The emerging awareness of shared risks that face animal and human populations (ie, a concept that was originally part of the One Health approach) fosters opportunities for leveraging large amounts of unexplored data sources on zoonoses to generate new information and knowledge. The scope of the ODH includes supporting rapid and more accurate methods for detecting disease trends, outbreaks, pathogens, and causes of emergence [8,39-41]. The recognized, paramount importance of collecting digital health data to strengthen our understanding highlights the need for frameworks and tools that support and acknowledge the shift from big data to smart data. Smart data involves innovative processes that are necessary for answering pivotal questions from the health sector [33]. Addressing these issues requires new, ecosystem-driven paradigms and transformative technologies that reinforce traditional surveillance systems’ ability to prevent and control diseases. These innovations will further impact intersectoral coordination; link human and animal health data; allow for the flow of reliable information and knowledge; and promote the proper use of infrastructures, systems, and human resources for detecting outbreaks, thereby ensuring good health and well-being.

Discussing populations and their societal organization does not simply mean providing a generalization of concepts that are related to personalized health care and well-being. Although we are discussing individual-level, predictive, personalized, preventive, and participatory health care [42-44], shifting to the topic of population-level, unified perspectives requires us to account for the differences between individuals. Contrary to a one-size-fits-all approach, personalized health has to take into account an individual’s variability in terms of genes, environments, digital health literacy, preferences, and lifestyles. This requires a systematic integration of knowledge (ie, knowledge about health care, veterinary care, agriculture, meteorology, climate change, environmental protection, and intelligence) [45] into novel decision-making processes. These processes include decisions about the prevention and management of all hazards and threats to public health, such as exposure to animal diseases with zoonotic potential, environmental exposures, drug adverse events, the rise of misinformation [46], instances of diseases, and disease outbreaks [47].

There is a lack of solid and recognized leadership. Therefore, establishing an ethical framework that is socially and culturally accepted will lead to thriving digital platforms that can develop and grow independently. Such platforms will evolve to deliver the needs of a business.

Due to peoples’ current understanding of the term “environment,” many assume that “environment” refers to a systemic perspective that focuses on the deep, existing connections among the sky, water reserves, and soil (ie, the global environment or the environment of specific geographic areas). In this viewpoint paper, the concept of environment refers to the consideration of all living and nonliving things in an ecosystem. This accounts for issues that are related to biodiversity conservation and the intimate links among the health, care, and well-being of all components in any given ecosystem [48].

The control and governance of environmental sustainability can be best approached by assessing ecosystem services that are capable of quantifying and valuing all the goods and services that are generated within the ecosystems themselves. Recently, such networks have been increasingly endowed with digital technologies such as (1) environmental management and monitoring information systems; (2) automated and scalable approaches for collecting, digitalizing, and assembling geocoded big data; and (3) information-fusion algorithms that use multiple data streams and clinical decision support algorithms that integrate population-based, public health–focused perspectives into outbreak detection–focused management systems [49-51].

Environment turns therefore out as “digital” in ecosystems that, under a digital vest, feature animals and humans, as well as software and robots, as autonomous and interacting agents to enhance decision support. The goals of digital ecosystem implementation include improving the efficiency of communication and avoiding peoples’ dependence on centralized or distributed control dynamics [52]. Such digital biodiversity originates from common substrates of data, information, and knowledge that are now accessible, available, and able to be analyzed in novel and innovative ways. However, this puts us into a Copernican situation [53], which is a prerequisite to figuring out appropriate digital solutions (ie, methods for safeguarding digital biodiversity) that are based on people’s intelligence and rationality. Such solutions are preferable over those that are gained through traditional artificial intelligence methods [52,54].

