Health 3.0 is a health-related extension of the concept of Web 3.0 in which data and information on the Web and other electronic devices is personalized to optimize user experience and outcomes (Chen et al. 2013; Wikipedia). In Health 3.0 consumers, including older people, can use portable electronic devices, desktop computers, or smart home devices, to read personalized health messages at their convenience, to communicate with clinicians and people with similar health risk profiles to improve their health knowledge, and to make informed decisions in personal health management. Only those consumers who are willing to take guidance from their healthcare providers, be responsible, and collaborate with their healthcare providers can engage in Health 3.0, using configurable, intelligent agents built on Semantic Web and network technologies.
The drivers for Health 3.0 are the same as Health 2.0 (Yu 2019). In order to understand Health 3.0, we need to understand Web 3.0, a term that was first coined by the reporter John Markoff of the New York Times in 2006. It refers to a new evolution of the Web, a further extension of Web 2.0. The new movement to apply Web 3.0 to improve health has led to the emergence of Health 3.0.
Key Research Findings
The Current Status of Health 3.0
Despite passionate advocacy for Health 3.0 to “unload” the heavily loaded public health system by digital health futurists (Gagnon and Chartier 2012; Kalra 2011; Nash 2008), the term is yet to receive wide acceptance by the general public, health community, and health informatics research community. To date, there is little peer-refereed empirical research to report Health 3.0 initiatives despite some pilot studies that report the implementation of Health 3.0 technologies to improve biomedical information services (Noy et al. 2008). Interestingly, due to the increased capability of expressing semantic meaning and presenting knowledge in flexible, computer-readable format, Semantic Web technologies have wider adaptation in smart home initiatives (Alirezaie et al. 2017; Chiang and Liang 2015; Huang et al. 2016; Mitchell et al. 2014).
Four Types of Dynamic Interactions Essential for Health 3.0 Success
Gagnon and Chartier (2012) proposed a model to map four types of key, dynamic interactions essential for the transformation of the current “dependent patient” healthcare paradigm to the Health 3.0 paradigm of “autonomous patient”: (1) healthcare providers and patients, (2) healthcare organizations and providers, (3) patients and Health 3.0, and (4) healthcare organizations and Health 3.0.
Healthcare Providers and Patients
Lussier and Richard (2008) classify three types of healthcare provider-patient relationships: expert-in-charge, expert guide, and partner and facilitator. The occurrence of any type is determined by three decision factors: (1) the severity of the patient’s health condition, (2) whether the condition is acute or chronic, and (3) the patient’s willingness and capacity to be co-responsible and collaborate with the healthcare providers in their personal health management. The second and third types of relationships are required for Health 3.0 to be adopted by all stakeholders and to be seamlessly integrated into the mainstream healthcare delivery system. For example, it is more likely for patients to engage in Health 3.0 when their chronic conditions are relatively stable, and they are willing and able to take co-responsibility in collaboration with their healthcare providers.
Healthcare Organizations and Providers
As a new philosophy of health management, Health 3.0 requires a new set of health service organization and delivery mechanisms that promote the second and third types of healthcare provider-patient relationship as suggested above and promote patients’ autonomy and active interaction with Health 3.0 to self-manage chronic conditions and well-being (Gagnon and Chartier 2012). This may not come easily because adequate information, guidance, training, and coaching from trusted healthcare providers are vital to motivate consumers, particularly older people, to commit to an ongoing, lifelong learning process (Seckin 2014). Without these, there cannot be safe and appropriate delegation of responsibilities to the consumers (Gagnon and Chartier 2012).
However, traditional healthcare providers often take a paternalistic and unidirectional approach of authority with their patients (Quill and Brody 1996). The common pay-for-service model discourages providers to partner with patients, to guide and facilitate them to learn and gain autonomy and co-responsibility for their disease management (Gagnon and Chartier 2012). Therefore, to promote Health 3.0, remuneration and performance measurement mechanisms need to be reformed to recognize and reward expertise, time, and effort in order for healthcare providers to change their role from paternalistic expert-in-charge to expert guide, partner, and facilitator for patients. Training and continuous medical education is also needed for healthcare providers to develop their competence or flexibility to change roles depending on the above three decision factors. They also need to develop competency to interact with Health 3.0.
