The field of eHealth is about the use of digital technology in health care delivery, management, and education. In its definition, the World Health Organization (WHO) emphasizes the aspects of cost-effectiveness and secure use of information and communications technologies in support of health and health-related fields [1]. Expedited by the recent COVID-19 pandemic [2,3], multiple technologies that are broadly labeled as eHealth facilitate remote patient monitoring, improve access to medical services, and enhance efficiency in health care systems. For example, mobile health (mHealth) apps offer significant value to patients [4] by supporting data sharing with health care providers. This enables personalized care, promotes continuity of care, and enhances understanding of condition progression and treatment response during medical appointments. Artificial intelligence (AI), a recent advancement in eHealth, is poised to reshape medicine, improving the experiences of health care professionals and patients [5] through pattern recognition and generating insights that can improve diagnosis, treatment, and patient outcomes. These and other eHealth technologies enable patients to actively participate in their health care decisions and promote preventive care through personalized health information [6]. With the potential to streamline workflows and improve health care outcomes, eHealth leverages IT to transform access to and delivery of health care services [7].

This comprehensive bibliometric analysis examines the scholarly landscape of eHealth research over the past 25 years. We map research directions in this domain and the contributing scientific disciplines that have shaped the field. Bibliometric methods allow to quantitatively analyze published studies and their metadata to describe research output and to visualize intellectual structures and trends [8] in scientific domains of interest. The field of eHealth was the subject of bibliometric reviews; however, their scope was almost always limited to select technologies, regions, eHealth user experiences, or narrowly defined health and wellness goals.

Most bibliometric reviewers summarized literature subsets defined by eHealth user needs, such as promoting physical activity, healthy eating, and weight loss [9-12]; preventing substance use [13]; and providing e-mental health services during the COVID-19 pandemic [14]. In addition, bibliometric researchers reviewed digital technologies for health behavior change [15] and eHealth tools for anticoagulation management after cardiac valve replacement [16].

A distinct subset of bibliometric studies focused on eHealth and health informatics competencies [17], literacy [18], and information and communication technology use by individuals experiencing homelessness [19]. Region-specific bibliometric reviews demonstrated global interest in eHealth research: medical informatics and telemedicine in sub-Saharan Africa and BRICS (Brazil, Russia, India, China, South Africa) countries [20], eHealth research in Southeast Asia [21], and European funding of research on ambient assisted living [22].

Technology-centered bibliometric reviews assessed literature on technology adoption [23], telehealth [24], the internet of things [25], telemedicine in rural areas for cost-effective and sustainable health care [26], mHealth as a means of involving citizens and public agencies in cardiovascular disease prevention [12], and AI adoption by health care organizations [27].

Two broad-scope bibliometric reviews published in 2022 were dedicated to eHealth [28] and digital technologies [29]. The former study was limited to 2989 bibliometric records (2000-2021) that mentioned eHealth in titles; the latter included only 403 recent (2017-2021) publications. Other eHealth reviews published before 2022 did not include recent studies on evolving eHealth technologies, such as blockchain and AI [6,30].

To address gaps and limitations of past bibliometric reviews of eHealth, this study was designed with a broad chronological scope (2000-2024). It included recent publications from 2022 to 2024 from 2 different databases, a comparison of articles and reviews, and documents that mentioned eHealth not only in titles but also in abstracts or keywords. Our overarching aim was to examine a wide range of studies to provide a comprehensive overview of the eHealth research field, tracking its research directions, concept evolution, chronological developments, and multidisciplinary roots. A broad understanding of eHealth scholarship, as it developed over 25 years, is critical for informing policy makers, funders, researchers, educators, and practitioners about the dynamics of the field and thus supporting informed decision-making and advancing further research.