In the broad digital ecosystem, ODH interventions must support, improve, and lead to efficient end-to-end processes for predictive, personalized, preventive, and participatory health care [42]. The management of disruptive innovation is the catalyst required for making this leap forward [55]. This requires the systematic, continuous, and intelligent integration of big, smart, and multidimensional data into digital health technologies. Such technologies are used for detecting, monitoring, and tracking the origins and causes of emerging pathogens, disease outbreaks, and disease-related trends that affect both humans and animals in the ecosystem. Thus, a challenging key issue that ODH must face is collecting and managing critical indicators that are related to human and veterinary health care, education, citizen engagement, environmental observation, and the health care industry. However, these indicators are required for active innovation. The COVID-19 outbreak is an example of an event that has triggered digital innovation and revolution in health care (ie, including ODH) on a global scale; the pandemic has quickly strengthened the overall care domain [56].

The three perspectives in the “individual-population-ecosystem” triad need to be further assessed as individual perspectives and as a specific set of strongly interconnected dimensions. These aspects will be discussed in-depth in the next sections.

ODH should not be limited to higher education in health-related fields. Basic and transversal knowledge and an understanding of health and its digitalization must be provided by education programs for all levels and disciplines (ie, similar to how the COVID-19 pandemic needs to be understood at all levels). ODH is about more than supporting data management. ODH provides learning opportunities to citizens, so that they can protect and take care of themselves when they share personal data on social networks [57-59]. At its core, ODH teaches people how to differentiate between real and fake news, which are disseminated over the mass media and the internet.

The next generation of researchers and practitioners will be actively involved in redefining critical transition points for incorporating new knowledge into ODH, and constantly updating the underlying philosophy and the scientific, technological, and regulatory implications of ODH [60]. Fully appraising an ODH perspective is challenging due to its intrinsic complexity. Therefore, researchers, practitioners, and citizens need to engage in science and cocreate sound implications for health and well-being. The core challenges of ODH include (1) establishing a systemic and integrated understanding of the health and wellness of humans and animals in their common ecosystem; and (2) establishing how digital systems may support and improve the health and wellness of humans and animals. Therefore, it is necessary to provide learners with project-based learning and case-based learning, which are well-known methods that allow learners to autonomously develop their skills and form learning communities [61-64].

Supporting digital health literacy by actively encouraging everyone (ie, from children to elderly people) to engage with personal health, public health, and environmental monitoring systems can increase citizens’ awareness. Advancements in mobile and ubiquitous tracking, reporting, and follow-up apps/web-based systems have provided opportunities for citizens to engage and collaborate with governmental, health, environmental, and ecological organizations [65]. Therefore, opportunities in which citizens use their electronic health data to manage personal health and share personal information require citizens who trust in health care delivery. The use of implemented opportunities depends on patient engagement, the direct benefits for users, and health care management organizations [66]. Smart cities and neighborhoods (ie, cities and neighborhoods that extensively use smartphones that can geolocate individuals and trace/track behaviors) [67] can be intrusive sources of information. The act of intrusively obtaining information has introduced ethical dilemmas, challenged citizens’ trust, and disrupted societal norms. The European General Data Protection Regulation has been implemented to reduce the amount of excess data in the overall data sphere. Nevertheless, in cases of force majeure, intrusive approaches that are implemented during pandemics outbreaks (eg, the COVID-19 outbreak) may help public health decision makers understand how to develop efficient policies that are based on real-world and near–real-time data [68]. Therefore, policy decision makers may decide to passively involve the population by allowing the use and analysis of data on individuals, mass movements, and individuals’ (ie, humans’ and animals’) engagement with and access to certain facilities. This would mean having citizens engage with complex policy making over time (ie, for policies that are related to ethics, regulation, decisions on big smart data, and the shaping of social norms) [69]. From an ODH point of view, collaboration and transparency are essential for enhancing health systems’ and government authorities’ times to response during disruptive and potentially major events [45]. For example, in Taiwan, efficient smart contact tracing analyses were conducted to support an automated alert messaging system, which informed citizens about who required quarantine and isolation after coming into contact with the potentially infected passengers of the Diamond Princess cruise ship [70]. Furthermore, the European eHealth Network established the cross-border directive on patient’s rights to cross-border health services (ie, article 14), and recently developed guidelines for the interoperability of contact tracing apps [71]. These examples show how ODH views the health-related and environment-related movements of individuals. However, several countries have discontinued COVID-19 contact tracing apps after data protectorate organizations considered these apps to be too intrusive [72].

