pandemic. Text mining that uses natural language processing to extract insights from unstructured text is used to find out the customer sentiment towards the telemedicine apps during the COVID-19 pandemic. Machine learning algorithms like SVM and Naïve Bayes classifier are used for classification, and their sensitivity and specificity are found using a confusion matrix. The customers who used telemedicine apps have positive sentiment as well as negative sentiment towards the telemedicine apps. Some of the customers have concerns about the medicines delivered, their delivery time, the quality of service and other technical difficulties. Even a small percentage of doctors feel uncomfortable in online consultation through the application.

The primary value of this paper lies in providing an overview of the customers’ approach towards the telemedicine apps, especially during the COVID-19 pandemic.

As a little research has been conducted to understand the factors influencing users’ intentions to adopt blockchain-based telemedicine (BBT), it is important to investigate BBT acceptance as incorporation of blockchain technology can solve telemedicine-related issues. Accordingly, this study aims to investigate the factors influencing behavioral intentions (BI) to adopt BBT.

An integrated model comprising the constructs taken from technology–organization–environment framework, technology acceptance model, unified theory of acceptance and use of technology and theory of planned behavior based on their relevance to the context and the objectives of the study has been used for this research. A quantitative approach has been used to test the hypotheses, for which the data was collected from 324 respondents through a self-administered questionnaire. Partial least squares structural equation modeling has been used to test the hypotheses.

The results of the study show that relative advantage, perceived usefulness, trust and perceived ease of use have a significant impact on BI to adopt BBT, whereas regulatory support, subjective norms and facilitating conditions do not have any significant impact on the same.

As the concept of BCT in Pakistan is at its nascent stage and literature regarding this technology’s adoption is also limited, researchers and scholars can apply it to several other fields in Pakistan. For example, this study can be extended to explore the factors influencing blockchain adoption in areas such as education, logistics, transportation, finances and management. This research only considers the direct effects of constructs on BI to adopt BBT and does not consider any mediation and moderations constructs. Future researchers can also study the influence of mediation and moderation constructs on BI to adopt BCT.

Although studies on the acceptance of telemedicine exist, there is a gap concerning the acceptance of BBT, which the current study helps to bridge. From a practical standpoint, the current study makes a highly valuable contribution toward understanding acceptance factors for BBT projects, leading to help policymakers devise policies to promote telemedicine.

This study aims to analyze the effects of performance expectancy, effort expectancy, social influence and facilitating conditions on the behavioral intention of using mobile health applications, especially during and after the COVID-19 pandemic.

A survey was developed using an online survey platform and distributed to Indonesian consumers for three weeks, and 149 usable responses were obtained. The principal component analysis, linear regression and analysis of variance tests were performed to test the validity and reliability of the measurement model and the hypothesized relationships among constructs.

Surprisingly, unlike previous studies on IT adoption, the findings show that social influence has no significant impact on behavioral intention. Facilitating conditions have a very weak to almost no significant impact on behavioral intention to use mobile health applications.

This research is conducted during pandemic COVID-19 where using mobile health apps is a must. In the future this research can be expanded as comparison study after the pandemic COVID-19 stated.

The result implies that digital technologies adoption intention is strongly affected by performance expectancy and effort expectancy, with performance expectancy as the most significant predictor. Nonetheless, the interaction of performance expectancy, effort expectancy, social influence and facilitating conditions influences behavioral intention significantly. Therefore, social influence and facilitating conditions are still important even with very insignificant effects.

To improve consumers’ behavioral intention to use mobile health applications, application providers should promote mobile health applications as useful telemedicine tools by primarily focusing on the application performance and usage experience.

Disasters and pandemics pose challenges to health-care provision. Accordingly, the need for adopting innovative approach is required in providing care to patient. Therefore, the purpose of this study is to present telehealth as an innovative approach for providing care to patients and reducing spread of the infection and advocates for the adoption of telehealth for digitalized treatment of patients.

An integrative review methodology of existing evidence was conducted to provide implications for integration of telehealth for digitalized treatment of patients. This paper draws on Technology Organization Environment (TOE) framework to develop a model and propositions to investigate the factors that influence telehealth adoption from the perspective of the supply side and the demand side of medical services.