First, we endeavored to reveal research directions by analyzing the structure and contents of bibliometric networks. The network structures may help to identify distinct groupings of interrelated research topics. The network contents may provide clues about the stakeholder groups and their needs, which eHealth technologies were designed to support. We asked the following question: What research directions define the domain of eHealth? (research question [RQ1])

Second, we aimed to understand the chronological development of eHealth scholarship by examining publication trends across 2 document types: research articles and review articles. Their comparison might indicate areas that had accumulated a sufficient body of primary literature to warrant its synthesis. In addition, to gain insights into the maturation of research concepts and topics within the broader eHealth domain, we attempted to identify temporal lags between the emergence of active research areas and the publication of corresponding review articles summarizing those literatures. We posed the following question: How did eHealth scholarship—articles and reviews—develop over time? (RQ2)

Third, to offer a comprehensive account of eHealth as a multidisciplinary field, we attempted to document the disciplinary origins by mapping intellectual structures that have shaped the eHealth field. Our inquiry was guided by the following question: On what scientific fields does eHealth research build, as evidenced by cited sources and OpenAlex concepts that tag eHealth articles? (RQ3)

This bibliometric study does not involve any human subject as the term is defined at CFR 46.102(e)(1). It uses bibliographic data about academic publications from publicly accessible databases, adhering to the principles of open science. Therefore, ethical concerns related to informed consent and privacy are not applicable and we did not seek an ethics review board assessment.

We retrieved and screened 2 sets of Web of Science (WoS) records with eHealth or e-Health in titles, abstracts, or keywords: (1) 5000 most relevant articles, according to the WoS ranking algorithm; and (2) a nonoverlapping collection of 1885 WoS eHealth reviews written in English since 2000.

Figure 1 shows the study identification process as a PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) diagram, including search queries performed in WoS and Open Alex databases; initial removal of records based on year, language, and document type; and subsequent record screening choices guided by RQs and database limitations.

The hyphenated search term, e-Health, produced both relevant and irrelevant records. Any word ending with e before the word health was counted as e-Health, prompting manual screening of WoS records. Not knowing exactly how many records would be screened out, we oversampled WoS articles. Only 5000 WoS articles with the highest relevance ranks were retained after screening. To extend our WoS findings, we also obtained 10,022 OpenAlex articles with eHealth in their titles or abstracts. OpenAlex search query was limited to eHealth to avoid potential issues with the hyphenated search term.

We built and analyzed the following types of bibliometric networks or maps in VOSviewer [31] designed by researchers from the Center for Science and Technology Studies at Leiden University in the Netherlands: keyword co-occurrence networks for WoS datasets and a concept co-occurrence network for an OpenAlex dataset. The contents of networks were examined to identify eHealth research directions, conceptualized as eHealth technologies, stakeholders, and their needs. Keywords are controlled vocabulary used by the authors and WoS database managers to index studies. They differ from concepts that OpenAlex assigns to the majority (85%) of published works in its database. OpenAlex uses a hierarchical system of approximately 65,000 concepts, each linked to a Wikidata ID, to tag scientific publications with ≥1 concepts that are assigned based on the contents of the title, abstract, and the title of the host venue [32]. Concepts could add value above and beyond keywords because OpenAlex designers hierarchically organized concepts such as a family tree, starting with 19 major categories that branch out into discipline-relevant concepts [32].

Bibliometric networks consist of nodes, for example, keywords, concepts or cited journals, and the lines that link them [31]. The links represent relationships between nodes. For example, the more publications in our collection are indexed with the same keywords (or tagged with the same concepts), the stronger they would be linked and the closer they would be located to each other in our maps. Node size indicates the frequency of a specific keyword or concept—the number of documents that are indexed or tagged with it. Moreover, related nodes are grouped into clusters. Network overlays are information layers that highlight select keywords in red, such as those related to eHealth objectives, while the remaining keywords are shown in blue. Overlays help to contextualize the meaning of individual keywords through spatially close and linked nodes, which show keyword co-occurrences and interconnected research domains.

An abstract review was performed on multiple occasions to decipher ambiguous keywords or concepts or to identify examples of studies relevant to our main findings.