ODH aims to answer complex questions about zoonotic disease follow-ups by assessing public health impacts, surveilling human and animal health, and conducting related risk assessments and management processes [73,74]. Recently, health care delivery services have focused on providing services to clusters of patients [75]. The timely identification of health care customers, efficient service provision, and the continuous improvement of care quality accelerate the digital transformations of human and veterinary health care systems. In the context of the COVID-19 pandemic, environmental factors (eg, weather, pollution, and food) and animal health (eg, pets, livestock, and food) [76] influence human health, care, and well-being.

Digitalization and the increasing use of mobile and ubiquitous health/wellness apps and platforms have allowed for the assessment of patients (ie, human and animal patients) via dynamic, near–real-time methods that use patient-generated and participatory data. These methods enhance personalized health delivery services, such as preventive recommendations and predictive alert systems [77]. This new time- and space-oriented paradigm recalls connected health model features. It also allows us to view teleservices (ie, digital health services) as a full component of integrated care for health care customers and citizens who share their data to support the development of new health care practices and guidelines.

An example of connected health tools is SMS text messaging services, which are used to maintain patients’ involvement in their treatments (eg, vaccinations, rehabilitation, and medication renewals) by improving their adherence to therapy [75,78,79]. In terms of pets or livestock, owners can use connected health tools to stay involved in the health surveillance and follow-up of their animals [80]. In terms of the ODH framework, connected health tools support efforts for increasing global health security, as these tools help with collecting near–real-time feedback from populations and health care organizations [81].

The health care system that deals with humans and animals is comprised of (1) health care service providers and insurers; (2) medical equipment and pharmaceutical product producers, sellers, deliverers, and maintenance service providers; (3) regulatory agencies and standardization organizations; and (4) health care customers, patients, and caregivers.

The entire health care sector is driven by digitalization and interconnectivity, which make up the foundation of new automation paradigms. The main challenges of the new health care framework include managing, collecting, storing, archiving, and analyzing a wide range of real-time data that are made available by any kind of organization and any kind of connected system. The leading goals of the current industrial revolution (ie, Industry 4.0) [82] are to enhance the automation and connectivity of systems, by increasing the interoperability and flexibility of systems and allowing for decentralized, real-time data collection and storage. The objective of Industry 4.0 is to use findable, accessible, interoperable, reusable, ethical, and revisable (FAIRER) data [83]. By adopting a FAIRER model, health care will benefit from greater traceability, flexibility, adaptability, and efficiency in health delivery processes [84]. As the health industry continues to adopt the ODH paradigm, a strong dependence on smart data means that standardization and interoperability framework issues are more critical than ever. A large number of systems need to be interconnected to collect real-time data that are generated by all parts of the health care system. The Health Level 7 Fast Healthcare Interoperability Resources standard [85] has been quite successful in this regard, as it focuses on the use of application programming interfaces within and across health/care sectors to improve a wide range of areas (ie, interconnectedness to contractual agreements).

The global health care industry has used the same technologies as those used by Industry 4.0 in the digital era [86], to achieve the use of the well-known “4.0” suffix. Telemonitoring systems use technologies that were initially designed for Industry 4.0. These technologies have been extensively used in human medicine [87,88]. Recently, their use has expanded as a result of the COVID-19 outbreak [89], as teleconsultation technology use has become the new norm in medical care. In terms of ODH, the same approach has been used by veterinarians. They can use Industry 4.0–related technologies to receive health and wellness information from wearable devices or implanted sensors that provide data on physiological signs. Veterinarians can also use these technologies to receive third-party, pet-related information [90]. The integration of these systems into pet health care may provide a better understanding of factors that affect pets’ health and pet owners’ benefits. Moreover, data on changes in a pet’s health can be used as pet owner health indicators [91] and environmental quality indicators [92].

