Digital healthcare: Historical development, applications, and future research directions

Twenty-Five Years of Progress-Lessons Learned From JMIR Publications to Address Gender Parity in Digital Health Authorships: Bibliometric Analysis

BACKGROUND

Digital health research plays a vital role in advancing equitable health care. The diversity of research teams is thereby instrumental in capturing societal challenges, increasing productivity, and reducing bias in algorithms. Despite its importance, the gender distribution within digital health authorship remains largely unexplored.

OBJECTIVE

This study aimed to investigate the gender distribution among first and last authors in digital health research, thereby identifying predicting factors of female authorship.

METHODS

This bibliometric analysis examined the gender distribution across 59,980 publications from 1999 to 2023, spanning 42 digital health journals indexed in the Web of Science. To identify strategies ensuring equality in research, a detailed comparison of gender representation in JMIR journals was conducted within the field, as well as against a matched sample. Two-tailed Welch 2-sample t tests, Wilcoxon rank sum tests, and chi-square tests were used to assess differences. In addition, odds ratios were calculated to identify predictors of female authorship.

RESULTS

The analysis revealed that 37% of first authors and 30% of last authors in digital health were female. JMIR journals demonstrated a higher representation, with 49% of first authors and 38% of last authors being female, yielding odds ratios of 1.96 (95% CI 1.90-2.03; P<.001) and 1.78 (95% CI 1.71-1.84; P<.001), respectively. Since 2008, JMIR journals have consistently featured a greater proportion of female first authors than male counterparts. Other factors that predicted female authorship included having female authors in other relevant positions and gender discordance, given the higher rate of male last authors in the field.

CONCLUSIONS

There was an evident shift toward gender parity across publications in digital health, particularly from the publisher JMIR Publications. The specialized focus of its sister journals, equitable editorial policies, and transparency in the review process might contribute to these achievements. Further research is imperative to establish causality, enabling the replication of these successful strategies across other scientific fields to bridge the gender gap in digital health effectively.

Barriers and Implications of 5G Technology Adoption for Hospitals in Western China: Integrated Interpretive Structural Modeling and Decision-Making Trial and Evaluation Laboratory Analysis

BACKGROUND

5G technology is gaining traction in Chinese hospitals for its potential to enhance patient care and internal management. However, various barriers hinder its implementation in clinical settings, and studies on their relevance and importance are scarce.

OBJECTIVE

This study aimed to identify critical barriers hampering the effective implementation of 5G in hospitals in Western China, to identify interaction relationships and priorities of the above-identified barriers, and to assess the intensity of the relationships and cause-and-effect relations between the adoption barriers.

METHODS

This paper uses the Delphi expert consultation method to determine key barriers to 5G adoption in Western China hospitals, the interpretive structural modeling to uncover interaction relationships and priorities, and the decision-making trial and evaluation laboratory method to reveal cause-and-effect relationships and their intensity levels.

RESULTS

In total, 14 barriers were determined by literature review and the Delphi method. Among these, "lack of policies on ethics, rights, and responsibilities in core health care scenarios" emerged as the fundamental influencing factor in the entire system, as it was the only factor at the bottom level of the interpretive structural model. Overall, 8 barriers were classified as the "cause group," and 6 as the "effect group" by the decision-making trial and evaluation laboratory method. "High expense" and "organizational barriers within hospitals" were determined as the most significant driving barrier (the highest R-C value of 1.361) and the most critical barrier (the highest R+C value of 4.317), respectively.

CONCLUSIONS

Promoting the integration of 5G in hospitals in Western China faces multiple complex and interrelated barriers. The study provides valuable quantitative evidence and a comprehensive approach for regulatory authorities, hospitals, and telecom operators, helping them develop strategic pathways for promoting widespread 5G adoption in health care. It is suggested that the stakeholders cooperate to explore and solve the problems in the 5G medical care era, aiming to achieve the coverage of 5G medical care across the country. To our best knowledge, this study is the first academic exploration systematically analyzing factors resisting 5G integration in Chinese hospitals, and it may give subsequent researchers a solid foundation for further studying the application and development of 5G in health care.

