**3.4 Results**

Health systems based on primary care in the digital era are turning toward collaboration and the generation of complementary service partnerships that make it possible to obtain many of the benefits of economies of scale through the segmental participation of each actor and health service provider [42]. These value chains that are being built around the current installed capacity and the capacities of the (human) medical teams seek multichannel strategies to offer complementary services. Among these strategies are those that allow systematized service channels such as chatbots from which it has been observed that customer service advantages are obtained.

Public Health application chatbots across many countries and languages other than English were used to reach millions of citizens and all relevant institutions, in order to deliver education of citizens, surveillance and detection of contacts, risk assessment, and dissemination of information, allowing the organization of health systems while promoting cooperation, community, involvement, and accountability of citizens through collective actions [15, 37–39]. Healthcare Evolution, which includes Health 4.0 through telemedicine and artificial intelligence, is emerging toward customization and models based on value, which are connected by integrity into the business models that accompany people throughout life, by customization of healthcare, based on the optimization of determinants by reducing the follow-up effects of different health conditions [7].

The dimensions such as education, economy, income, finance, food security, communications and transport infrastructure, assurance, access to health services allow to investigate the social determinants of health that make up the context of life of people. This health information from the social context improves the personalization toward the person, by combining these data with the medical and family records, to add the self-care and personalized management, the PHC system's strategies to chronic disease monitoring, and provision of medical and assistance services [9]. As part of cognitive services, expert systems for healthcare were explored in the Scopus database. We obtained 1138 results that are observed in the cluster in the **Figure 1**. Green color presents various topics related to decision making, decision support, algorithms, data mining, machine learning, deep learning, and information classification.

#### **Figure 1.**

*Visualization by VOSviewer software of word co-occurrence network built using words in titles and abstracts of documents on healthcare expert system field.*

Likewise, in the cluster in the color blue, a set of health conditions is presented where the appearance of research related to the SARS-COV 2 virus (COVID-19) is observed and where topics on mental health, anxiety, stress, and depression also appear. In red, public health issues that address different aspects of public healthcare policies are observed, going through issues such as government, financing, budget, participation, inequities in health and access to health, and causes of death. In turquoise, economic topics appear under labels such as economic models, cost-benefit analysis, costs, disease burden, and drug costs. In olive green, the risk themes appear, where risk factors, evidence-based medicine, patient follow-up protocols, patient safety, complications, and hospitalization are displayed. In brown, the nodes with contents linked to existing soft skills in hospital management are presented, such as perception, quality of healthcare, interpersonal and communication skills, and internal consistency. Finally, in pink, there are some small nodes representing studies on issues related to mental health, psychology, and their conditions such as mental illnesses and disorders.

Aforesaid, once again, a close link is observed between the topics where there is no dominant approach that segments the cluster based on a particular research topic. In the same way, for this image, it is interesting to observe that although machine learning and deep learning appear, there are no nodes referring to artificial intelligence. **Table 1** shows the results for PHC chatbot search in Scopus and Science Direct databases. The research topic is still a new research direction, contrasting to the big healthcare apps developed in recent years and available on apps marketplaces.

Weobot, as a self-care expert, trained and tested approaches as cognitive behavioral therapy (CBT), mindfulness, and dialectical behavior therapy (DBT), have reached a significant awareness for the accompaniment of the substance-use


*A State-of-the Art Survey on Chatbots Technology Developments and Applications in Primary… DOI: http://dx.doi.org/10.5772/intechopen.110847*

#### **Table 1.**

*Results for PHC chatbot search in Scopus and science direct databases.*

disorders; nevertheless, there are a wide range of tests to analyze performance to generate advances in drug/alcohol use [3]. IBM's Watson Cloud Services has developed an app that allows chatbots for different applications, including assistants for learning the treatment process in radiotherapy for cancer patients, genomics, measurement of intellectual disability in children, support for depression episodes in older adults, and medical imaging [47].

Likewise, the use of information and communication technologies that enable quick, simple, clear, and unambiguous access to health information also contributes to the quality of health services. But the focus of public health is very different from the individualized practice of clinical medicine, and as such, public health values and ethics have several justifiable challenges that differ from medical or bioethical ones. Public health is aimed at the population, not individuals, and because of its nature, it is interested in public good [15]. Specific collective health and public health applications for large populations could be observed in many governments, civil society, and international organizations such as WHO. To provide verified information, updated

news and reports were provided on pandemic as well as details of symptoms and measures to discount public health systems [15, 39].