3D printing is an emerging dynamic field in health care industries [93], including therapeutic industries (eg, printing prosthetics implants), human and veterinary care industries (eg, 3D printing in dental surgery), and educational industries [64]. The use of this on-demand technology supports the provision of personalized therapies and medical products to patients who are directly involved with health care practitioners. 3D printing has been shown to reduce the time to treatment by removing the need to travel back and forth between production laboratories and clinics.

Environmental monitoring is a critical dimension of community-oriented management in day-to-day situations or disruptive situations, such as hazardous material incidents or disasters (eg, the COVID-19 pandemic) [94,95]. The digital landscape has allowed theoretical and laboratory-limited developments in automation and artificial intelligence (ie, those in the last several decades) to be put into practice [96]. Discussing the environment dimension of ODH means having to discuss the “human-nature-pollution” relationship and its role in the smart city paradigm. People who live in high-density urban areas have been increasingly using technological systems to report unusual events, thereby improving citizen engagement [97,98]. Furthermore, these people have been increasingly using green technologies, which reduce the negative impact of certain actions on the environment (eg, carbon dioxide emissions, car traffic, and waste production) and health [99].

With regard to the ODH, this environmental revolution means more than using smartphone and Internet-of-Things technologies for behavior, wellness, and health monitoring purposes. This revolution is a consequence of the Industry 4.0 revolution [100], which aims to enable citizens and their communities to interact with complex, but easy-to-use, digital systems. However, the Industry 4.0 revolution has different issues, which are mainly related to development costs, monitoring costs, implementation costs, maintenance costs, and the understanding and expectations of users (eg, citizens and decision-makers) [101].

The environment dimension is strongly related to the human and veterinary health care, One Health, and ODH. Wild and domestic animals are used as a part of surveillance platforms that provide early warnings about health hazards, which may impact the whole ecosystem. This is an example of why the environment, animals, and humans are part of the One Health “individual-population-ecosystem” triad [102]. Environment systems must be able to integrate, and be integrated into, health care management systems, to enrich data, information, and knowledge; actively support decision-making processes about environmental exposures and health risks; and disseminate efficient recommendations with the right communication tools [103]. This ODH dimension also relates to smart environment platforms that provide information to smart homes to monitor and deliver personalized services to older adults [104].

The main aims of digital health are improving health,  care, wellness, and public health through BMHI research [22]; digital health expands these concepts by allowing for the consideration of digital consumers who use a wide range of smart-devices, connected medical equipment, or connected wellness equipment. Digital health also encompasses other digital technologies for health, such as the Internet of Things, artificial intelligence, big data, and robotics. These technologies have become a full component of day-to-day life, [84] and in some cases, health-related crisis management [46,70]. Healthy individuals, chronic patients, and health management organizations have been increasingly using automated reminder systems, smartphone apps, and health monitoring wearables [75,105,106]. In terms of ODH, technology serves as the catalyst for digitizing One Health information for ODH; advanced technological innovations have emerged to improve citizen engagement and empower future health ecosystems (Figure 1).

In health crises like the COVID-19 pandemic, the applications of digital technologies (eg, managing, preparing for, and mitigating crises) include the planning and scheduling of response and recovery processes. Digital technologies use surveillance, contact tracing, contact tracking, testing, confinement, and other health and care methods when necessary (eg, after ethical and legal implications have been considered) [107]. A similar level of attention is necessary for effectively tracking veterinary information that needs to be extracted from vast amounts of data. This process involves the integration of animal medical data into real-time information systems that are dedicated to supporting public health [108]. Thus, the role of veterinarians and veterinary informatics enhances that of BMHI (ie, protecting health care), as veterinarians have been called on to help deal with the increase amounts of dynamic data on emerging and reemerging infectious diseases. Local and supranational indicators of health have been used to identify global-level risk factors and causes of health problems that arise at the human, animal, and environmental levels, which need to be taken into account [109,110].