Patients and Health 3.0
For consumers, such as older people, to effectively adapt and benefit from Health 3.0, long-term authentic, equal cooperation with healthcare providers is required. This is a demanding role that requires consumers to take responsibility in engagement, learning, and making decisions about their own health and care. Its legal implications are yet to be explicitly framed (Gagnon and Chartier 2012).
Based on a comprehensive literature review, Gagnon and Chartier (2012) proposed three high-level behavioral profiles for patients to engage in Health 3.0: (1) willingness and ability to take responsibility for their own health and treatment in cooperation with healthcare providers; (2) willingness to improve personal “health literacy,” including actively searching and reading appropriate information about their own healthcare; and (3) willingness and ability to learn and to use Health 3.0 in a secure manner.
Healthcare Organizations and Health 3.0
For Health 3.0 to be embedded in the mainstream healthcare delivery system, healthcare organizations need to encourage medical leadership to reach a new balance between traditional institutional interventions and interventions that are underpinned by the tridimensional approach in Health 3.0 that involves healthcare providers, consumers, and technology. In addition to invest in in-house digital health technologies such as electronic health records, mechanisms need to be established to integrate open-source and externally owned Health 3.0 solutions to deliver highly efficient, low-cost, personalized health services to consumers that meet the healthcare challenges in the twenty-first century.
Methods and Tools for Health 3.0
The technical foundation of Health 3.0 is Web 3.0.
Web 3.0 is connective intelligence, i.e., connecting data, concepts, applications, the virtual world, and the physical world. The five main technical features that distinguish Web 3.0 from its predecessor are the Semantic Web, artificial intelligence, 3D graphics, connectivity, and ubiquity (ExpertSystem 2017). Extending the functions of Web 2.0, Web 3.0 is “the portable personal Web” that consolidates dynamic content through Semantic Web and network technology to improve individual user engagement and experience (Agarwal 2009). It uses features such as widgets, drag and drop mash-ups, and iGoogle for personalization (e.g., in advertisements).
Semantic Web underpinning Web 3.0. The Semantic Web is the vision of the World Wide Web Consortium (W3C) of “the Web of linked data”, similar to the data in databases (W3C 2017). The term was initially coined by the father of the Internet Sir Tim Berners-Lee, who states that “The Semantic Web is not a separate Web but an extension of the current one, in which information is given well-defined meaning, better enabling computers and people work in cooperation” for tasks such as information retrieval (Berners-Lee et al. 2001). The advantage of Web 3.0 over Web 2.0 is its ability to support trusted interactions among computers over the network (W3C 2017) so that “they can perform the tasks necessary for us to do our work” (Giustini 2007). The Semantic Web is underpinned by a suite of Semantic Web technologies.
The Semantic Web technologies. The Semantic Web technologies include eXtensible Markup Language (XML), Resource Description Framework (RDF), Web Ontology Language (OWL), SPARQL, JSON-LD, and SKOS. XML allows everyone to create their own tags to add arbitrary structure to their documents. RDF is used to express meaning of data by encoding data in sets of triples; each triple is composed of a subject, a verb, and an object, resembling the structure of an elementary sentence in many human languages (Berners-Lee et al. 2001). These triples can be written in XML tags. RDF uses URIs to encode subject, verb, and object in a document. URI is the abbreviation of universal resource identifier (URLs, uniform resource locators, are the most common type of URI). Using URIs to name every concept ensures that concepts are not just words in a document but are tied to a unique definition that everyone can find on the Web.
Ontology. Ontology provides formal, explicit specification of a shared conceptualization that is machine readable, with data and their relationships defined by concepts, properties, functions, and inference rules (Studer et al. 1998). “The most typical kind of ontology for the Web has a taxonomy and a set of inference rules” (Berners-Lee et al. 2001). Web Ontology Language (OWL) is used to build ontology. SPARQL is the query language for the Semantic Web. It enables search semantic data. Based on semantics, computers can learn to interpret data and provide people with knowledge.
By retrieving a precise concept instead of ambiguous keywords contained in many Web pages (Berners-Lee et al. 2001), Semantic Web technology can significantly improve the accuracy of an information search in Web 3.0.