Findings from this study discuss applications adopted for telehealth and recommendations on how telehealth can be adopted for medical-care delivery. More importantly, the findings and propositions of this study can act as a roadmap to potential research opportunities within and beyond the pandemic. In addition, findings from this study help provide guidelines on how health practitioners can rapidly integrate telehealth into practice for public health emergencies.

This study identifies the social, technological and organizational factors that influence telehealth adoption, and opportunities of adopting telehealth during the public health emergencies. This study concludes that specific policy changes to improve integration of interoperable solutions; data security; better physical infrastructures; broadband access; better transition and workflow balance; availability of funding and remuneration; regulations and reimbursement; awareness; and training will improve telehealth adoption during public health emergencies.

Doctor–medical institution collaboration (DMIC) services are an emerging service mode in focal online health communities (OHCs). This new service mode is anticipated to affect user satisfaction and doctors' engagement behaviors. However, whether and how DMIC occurs is still ambiguous because the topic is rarely examined. To bridge this gap, this study explores doctors' participation in DMIC services and its effects on their online performance, as well as its effect on patients' evaluation of them on OHC platforms.

The authors propose hypotheses based on structural holes theory. A unique dataset obtained from one of the most popular OHCs in China is used to test the hypotheses, and difference-in-differences estimation is adopted to test the causality of the relationship.

The results demonstrate that providing DMIC services improves doctors' online consultation performance and patients' evaluations of them but has no significant effect on doctors' knowledge-sharing performance on OHC platforms. Doctors' knowledge-sharing performance and consultation performance mediate the relationship between participation in DMIC services and patients' evaluation of doctors. Regarding doctors' participation in DMIC services, its impact on doctors' consultation performance and patients' evaluation of them is weaker for doctors with higher professional titles than for doctors with lower professional titles.

The findings clarify the value creation mechanisms of online collaboration between doctors and medical institutions and thereafter facilitate doctors' participation in DMIC services and enhance the sustainable development of OHCs.

This study specifically seeks to investigate the strategic implementation of machine learning (ML) algorithms and techniques in healthcare institutions to enhance innovation management in healthcare settings.

The papers from 2011 to 2021 were considered following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. First, relevant keywords were identified, and screening was performed. Bibliometric analysis was performed. One hundred twenty-three relevant documents that passed the eligibility criteria were finalized.

Overall, the annual scientific production section results reveal that ML in the healthcare sector is growing significantly. Performing bibliometric analysis has helped find unexplored areas; understand the trend of scientific publication; and categorize topics based on emerging, trending and essential. The paper discovers the influential authors, sources, countries and ML and healthcare management keywords.

The study helps understand various applications of ML in healthcare institutions, such as the use of Internet of Things in healthcare, the prediction of disease, finding the seriousness of a case, natural language processing, speech and language-based classification, etc. This analysis would help future researchers and developers target the healthcare sector areas that are likely to grow in the coming future.

The study highlights the potential for ML to enhance medical support within healthcare institutions. It suggests that regression algorithms are particularly promising for this purpose. Hospital management can leverage time series ML algorithms to estimate the number of incoming patients, thus increasing hospital availability and optimizing resource allocation. ML has been instrumental in the development of these systems. By embracing telemedicine and remote monitoring, healthcare management can facilitate the creation of online patient surveillance and monitoring systems, allowing for early medical intervention and ultimately improving the efficiency and effectiveness of medical services.

By offering a comprehensive panorama of ML's integration within healthcare institutions, this study underscores the pivotal role of innovation management in healthcare. The findings contribute to a holistic understanding of ML's applications in healthcare and emphasize their potential to transform and optimize healthcare delivery.

Digital healthcare manages to grab considerable attention from people and practitioners to avoid severity and provide quick access to healthcare. Entrepreneurs also adopt the digital healthcare segment as an opportunity; nevertheless, their intentions to participate and encourage innovation in this growing sector are unexplored. Drawing upon the social capital theory and health belief model, the study examines the factors that drive entrepreneurship. A novel model is proposed to comprehend entrepreneurial intentions and behavior entrenched in social capital and other encouraging and dissuading perceptive elements with the moderation of trust in digitalization and entrepreneurial efficacy.