According to the WHO vision, establishing an interoperable digital health ecosystem that is capable of seamless, secure health data exchange and processing is crucial for combating pandemic outbreaks. Developing infrastructures and apps that allow the use of health data to manage adverse events is within the scope of achieving the 17 health-related sustainable development goals that were issued in the WHO global action plan [111]. We therefore introduced the concept of digital One Health interventions [112] in this viewpoint paper as a set of digital functionalities that should be designed and deployed to (1) support specific initiatives that address human, animal, and environmental systems’ needs and challenges; and (2) assess, study, and collect data on these systems’ expected outcomes, unexpected outcomes, and effects [36]. This related to the selection of timely metrics for the outcomes of multicriteria decision analyses.

These digital functionalities can be also called “digitalities,” as they account for how technology has been embedded into human experiences and humans’ daily lives. This has resulted in core issues in social and cultural anthropology (ie, digital humanities) [112]. Digitalities also account for how humans affect animals’ daily lives, animal health issues, and human-animal relations. These digitalities aim to increase animal welfare (ie, digital animalities) [113]. Furthermore, digitalities aim to manage the complex web of human, animal, and environmental interconnections, by improving environmental governance (ie, digital environmentalities) [114,115].

The delivery of digital One Health interventions and the mechanisms of the impacts and contextual factors of these interventions can be referred to as “ODHness.” This term is based on the combined digitalities of the three overarching, complementary perspectives. These digitalities apply to the five ODH dimensions and their constituent subcategories. Therefore, the technology ring in Figure 1 serves as the connection between the ODHness concept and relevant digitalities.

Figure 2 shows several topics that are relevant to each of the three digitalities (ie, the three colored areas inside the technology ring). Each topic is characterized by each portion within the corresponding colored area; the width of these areas depends on the number and the nature of the area’s related subtopics. It should be noted that these subtopics have not been clearly represented in the figure to ensure that people comprehend the ODH scheme. In terms of digitality, a specific metric or set of metrics must be used to evaluate the levels of development, use, contribution for each digital One Health intervention topic. These levels are represented with black dots for each subdomain, connected with each other via dashed lines. Since interactions between topics from different digitalities are possible, the edges that connect the different areas are represented with different colors. In addition, “trialities” (ie, triangles that connect all three digitalities) are represented via thick lines. Furthermore, each edge is assigned an appropriate weight. This of course means that harmonization must exist between the implemented metrics. Harmonization is required to fulfill the needs of a multicriteria decision analysis.

The technology ring in Figure 2 encompasses the set of digital functionalities that the ODH framework relies on. Therefore, technology acts as the catalyst that connects and unifies the ODH disciplines. ODHness is represented by the technology ring’s “center of mass” (ie, the red dot in Figures 2 and 3). More specifically, the “center of mass” is the unique point where the weighted relative position of the distributed mass (ie, the edges) equals 0. In other words, this means that a simple set of digital technologies (ie, those related to the human, animal, and environmental fields) is not capable of providing timely solutions in an interdisciplinary context, unless these technologies act as the catalyst for balancing the different perspectives, needs, and interests of ODH. In Figure 2, we attempted to highlight the “human-animal-environment” computer interaction research area, which includes technology designs that support animals in different contexts, and the development of user-centered approaches for designing technologies that are intended for more-than-human animals [116].

Examples of possible pandemic-related interactions within the technology ring are shown in Figure 3. Several of the main topics of each digitality are shown. The levels of development, use, and contribution for each digital One Health intervention are also shown. For convenience, the ODHness point is represented by the center of the technology ring.