To answer RQ1, we analyzed network clusters—groups of co-occurring keywords or concepts that commonly reflect thematically distinct research directions [33,34]. A comparative analysis of 2 keyword co-occurrence networks was done. The network built for articles most relevant to eHealth, as determined by the WoS ranking algorithm, was compared to the network for all available eHealth reviews from WoS. Specifically, we compared clusters that imply research directions and node sizes, indicative of similarities and differences in research directions pursued by article authors versus review authors. In both maps, we assessed nodes with the strongest links to eHealth to reveal terminology at the heart of this research domain.

We used binary 0 and 1 coding to highlight keywords about groups involved with eHealth (who); health conditions, needs, or care settings addressed by eHealth interventions (what); and eHealth technologies or technology-related keywords (how). To estimate reliability, a second trained coder independently identified technology-related keywords from a list of 677 keywords selected for mapping, achieving a high level of agreement (κ=0.96; 95% CI 0.93-0.98; P<.001). Additional keyword coding was done as we developed a conceptual model of eHealth research. Binary codes were assigned to technology keywords based on their relevance to eHealth umbrella terminology or eHealth applications, objectives, infrastructure, data security and privacy, and health analytics. We added the aforementioned codes to a scores file in VOSviewer to display them as custom overlays to the keyword map for WoS articles.

RQ2 was answered by contrasting network overlays to draw conclusions about publication recency for articles and reviews. We also computed mean publication years for groups of keywords that characterized eHealth application types.

Evidence for RQ3 came from the scientific literature behind eHealth reviews, which were assessed using a cocitation map. In this network type, the nodes correspond to journals and other cited sources. The relationships between journals are defined based on the frequency with which they are jointly referenced or “cocited” within the bibliographies of eHealth literature reviews. The larger the node size, the more frequently the journal was cited by the authors of eHealth reviews. We chose reviews for this analysis because their bibliographies tended to be the most comprehensive and focused on well-researched eHealth aspects.

A concept co-occurrence network for OpenAlex eHealth studies was used to gather additional evidence about the multidisciplinary nature of eHealth to answer RQ3. In an OpenAlex concept map, many nodes are represented by discipline-relevant concepts nested within major categories, both of which are relevant to answering RQ3. Tagging the greatest number of eHealth studies, major categories would be the largest nodes in this map, whereas the smallest nodes would be specific topics relevant to our understanding of technologies researched by eHealth scholars. Similar to the network of WoS article keywords, we enhanced the OpenAlex concept network with custom overlays highlighting technologies, health topics (physical health, illness, wellness, and mental health), and other concept characteristics such as risk (eg, security) and money (eg, economics). Concept attributes were first coded using linguistic inquiry and word count (LIWC)–22, a computational linguistics program, and then converted to binary scores (code 0, or “not present,” was assigned to LIWC scores of 0, and code 1 was assigned to all other LIWC scores). The binary scores were manually verified and refined before being added as new overlay scores to the VOSviewer map file, in addition to mean publication year and normalized citations overlays.

To help readers follow our map interpretations, we used italics to indicate specific network nodes, whether they are WoS keywords, cited journals, or OpenAlex concepts. Unless noted otherwise, we consistently listed nodes based on the number of articles they represented, from high to low.

In Figure 2 [35], we presented a keyword co-occurrence cluster map. Color-designated clusters are thematically linked groups of keywords derived from WoS articles. We provided a URL for an interactive map where the number of articles indexing each keyword can be explored, as well as keyword interconnections. The more frequently 2 keywords co-occur across multiple articles, the more likely they are to be located near each other, within the same cluster, and linked. The map shows 677 keywords and 1000 strongest links. Alternately spelled nodes eHealth and e-Health had the strongest co-occurrence link. In addition, the keyword eHealth was strongly linked to 9 other keywords: telemedicine, mHealth, internet, digital health, self-management, mobile health, intervention, telehealth, and depression.