A digital health ecosystem ontology from the perspective of Australian consumers: a mixed-method literature analysis

This study presents an ontology that scopes the digital health ecosystem from a consumer-centered perspective. We used a mixed-method analysis on a set of papers collected for a comprehensive review to identify common themes, components, and patterns that repeatedly emerge within Australian-based digital health studies. Three major and four child themes were identified as the foundational aspects of the proposed ontology. The child themes have more precise concept definitions, inherited and distinguishing attributes. Out of 179 recognized concepts, 33 were related to the Healthcare theme; 23 concepts formed a cluster of employed devices under the Technology theme; 40 concepts were associated with Use and Usability factors. 60 other concepts formed the cluster of the consumer-user theme. The theme of Digital Health was seen as being connected to 2 independent clusters. The main cluster embodied 21 extracted concepts, semantically related to "data, information, and knowledge," whilst the second cluster embodied concepts related to "healthcare." Different stakeholders can utilize this ontology to define their landscape of digitally enabled healthcare. The novelty of this work resides in capturing a consumer-centered perspective and the method we used in deriving the ontology - formalizing the results of a systematic review based on data-driven analysis methods.

The measurement, sources of variation, and factors influencing the coupled and coordinated development of rural revitalization and digital economy in China

An evaluation index system for the coupled and coordinated development of China's digital economy and rural revitalization, including a total of 46 indicators for the digital economy and rural revitalization subsystems, was constructed and combined with the entropy weight method, the coupling coordination degree model, Zhou's constraint identification index, the Dagum Gini coefficient decomposition method, and the panel spatial econometric model to analyze the level of coupled and coordinated development of China's digital economy and rural revitalization. The results found that: (1) the coupling and coordination between the two have gradually improved. The constraints of the digital economy on rural revitalization were gradually alleviated from 2011 to 2015, but after the 19th Party Congress, the development trend of rural revitalization has significantly outstripped the digital economy. (2) the spatial differences in the degree of coupling and coordination between the two are dominated by inter-regional differences and show significant spatial convergence and spatial correlation. Differentiated digital economy development strategies and more radiation in polarized areas are important for reducing regional differences in the level of coupling and coordination between the digital economy and rural revitalization. This will help China's digital countryside grow more efficiently.

Integrating Digital Technologies and Public Health to Fight Covid-19 Pandemic: Key Technologies, Applications, Challenges and Outlook of Digital Healthcare

Integration of digital technologies and public health (or digital healthcare) helps us to fight the Coronavirus Disease 2019 (COVID-19) pandemic, which is the biggest public health crisis humanity has faced since the 1918 Influenza Pandemic. In order to better understand the digital healthcare, this work conducted a systematic and comprehensive review of digital healthcare, with the purpose of helping us combat the COVID-19 pandemic. This paper covers the background information and research overview of digital healthcare, summarizes its applications and challenges in the COVID-19 pandemic, and finally puts forward the prospects of digital healthcare. First, main concepts, key development processes, and common application scenarios of integrating digital technologies and digital healthcare were offered in the part of background information. Second, the bibliometric techniques were used to analyze the research output, geographic distribution, discipline distribution, collaboration network, and hot topics of digital healthcare before and after COVID-19 pandemic. We found that the COVID-19 pandemic has greatly accelerated research on the integration of digital technologies and healthcare. Third, application cases of China, EU and U.S using digital technologies to fight the COVID-19 pandemic were collected and analyzed. Among these digital technologies, big data, artificial intelligence, cloud computing, 5G are most effective weapons to combat the COVID-19 pandemic. Applications cases show that these technologies play an irreplaceable role in controlling the spread of the COVID-19. By comparing the application cases in these three regions, we contend that the key to China's success in avoiding the second wave of COVID-19 pandemic is to integrate digital technologies and public health on a large scale without hesitation. Fourth, the application challenges of digital technologies in the public health field are summarized. These challenges mainly come from four aspects: data delays, data fragmentation, privacy security, and data security vulnerabilities. Finally, this study provides the future application prospects of digital healthcare. In addition, we also provide policy recommendations for other countries that use digital technology to combat COVID-19.