The cross-sectional method is used to collect data through a questionnaire from experienced respondents in China. The valid data comprises 280 respondents, analyzed by partial least square structural equation modeling.

Social capital significantly influences monetary attitude, and perceived risk and holds an inconsequential association with perceived usefulness, whereas monetary attitude and perceived usefulness meaningfully explain entrepreneurial activities. Perceived risk has a trivial impact on entrepreneurial intention. Entrepreneurial efficacy and trust in digitalization significantly explain entrepreneurial behavior and moderate the positive relationship between intention and behavior.

The present research proposes a novel research model in the context of entrepreneurship rooted in a digitalized world and offering new correlates. It provides valuable insights by exploring entrepreneurial motivation and deterring factors to get involved in startup activities entrenched in social capital, providing guidelines for policymakers and practitioners to promote entrepreneurship.

This study was carried out to examine the volume and annual growth pattern of research on e-health literacy research, investigate the open-access types of e-health literacy research and perform document production by country and by sources. The study also mapped the keywords used by authors to represent e-health literacy research and performed an analysis of the clusters of the keywords to reveal the thematic focus of research in the area.

The research was guided by a bibliometric approach involving visualization using VosViewer. Data were sourced from Scopus database using a syntax that was tested and verified to be capable of yielding reliable data on the subject matter. The analysis in this study was based on bibliographic data and keywords.

A total number of 1,176 documents were produced during 2006 and 2022. The majority of the documents (18.90%) were published based on hybrid open-access processes, and the USA has the highest contributions. The Journal of Medical Internet Research is the venue for most of the documents on the subject. The 1,176 documents were described by 5,047 keywords, 4.29 keywords per document, and the keywords were classified into five clusters that aptly capture the thematic structure of research in the area.

e-Health literacy has experienced significant growth in research production from 2006 to 2022, with an average of 69 documents per year. Research on e-health literacy initially had low output but began to increase in 2018. The majority of e-health literacy documents are available through open access, with the USA being the leading contributor. The analysis of keywords reveals the multifaceted nature of e-health literacy, including access to information, attitudes, measurement tools, awareness, age factors and communication. Clusters of keywords highlight different aspects of e-health literacy research, such as accessibility, attitudes, awareness, measurement tools and the importance of age, cancer, caregivers and effective communication in healthcare.

This study has practical implications for health promotion. There is also the element of patient empowerment in which case patients are allowed to take an active role in their healthcare. By understanding their health information and having access to resources that help them manage their conditions, patients can make informed decisions about their healthcare. Finally, there is the issue of improved health outcomes which can be achieved by improving patients' e-health literacy. Visualisation of e-health literacy can help bridge the gap between patients and healthcare providers, promote patient-centered care and improve health outcomes.

Research production on e-Health literacy has experienced significant growth from 2006 to 2022, with an average of 69 documents per year. Many e-health literacy documents are available through open access, and the USA is the leading contributor. The analysis of keywords reveals the nature of e-health literacy, including access to information, attitudes, measurement tools, awareness and communication. The clusters of keywords highlight different aspects of e-health literacy research, such as accessibility, attitudes, awareness, measurement tools and the importance of age, cancer, caregivers, and effective communication in healthcare.

The purpose of the study is to comprehend medical service quality, information quality and system quality toward actual use of Tele-Health in rural India. The study further validates the impact of the actual use of Tele-Health on sustainable development, thus providing implications to improve upon the Tele-Health penetration in India.

Data was collected from 326 healthcare practitioners practicing Tele-Health in North Indian states and Structural Equation Modeling was applied to validate the conceptual framework.

The results indicated that medical service quality, information quality and system quality influence Tele-Health behavioral intentions which in turn impact actual use and sustainable development. This research draws upon a conceptual framework to deepen our understanding of Tele-Health by providing an all-inclusive overview.

The massive topography of India with a prime rural populace instills the need for timely healthcare facilities. Tele-Health is a solution to all these problems but is at a nascent stage. Therefore, there is a vital need to study the factors which improve the penetration of Tele-Health in the Indian context. The model that emerged from the study may be validated by other Indian sub-continental countries so that Tele-Health may be implemented hassle-free.