Next, we summarized clusters by categorizing their most frequently occurring keywords in Multimedia Appendix 1 to identify stakeholders, care needs or settings, and eHealth technologies. Cluster 1 (shown in red in Figure 2) encompassed thematically diverse nodes related to eHealth with a centrally positioned self-management keyword indexing 217 articles, the third highest occurring keyword after impact and interventions. Abstracts that mentioned “self-management” suggested that the authors defined it as an oversight of one’s own health conditions, for example, to cope with a chronic disease by reducing anxiety, fatigue, or depression or to prevent negative health outcomes. Mental health (depression, anxiety, psychological distress, schizophrenia, and cognitive-behavioral therapy), cancer, and pain-related keywords were particularly prominent in this cluster. The cluster had keywords that described study populations: young people, survivors of cancer, and caregivers. Article authors reported eHealth intervention technologies—web based and mobile applications—used for assessment, reporting of symptoms and adverse events, cognitive interviewing, and supporting self-management goals, for example, by generating and communicating self-management actions. Together, the keywords intervention and interventions are indexed in 863 studies.

Keywords are listed in the order of their occurrence counts, from high to low, excluding the keywords used to index <10 articles. All overlays to the Figure 2 map are listed in Multimedia Appendix 1 and can be explored interactively.

Cluster 2 (green) keywords were dedicated to telemedicine and telehealth as well as health organizations’ electronic record systems (eg, electronic health records) used for storing information that is accessed, used, and documented during a telehealth session. This cluster’s keywords mentioned eHealth stakeholders who were patients, different health care professional groups, health leaders, and communities. Specifically, telemedicine was researched as a means of building community capacity and communities of practice. In rural communities, telemedicine connected remote populations to health care professionals, strengthening local health systems. Geographically dispersed health care professionals could improve their medical practice through telemedicine-enabled knowledge sharing within communities of practice. This cluster also prominently featured nodes related to the acceptance and adoption of eHealth technologies. Abstracts that mentioned Technology Acceptance Model (TAM), or Unified Theory of Acceptance and Use of Technology (UTAUT), referred to stakeholder reactions to telehealth technologies and their impact on patient–health care professional relationship, with an emphasis on improved access to care and patient empowerment, patient engagement, and patient participation.

Keywords in cluster 3 (dark blue) were especially focused on 3 technological aspects of eHealth, namely, eHealth technology infrastructure, data security and privacy, and health analytics. Keywords relevant to the eHealth technology infrastructure were, for example, internet, online, internet of things, cloud computing, blockchain, information-systems, interoperability, smartphone, cloud, etc. The second group included security, blockchain, authentication, encryption, access control, cryptography, privacy protection, access-control, and other data security and privacy considerations. The third group was about health analytics: artificial intelligence, machine learning, big data, algorithm, algorithms, deep learning, data mining, etc. The most frequent stakeholder keywords in cluster 3 were management and hospitals, in contrast to keywords related to patients in cluster 1 and health care professionals in cluster 2.

Cluster 4 (yellow) was about eHealth literacy, health information seeking, and concerns about misinformation and decision-making during the COVID-19 pandemic. This subset of studies focused on younger and older age groups, with a strong focus on students. With the help of eHealth tools and skill assessments for health education, researchers studied demographic and behavioral aspects of health information seekers who engage with web-based health information. They described their research using keywords such as internet, eHealth literacy, social media, computer, digital health literacy, consumer health informatics, world-wide-web, and website. This cluster also included the keywords disparities and digital divide.

The remaining 3 clusters contained the smallest number of keywords. Nodes in cluster 5 (purple) reflected the needs of adults and older adults related to physical activity and lifestyle changes aimed at preventing obesity, hypertension, cardiovascular disease, and diabetes. Researchers studied how these needs were addressed through eHealth interventions and mobile apps. Cluster 6 (light blue) keywords suggested a focus on eHealth, mHealth, and digital health applications, as well as telemonitoring, telerehabilitation, and communication technologies, for managing chronic diseases and medication adherence in older adults. The care types spanned primary care, rehabilitation care, home care, and integrated care. Finally, keywords in cluster 7 (orange) indexed research on eHealth interventions for HIV prevention among men who have sex with men.