#### **3.5 ChatGPT**

As discussed earlier, NLP facilitates the interaction of human language with computer systems, so with the release of Generative Pre-trained Transformer 3 (GPT-3), the most recent release version of a language model that uses deep learning to generate text similar to human natural language, it has been trained using large datasets [48]. The NLP systems have evolved from single-layer representations of neural networks using word vectors fed to task-specific architectures; later, multilayered recurrent neural networks (RNNs) were used to add context to achieve better representations, and now pre-trained recurrent or transformer language models have brought great progress in tasks such as reading comprehension, answering questions, and textual detailing, among others. This type of language model has the limitation that even though it can be adapted to different tasks, it requires datasets for specific tasks in order to achieve refinement in those tasks [17]. This means that the almost human responses, the ease of use, and the friendly interaction have a weak side on the training set, which since training with Internet content incorporate deviations from the same information, such as gender, racial, and geopolitical biases [33]. However, the potential for use extends to many of the preventive medicine and PHC applications, especially in the management of electronic clinical records where a correct incorporation of this technology is visualized if said technology is fine-tuned by health professionals that restrain risks in its use.

### **4. Challenges and trade-offs for healthcare**

The capabilities that chatbots will have to favorably influence health work, using the power of computing and big data analysis, which will be exploited by health professionals in ways that are yet to be discovered. However, before the maturation and daily exploitation of this technology arrives, the road will have to go through multiple challenges that are experienced daily in health systems and health establishments, where beyond linguistic accuracy, usefulness, updating of clinical knowledge, the accuracy of medical knowledge, clinical responsibility, the domain of the health condition, the ethics of training and evaluation of the algorithm, and the acceptability of the user, among many others, must be integrated into the problems of organization, budget, culture, generation gap, old infrastructure, telecommunications architecture, financing models, and technologies for healthcare.

Although this technology can structurally change the way of delivering health services, the applications for the delivery of services within hospitals are perceived as much more limited. Hospital procedures, clinical practices, and protocols, due to their complexity and real dynamism, especially in highly specialized fields, place chatbots and NPL technology in the role of support.

However, for the PHC, the range of challenges is wide and almost as large as the universe of application possibilities, which are unique in their forced adaptation and integration to different health systems. In the short and medium term, the challenges for chatbots involve fine-tuning technology, the social dimension of the paradigm shift and evolution of tools, regulations, information management, privacy, data collection mechanisms, and so on. In the case of information management, the

*A State-of-the Art Survey on Chatbots Technology Developments and Applications in Primary… DOI: http://dx.doi.org/10.5772/intechopen.110847*

information of people and its interoperability with information systems, or medical records are examples of the challenges. The restrictions imposed by the COVID pandemic generated new adaptations of conventional therapies toward digital approaches [14].

Some studies have begun to be carried out for other disorders such as those related to gambling, smoking, sex, Internet, and mobile phone, which manifest compulsive behaviors and will be a subject of deep reflection to review the limits of the use of natural language in healthcare chatbots. However, the evidence indicates that chatbots can take on specific steps within an addiction treatment process and that they should be accompanied by the help of an expert, a peer, or a support group that contextualizes the activities within an addiction program recovery [3]. In this way, it is important to point out that the expected benefits contrast with the less-developed sides of the technology related to design, architecture, the opacity of its programming in the internal layers of the RNNs, privacy, the anonymity of people, honesty in communication, or understanding behavior disorders that lead to irrational actions or even self-deception. These deficiencies have been observed in the responses of the most popular open-domain chatbots and dialogue agents such as Siri, Alexa, Bixby, and ChatGPT, which provide counterintuitive output to user questions, such as mishandling simulated patients with suicidal ideation or providing addresses of marijuana dealers in response to questions about how to treat marijuana addiction [3].

In the same way, even with the increase in the use of chatbots, the evaluation of their effectiveness and feasibility are incipient and it requires more evidence, paying special attention to the methodology used to validate [12]. Similarly, the effectiveness of the soft aspects related to communication competence, and ability to understand users are difficult to evaluate and determinants for their safe adoption [18]. The effectiveness aspects imply the need to pay more attention and effort to include measures that ensure the safety of users, people, and patients, especially in diagnostic work and medical treatment. This complexity is also presented in the privacy and security of the data, the medical control and the authentication of the information obtained by the chatbots. Some regionalized studies showed that these chatbots and apps present from medical disclaimers, HIPAA compliant, child online privacy, and protection act [2]. This scalability characteristic that can bring so many benefits in the prevention promotion that characterizes the PHC, in the collective and population contexts, implies the inclusion of adaptation measures of health messages and prevention campaigns to cultural, educational, social, and economic contexts of each population, including the practices of native peoples [15].