Responsible Artificial Intelligence as a Secret Ingredient for Digital Health: Bibliometric Analysis, Insights, and Research Directions

With the unparallel advance of leading-edge technologies like artificial intelligence (AI), the healthcare systems are transforming and shifting for more digital health. In recent years, scientific productions have reached unprecedented levels. However, a holistic view of how AI is being used for digital health remains scarce. Besides, there is a considerable lack of studies on responsible AI and ethical issues that identify and suggest practitioners' essential insights towards the digital health domain. Therefore, we aim to rely on a bibliometric approach to explore the dynamics of the interplay between AI and digital health approaches, considering the responsible AI and ethical aspects of scientific production over the years. We found four distinct periods in the publication dynamics and the most popular approaches of AI in the healthcare field. Also, we highlighted the main trends and insightful directions for scholars and practitioners. In terms of contributions, this work provides a framework integrating AI technologies approaches and applications while discussing several barriers and benefits of AI-based health. In addition, five insightful propositions emerged as a result of the main findings. Thus, this study's originality is regarding the new framework and the propositions considering responsible AI and ethical issues on digital health.

A systematic review of emerging information technologies for sustainable data-centric health-care

BACKGROUND

Of the Sustainable Development Goals (SDGs), the third presents the opportunity for a predictive universal digital healthcare ecosystem, capable of informing early warning, assisting in risk reduction and guiding management of national and global health risks. However, in reality, the existing technology infrastructure of digital healthcare systems is insufficient, failing to satisfy current and future data needs.

OBJECTIVE

This paper systematically reviews emerging information technologies for data modelling and analytics that have potential to achieve Data-Centric Health-Care (DCHC) for the envisioned objective of sustainable healthcare. The goal of this review is to: 1) identify emerging information technologies with potential for data modelling and analytics, and 2) explore recent research of these technologies in DCHC.

FINDINGS

A total of 1619 relevant papers have been identified and analysed in this review. Of these, 69 were probed deeply. Our analysis found that the extant research focused on elder care, rehabilitation, chronic diseases, and healthcare service delivery. Use-cases of the emerging information technologies included providing assistance, monitoring, self-care and self-management, diagnosis, risk prediction, well-being awareness, personalized healthcare, and qualitative and/or quantitative service enhancement. Limitations identified in the studies included vendor hardware specificity, issues with user interface and usability, inadequate features, interoperability, scalability, and compatibility, unjustifiable costs and insufficient evaluation in terms of validation.

CONCLUSION

Achievement of a predictive universal digital healthcare ecosystem in the current context is a challenge. State-of-the-art technologies demand user centric design, data privacy and protection measures, transparency, interoperability, scalability, and compatibility to achieve the SDG objective of sustainable healthcare by 2030.

Barriers to blockchain adoption in health-care industry: an Indian perspective

Purpose

This paper aims to identify barriers toward the adoption of blockchain (BC) technology in Indian health-care industry and also examines the significant issues of BC applications in health-care industry.

Design/methodology/approach

The barriers of the study are identified by two phases including the review of literature and semistructured interviews with hospital staff and administration operating in India. The experts (N = 15) are being taken from top-level management, IT experts and patients from the hospitals. The study implemented integrated total interpretative structural modeling-FUZZY-Cross-impact matrix multiplication applied to classification (TISM-FUZZY-MICMAC) methods for identifying the interrelationship among the barriers.

Findings

A total of 15 barriers have been determined in the Indian health-care industry through discussion with the selected experts. TISM is applied to develop multilevel structure for BC barriers. Further, FUZZY-MICMAC has been used to compute driving and dependent barriers. The findings suggest that low awareness related to legal issues and low support from high level of management have maximum driving power.

Research limitations/implications

The present study applies multicriterion approach to identify the limited barriers in BC adoption in health care. Future studies may develop the relationship and mark down the steps for implementation of BC in health-care setting of a developing economy. Empirical study can be conducted to verify the results along with selected case studies.

Practical implications

The present study identifies the BC adoption barriers in health-care industry. The study examines the pertinent issues in context to major support required, bottlenecks in adoption, key benefits of adoption planning and activities. The technology adoption practices are expected to provide applications such as distributed, secured medical and clinical data and patient centric systems that will enhance the efficiency of the health-care industry.

Originality/value

The study is among few primary studies that identify and analyze the BC adoption in health-care industry.