While analyzing clusters, we found keywords that could be described as general or umbrella terms (ehealth, e-health, technology, digital health, internet use, etc) and more specific eHealth applications (telemedicine, mhealth, telehealth, mobile health, electronic health record, telecare, etc). In addition, we encountered many instances of keywords that shed light on eHealth objectives (ehealth literacy, health literacy, communication, education, prevention, quality-of-life, etc). Scattered across all clusters, eHealth objectives pertained to stakeholders’ health conditions, needs, or care settings. We coded these keyword groups, as well as subgroups about technology infrastructures, data security and privacy, and health analytics, to make them available as overlays (Multimedia Appendix 1).

Figure 3 [36] shows a cluster map for reviews obtained from the WoS database. Cluster colors in Figures 2 and 3 were set automatically by VOSviewer based on the number of nodes in a cluster. Despite differences in cluster colors, many keywords, for instance, those related to mental health or obesity prevention, were grouped in similar ways in both maps. Out of 358 keywords that appeared in Figure 3, 318 (88.9%) were present in Figure 2. Similar to Figure 2, the node eHealth in Figure 3 was strongly linked to nodes mHealth, telemedicine, digital health, telehealth, and internet.

A close examination of a subset of 318 keywords that appeared in Figures 2 and 3 revealed differences in eHealth topics covered by articles versus reviews (Table 1). A delta of z-scored keyword occurrence counts for keywords used to index reviews versus articles was used as an indicator of research focus for the 2 document types. We asked which eHealth topics were more or less likely to be covered by eHealth reviews as compared to eHealth articles? Several patterns emerged when we analyzed differences (Δ>0.5 SD) [37].

Similar to reviews conducted in other health disciplines [38], eHealth review authors attempted to summarize experimental research. A randomized controlled trial keyword indexed a disproportionately greater share of reviews than articles. Second, review authors favored studies on telemedicine, telehealth, digital health, and mHealth. Feasibility studies were also a likely subject of literature reviews.

Other keywords salient in the eHealth article map did not receive much attention from review authors. Two findings that stood out the most were (1) few reviews of eHealth (or e-Health) literature, a research domain this study was designed to address; and (2) a disproportionately small number of reviews on eHealth literacy relative to the number of articles in this area. In addition, reviews somewhat underrepresented studies on eHealth technologies indexed with keywords privacy or security and issues of eHealth technology adoption, such as barriers, usability, and user acceptance. Some mental health keywords, for instance, eHealth applications of cognitive-behavioral therapy or those related to social support and self-efficacy, were more frequently used to index articles than reviews. These underreviewed topical areas may be considered by systematic review authors interested in eHealth.

To answer RQ2, a comparison of mean publication years overlays for eHealth articles and reviews was used to identify trends in empirical research production and its subsequent synthesis. Figure 4 [39,40] shows publication recency overlays for eHealth articles and reviews.

Figure 4. Publication recency overlays to maps in Figures 1 and 2: keywords indexing articles (top) and reviews (bottom). Interactive overlays [39] (articles) and [40] (reviews).

Both map legends range from 2017 (blue) to 2021 (red) and are centered around 2019 (gray color). In the top overlay of Figure 4 with keywords for articles, eHealth was most strongly linked to telemedicine (mean publication year for all articles indexed with the keyword=2018) and mHealth (2019), followed by internet (2017), telehealth (2018), mobile health (2020), and digital health (2021). Mean publication years were most recent (2021-2022) for eHealth articles indexed with Covid-19 or pandemic, mindfulness, wearables, digital health, deep learning and blockchain, burden, and artificial intelligence. Some of the same keywords (deep learning, Covid-19, and artificial intelligence) also represented the most recent (2021-2022) collections of reviews, in addition to the following keywords: men, sedentary behavior, internet of things (iot), fatigue, and patient-reported outcomes.

Excluding methods-related keywords, keywords with the oldest mean publication years (2012-2016) represented eHealth articles on telepsychiatry, computer, web, information technology, ethics, weight loss, medical informatics, breast-cancer, and primary-care. In addition to health information technology and medical informatics, the oldest reviews (2016-2017) were indexed with keywords computer, health communication, smoking-cessation, telecare, records, user acceptance, electronic medical records, and internet use. Importantly, e-Health consistently indexed older publications in both maps, as compared to eHealth, which is a welcome terminology standardization trend given the difficulties we encountered while retrieving e-Health publications.