A significant feature of Semantic Web is building metadata, i.e., data about data, using ontology. This will allow a computer to dive into data to interpret it, to link data by relations and the associated rules, to use data as inputs for software agents to exploit artificial intelligence technologies to model and simulate systems, and to turn data into actionable knowledge (Hendler 2017).
The Semantic Web will extend into the physical world, such as home automation, when RDF and URIs are used to describe devices such as cell phones, electronic stoves, the DVD player, and TVs (Berners-Lee et al. 2001).
With inference rules and digital signatures to ensure trustworthiness of data sources, Web 3.0 will transform the Web into an organized giant database, along the lines of PubMed, or one of our trusted medical library catalogues, facilitating discovery of health knowledge (Giustini 2007).
Impact of Health 3.0 on Health and Medical Care
In an Editorial Commentary published in the British Medicine Journal in 2007, Giustini quoted a clinician’s remark: “Whereas web 1.0 and 2.0 were embryonic, formative technologies, web 3.0 promises to be a more mature web where better ‘pathways’ for information retrieval will be created, and a greater capacity for cognitive processing of information will be built” (Giustini 2007).
Different from Health 2.0, Health 3.0 brings new Semantic Web technologies into the arena of Internet technology. The semantic layer provides knowledge-based representation of the human action and environment, vital for an information system to achieve awareness of human position, action, and the associated context (Miori and Russo 2012). As ontology can represent semantic knowledge for “ambient intelligence,” the system can collect and analyze the digital traces of people and their living environment using the Internet of Things (IoT). For example, sensors and actuators can be used to acquire knowledge about everyday human behavior and human interaction with the ambient environment. This knowledge will enable researchers to design intelligent functions to assist older people with chronic conditions to cope with daily living tasks.
Utilizing these features, Health 3.0 has been broadly used by the biomedical research community with an interest in representing general biomedical knowledge and disease-specific knowledge, i.e., Alzheimer’s disease (Malhotra et al. 2014), to build intelligent homecare systems to assist older people with chronic diseases to live at home as long as possible (Dieng-Kuntz et al. 2006; Malhotra et al. 2014; Sharp 2017).
Examples of Health 3.0 in Action
Although not directly referred to as Health 3.0, by definition a large number of open-source, biomedical ontologies and the increasing knowledge-based systems for consumer-related activities are Health 3.0 technologies.
Biomedical ontologies. Biomedical ontologies offer the ability to structure and represent essential biomedical knowledge to drive health data integration, information retrieval, data annotation, natural language processing, and health decision support. A representative is BioPortal (URL: http://alpha.bioontology.org), a Web-based repository for biomedical ontologies, developed and owned by the National Center for Biomedical Ontology in the USA (Noy et al. 2008). BioPortal defines relationships between the ontologies and online data resources such as PubMed, ClinicalTrials.gov, and the Gene Expression Omnibus. It also supports community-based participation in the evaluation and evolution of ontology content.
Wikies in Health 3.0. The already popular Wikies may continue to flourish in Web 3.0. For example, WikiProteins (URL: http://ecoliwiki.com/colipedia/index.php/WikiProteins) has already imported data mined from several leading biomedical databases, including PubMed, UniProt, and the National Library of Medicine in the USA. Its entries integrate genetic information and scientific literature.
An ontology-based, context-aware system for smart homes: E-care@home. Smart home has long been the dream of older people wishing to age at home instead of being relocated to long-term care facilities. A substantial amount of research effort has been spent on developing smart home technologies worldwide (Alirezaie et al. 2017; Chiang and Liang 2015; Huang et al. 2016; Mitchell et al. 2014). For example, Alirezaie et al. (2017) reported a technical framework called E-care@home that consists of an IoT infrastructure that provides information organized in ontology, with clear, shared meaning that is understandable by all stakeholders, including IoT devices, end users, relatives, and health and care professionals and organizations. This has enabled semantic interpretation of events and context awareness. Based on E-care@home, a smart environment can be constructed to enable context recognition based on the activities and the events occurring in the home.