Calculated across 318 keywords that appeared in Figures 2 and 3, the mean publication year was 2018.77 for eHealth articles and 2019.80 for eHealth reviews, a difference of about 12 months. The time gap between the mean publication date for all articles and all reviews indexed with mHealth was 8 months, M₃₅₆=2019.47 for articles and M₃₀₃=2020.10 for reviews. The time gaps were 11 months for studies indexed with eHealth, M₂₀₈₉=2019.08 and M₈₃₇=2019.96, for articles and reviews, respectively; 15 months for telemedicine, M₅₂₂=2018.32 and M₄₂₂=2019.62; and 16 months for telehealth, M₂₄₂=2018.07 and M₂₃₆=2019.93.

To answer RQ3, we analyzed multidisciplinary contributions using journal names that appear in reference sections of eHealth reviews (Figure 5) [41].

Figure 5. Cocitation network (cluster map) of sources for 1885 eHealth reviews. Sources that occurred ≥50 times in eHealth reviews’ reference lists were mapped. Link to an interactive map [41].

A 9-cluster model of journals contributing to eHealth reviews highlighted the leading role of the Journal of Medical Internet Research. It was cited the most, specifically 6329 times in 1884 reviews for which citation lists were available. It belonged to the largest cluster (cluster 1, red), with a large group of journals mostly dedicated to psychology and psychiatry.

In cluster 2 (green), the largest nodes were telemedicine, eHealth, and telecare journals, followed by journals in other disciplines—health informatics, public health, health services, medical education and health communication, clinical practice, HIV and AIDS research, and health care policy. Interestingly, we did not observe journals specializing in social media in this or any other cluster, given social media keywords observed in Figures 2 and 3.

Cluster 3 (dark blue) encompassed mHealth and ubiquitous health content (JMIR mHealth and uHealth), followed by cited sources in the fields of preventive medicine and public health; nutrition, obesity, and exercise; behavioral medicine and health psychology; and diabetes and endocrinology, among other disciplines.

Cluster 4 (yellow) was unique in that its sources were less likely to be cocited with sources from other clusters. Journals in cluster 4, related to sensors, AI, and health informatics, focused on IT, computing, health care, and biomedical topics. An interdisciplinary journal, Nature, was also in this cluster, a distant node with stronger cocitation ties to medical sources than most computing journals in this cluster.

Cluster 5 (purple) included journals in general and internal medicine, cardiology and cardiovascular medicine, epidemiology, and other specialized medical fields. Several leading medical journals (The BMJ, The Lancet, JAMA: Journal of the American Medical Association, and The New England Journal of Medicine) were among the largest nodes in this cluster.

In addition to general medical research sources, cluster 6 (light blue) had journals on pain, digital medicine, geriatrics and aging, rehabilitation and disability, rheumatology, and neurology. Cluster 7 (orange) was dedicated to cancer and oncology journals and journals in related health care fields, including psycho-oncology, palliative care and symptom management, nursing in oncology, quality of life and patient outcomes, cancer education, nursing, and palliative care.

Most journals in cluster 8 (brown) belonged to either respiratory medicine and allergology or pediatrics and adolescent medicine, confirming our earlier findings about eHealth interventions for this age group. Finally, cluster 9 (pink) consisted of gastroenterology journals, particularly those focusing on inflammatory bowel diseases and related conditions. It is important to note that some fields, such as nursing, were represented by journals in many clusters.

In addition to a cited journals analysis, we gathered evidence of multidisciplinary contributions directly from a large corpus of eHealth articles in OpenAlex, which were tagged with ≥1 concepts. Concepts reflect disciplines, theories, methods, and other abstract ideas. We developed a custom thesaurus to select discipline-revealing concepts for the map in Figure 6 [42]. Specifically, after removing most methods and statistics-related concepts (eg, sample or odds ratio), geography, and general ideas (eg, work) and merging synonymous concepts, we mapped the remaining 392 concepts representing disciplines and ideas relevant to eHealth. Each mapped concept occurred at least 20 times.

Figure 6. Concept co-occurrence network (cluster map) for 10,022 eHealth articles from OpenAlex. Concepts that occur ≥20 times were mapped. An interactive map is available from Leiden University’s VOSviewer application [42].

Figure 6 validated our earlier journal-level findings from Figure 5, confirming eHealth research connections to health care services, medicine, psychology, public health, education, and computer science. It also added to our understanding of the multidisciplinary nature of eHealth research by highlighting the prominent role, according to node size, of political science and law, economics, business, and knowledge management. The strongest connections with eHealth were observed for medicine and nursing and computer science, followed by economics and economic growth, political science, and law. While these concepts were most central to eHealth, numerous other fields, ranging from human-computer interaction and engineering to philosophy and linguistics, contributed to eHealth scholarship. In Figure 6 network, eHealth had the strongest links to internet and intervention, followed by the 4 concepts strongly connected to eHealth in Figures 2 and 3: telemedicine, mHealth, digital health, and telehealth.

In Table 2 [43-51], we contextualized our cluster-based findings with map overlays, examining how study attributes and concept characteristics were distributed across the map depicted in Figure 6. Links to an interactive map with overlays appear in Multimedia Appendix 1.

According to the mean publication year overlay, the nodes with the most recent mean publication year were misinformation (2022) and new technologies such as edge computing (2021), deep learning (2022), pandemic concepts (2021), and concepts about mental health and psychological well-being, including depressive symptoms, mental health literacy, insomnia, and loneliness, all of which had a mean publication year of 2021. Older eHealth articles were represented by studies classified by OpenAlex as computer science, engineering, business, health informatics, and public relations. Setting aside concepts not specific to eHealth technologies, telematics (2008), semantic web (2013), web service (2013), IT (2013), ubiquitous computing (2013), health IT (2014), information sharing (2014), cross-domain interoperability (2014), and informatics (2014) were the oldest technology-related concepts with the mean publication year prior to 2015. These findings assist in answering RQ2 about eHealth research development over time. Other overlays explained in Table 2 addressed concepts specific to eHealth technologies and objectives, as well as different aspects of health. In the subsequent sections, we have highlighted several points that are most pertinent to multidisciplinary contributions.

The technology overlay demonstrated that 34.9% (137/392) of OpenAlex concepts mapping eHealth were directly linked to technology, a clear indication that eHealth multidisciplinarity is only partially grounded in data sciences, engineering, and computer sciences. Interestingly, the social media concept (also represented as a keyword in Figures 1 and 2) stood out as an eHealth literacy technology with relatively weak co-occurrence relationships with most other eHealth technologies. We found some evidence of research on social media information campaigns and pandemic interventions, for example, a moderated Facebook group that brought together 200 health care professionals and >58,000 laypeople from Denmark to support an informed approach to following pandemic guidelines [38]. Nevertheless, our maps consistently depicted social media as a small domain, suggesting this research played a modest role in the eHealth literature we examined.

When OpenAlex map and WoS maps were created, VOSviewer calculated node scores indicative of mean citations and normalized citations. These analyses were outside of the study scope; therefore, we have included them in Multimedia Appendix 1.

In the sections that follow, we synthesize eHealth research directions by creating typologies of eHealth applications, other technologies, and their objectives. Furthermore, we explore groups of stakeholders involved in eHealth and systematize levels of research. Our synthesis lays the groundwork for an evidence-based conceptual model of eHealth, grounded in a continuous 25-year research effort.

To illustrate the provided typologies with examples, we attempted to give priority to publications labeled by WoS as “highly cited papers” because their selection procedure controls for the field of study and the distribution of citations over time [52]. When no highly cited WoS papers were available, we manually picked pertinent articles and reviews using topic relevance, citation counts, and recency as our selection criteria.