ADO: a disease ontology representing the domain knowledge specific to Alzheimer’s disease. One type of Health 3.0 technology, disease-specific ontology, can facilitate disease knowledge exchange across multiple disciplines and ontology-driven data mining to model disease mechanisms. Alzheimer’s disease ontology (ADO) is an example of a disease-specific ontology. It contains information relevant to four main biological views of Alzheimer’s disease: preclinical, clinical, etiological, and molecular/cellular mechanisms (Malhotra et al. 2014). It also includes synonyms and references. The ADO was piloted in information retrieval to demonstrate its capability to capture both established and scattered knowledge about Alzheimer’s disease existing in scientific texts.
An assisted cognition system to support therapies for people with dementia. Navarro et al. (2016) designed a Health 3.0 application – an ontology-based ambient-assisted cognitive intervention system – to support occupational therapy to address psychological and behavioral symptoms of dementia (Navarro et al. 2016). They conducted a 16-week in situ evaluation with two caregiver-care recipient dyads and found that personalization and a touch-based interface encouraged the adoption of the system, helping to reduce challenging behaviors for persons with dementia and to reduce their caregiver’s burden.
To date there is little reporting on the real-world efficacy of Health 3.0 in action. This may be attributed to a general lack of recognition of the term due to its recent uptake. By definition, any online and off-line consumer health initiative, if built on Semantic Web technology, should be labeled a Health 3.0 initiative. However, although applying ontology, a major component of Semantic Web technology, for expressing and capturing knowledge in the ambient environment, smart home researchers do not appear to recognize and accept the notion of Health 3.0 yet and certainly do not brand their R&D under the umbrella of Health 3.0.
That said, the critical challenge for Health 3.0 is to ensure the correctness of the four key types of dynamic interactions mentioned above. The proliferation and penetration of Health 3.0 into mainstream health delivery systems require a new set of health service organization and delivery mechanisms that promote the second and third types of healthcare provider-patient relationships and promote patients’ autonomy and active interaction with Health 3.0 to self-manage chronic conditions and their well-being (Gagnon and Chartier 2012). This will not come automatically without radical revolution of the healthcare organization and delivery mechanism that encourages personalized healthcare, consumer autonomy, co-responsibility, and collaboration with healthcare providers.
The vision of the Semantic Web was popularized through a Scientific American paper authored by Sir Tim Berners-Lee and his colleagues in May 2001. Although it has been widely adopted and used in the research community to build innovative intelligent systems that are often funded by government grants, it still needs to be widely adopted and used by the broader software engineering community, who are entrusted with building a diverse range of real-world applications to enrich Health 3.0.
Future Research Directions
The most effective way for the translation of new knowledge into the health delivery system to achieve personalized healthcare is to leverage the current advancement in EHR, genomic data, and artificial intelligence. Therefore, Kalra (2011) recommends establishing well-harmonized semantic resources that richly connect virtual teams and their strategies to real-time and tailored evidence to consistently embed facts, decision logic, care pathway steps, alerts, and education with components that can interact with multiple EHR systems and services. These will enable consistent comparison of a patient’s current situation with very similar patients (across millions), based on genomic data built-up to deliver personalized healthcare recommendations.
For Health 3.0 to be seamlessly integrated into the mainstream healthcare delivery system, research needs to be conducted about the healthcare organization and delivery mechanisms that promote Health 3.0. We need to understand the tangible impact of healthcare providers’ guidance, partnership, and facilitation on patients’ interaction with Health 3.0 and the impact of patients’ engagement with Health 3.0 on their healthcare autonomy, co-responsibility, and collaboration with their healthcare providers in disease management and lifestyle choice. Substantial research effort is also needed on implementation research to promote societal adoption and usage of Health 3.0 to optimize consumer’s personal experience and outcomes.
The ongoing challenge for the health service delivery systems worldwide and the advancement of internet technology have called for a leap forward from Health 2.0 to Health 3.0. Although the notion of Health 3.0 has been coined to conceptualize the developmental trend of healthcare, it is still in its infancy. Considerable reform in all the related areas is still needed for real-world implementation of Health 3.0 to achieve optimal outcomes. These include reforms in healthcare organization and delivery systems that encourage healthcare providers to actively guide, coach, and partner with healthcare consumers, consumers with willingness and capacity to take responsibility for their own health and treatment in collaboration with healthcare providers, and societal investment in Health 3.0 technologies and their integration into in-house health technologies in healthcare organizations. Therefore, realization of the vision of Health 3.0 still requires substantial concerted effort at all societal levels and community groups.
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