In this section, we also discuss our findings related to publication recency and multidisciplinarity. We conclude by stating limitations and implications for research and practice.

Keywords and concepts from our maps offered insights into the intended uses for eHealth technologies. Research into clinical applications of eHealth technology was in support of patient monitoring, diagnosis, treatment, and rehabilitation, as well as patient– health care professional communication. Public health objectives were directed at improving health literacy, education, prevention, quality of life, and well-being. There were also organizational and societal objectives, which we discuss in the eHealth Stakeholders and Research Analysis Levels section that introduces eHealth research levels.

Consistent with prior research that emphasized consumer-oriented eHealth solutions, patient-centeredness, and ownership of one’s health as defining features of eHealth [7], we found strong evidence of eHealth research on fostering engagement, self-care, participation, and person-centeredness. The following keywords from Figure 2 indexed the highest number of studies: self-management, self-efficacy, social support, engagement, satisfaction, participation, self-care, empowerment, motivation, patient empowerment, and involvement. Self-management apps, for instance, were successfully used to facilitate medication adherence and adherence to treatment of noncommunicable diseases: cancer, respiratory diseases, cardiovascular diseases, and diabetes. In 1 study, an electronic health record–linked web-based system for reporting cancer symptoms dispensed automated advice for either self-management or medical attention [53]. Improvements in physical and psychological well-being were reported for patients with cancer who used the system. A 2022 systematic review of factors influencing mHealth app adherence, defined as following the intended program as designed, found 17 studies of mHealth apps for self-management of noncommunicable diseases with >15,000 participants [54]. The review showed 53.4% overall app adherence and 89.5% medication app adherence.

This study highlighted eHealth objectives from past studies as an overlay to the concept map to guide objective setting by future eHealth designers. Our objectives overlay and the keyword map can be used for brainstorming possible measures to set intervention user prerequisites (eg, health literacy, self-efficacy, and motivation), outcomes, and manipulation checks to evaluate internal validity. The concepts and keywords in these maps reflect established terminology, facilitating literature searches for measurement instruments and the replication of past studies.

One of the study limitations is the exclusion of e-Health articles from the OpenAlex search query to avoid potential issues with the hyphenated search term. The literature of interest is undoubtedly broader than the 10,022 articles we mapped. Moreover, our study addressed high-level patterns in metadata, limiting visualizations to keywords and concepts that met preset occurrence thresholds, specifically keywords used in no less than 10 studies and concepts tagging ≥20 OpenAlex articles. Less frequently used keywords and concepts were excluded, some of which were likely very useful in understanding eHealth.

Our study revealed a recent trend to index eHealth studies with a digital health keyword. Mapping the literature using search queries that include both “eHealth” and “digital health” would provide the most comprehensive and up-to-date depiction of the research domain. Therefore, future bibliometric research should incorporate both terms.

While this bibliometric study did not offer a summary of evidence that can be expected from traditional systematic reviews, for example, on integrating eHealth solutions into practice, it provided a high-level overview of past research, pertinent terminology, and interlinked research directions, which were categorized and synthesized to develop a conceptual model of eHealth scholarship.

The implications of our findings are presented in Textbox 2, and recommendations for future studies are presented in Textbox 3. The implications are organized into 3 groups: practical implications of our conceptual model and maps, availability of numerous and recent reviews in many eHealth research areas, and multidisciplinary contributions to eHealth. For future researchers, we offer recommendations on research directions, as well as guidance on study design and publication.

The multidisciplinary field of study at the crossroads of health and technology is widely recognized as eHealth. Over the past 25 years, researchers studied a broad range of established and emerging technologies—educational and informatics tools, telehealth services, health record management, mHealth, and wearables—in support of consumer-oriented solutions for patient monitoring, diagnosis, treatment, rehabilitation, and patient– health care professional communication. Beyond health care services, the field of eHealth offers a large body of literature on health literacy, disease prevention, and wellness. Conducted at the individual, health system, community, and society levels, eHealth research continues to develop by incorporating new technologies, responding to health emergencies, and addressing the needs of diverse stakeholders.