### **5. Future directions**

In recent years, technological advances in the health area have been oriented toward the incorporation of artificial intelligence, telemedicine, and automated monitoring of physiological signals as enablers of patient-centered medicine, the creation of value in health services, and the change toward a culture of prevention through digital well-being. Studies by leading consultants anticipate that chatbots will be used as a first-access channel to help navigate all the options available in the health system, leading people toward virtual solutions or traditional services [42].

The possibilities of chatbots for health will be the person-to-person interfaces of new health technologies, from applications focused on health and well-being and wearable IOT devices to monitoring physiological signals, assisted living, digital

mental health therapies, social robotics for nursing care [49], personalized drugs and genomic medicine, and the supply chain around the health and well-being needs of people. As the chatbots in the health domain are increasingly used to enable interaction with humans on an emotional level, for example, to comfort and entertain older people, lonely people, or those with dementia based on cognitive services and intelligence, new ethical challenges also arise to review, such as the need to explore new governance models to guarantee principles such as the *welfare principle*, which postulates artificial intelligence systems (AIS) must, above all, allow the growth of the well-being of all [1, 50]. In this direction, artificial intelligence (AI) is beginning to occupy a central place in the design of new therapies and treatments throughout the different dimensions of healthcare. These capabilities and the impact of cognitive computing will be reflected in the outcome of medical care, well-being, and medicine in the healthcare domain [51]. The personalized healthcare services are transforming the healthcare sector toward the integration of recent technological developments under new value-based care models to improve the efficiency of traditional healthcare systems. Technological advances in health have been oriented in recent years toward the incorporation of artificial intelligence, telemedicine, and automated monitoring of physiological signals, and recent studies have focused on integration around the patient with personalization and digital well-being.

Chatbots will be a strong component in the ecosystem of technologies surrounding the patient in this so-called personalized medicine since they are expected to be used as user interface for all these developments, which involves the use of new AI, Internet of Things (IOT), and genomic technologies to promote participatory, personalized, and participative preventive health where care is designed around people and not a place. This behavioral shift from healthcare to healthy aging will require more efficient and productive public health through the use of new generations of communication technology. In addition, health systems and public health needs will drive a shift in hospitals in the future to become a continuum care facilitator and to mentor people's

**Figure 2.** *Health system recent tendencies.*

#### *A State-of-the Art Survey on Chatbots Technology Developments and Applications in Primary… DOI: http://dx.doi.org/10.5772/intechopen.110847*

healthy habits. Infrastructure adaptations for holistic health will be pushed by the Internet of Things for health as an enabler of value-based care models (**Figure 2**).

The new pharmaceutical developments toward personalized medicine based on AI are expected to bring personalized medicine closer to patients by articulating integrated healthcare supply chains created around patient experience [42]. Each of these evolutions produced in each health system component will transform healthcare delivery, by forming next-generation integrated healthcare service delivery networks as ecosystems of complementary collaborations around people's well-being. These technologies will allow people to be accompanied through the decisions inherent in each stage of life and throughout the phases of each health condition. Many actions to improve health need to be conducted by other economic sectors and be technology based. Most of the so called *social determinants of health* are beyond the scope of action of health systems; however, health systems need to be prepared to evolve in order to become resilient to social changes, epidemiological shifts, or emergency situations, by proactively detecting early signs of epidemics and be prepared to act early in response to surges in demand for services, which represents a paramount challenge to be faced by disarticulated health service providers. As suggested in Ref. [52], Massive Internet Of Things (MIoT) has been conceived as a viable future scenario to face infectious deseases, especially if combined to other technologies like Blockchain for data privacy and access issues and federated learning. This is conceived as a11 distributed interactive artificial intelligence paradigm, proposed as a solution to single database or big data sets, since it relies on the sharing of machine learning models, instead of the raw data itself, as one of the puzzle pieces of the intelligent healthcare.

Another dimension of this complexity is observed in the training of deep learning models that require large amounts of data, distributed among different institutions and owners, and that, because they contain sensitive medical data, cannot be integrated into a single database in the cloud. For these, new approaches are being explored, such as Federated Learning, a collaborative artificial intelligence technology where information obtained by millions of devices is not shared, but only the trained models [52].

### **6. General conclusion**

Healthbots are potentially transformative in centering care around the user; however, they are in a nascent state of development and require further research on development, automation, and adoption for a population-level health impact [2]. Simple and task-oriented agents will represent a manageable channel for adoption in PHC, for the diagnosis of low-complexity diseases, high stigma, or in contexts where there are few health resources, increasing digital literacy and access. Current trends indicate that chatbots are a mechanism capable of engaging people in healthcare, which allow many interventions to be focused on patients and people; however, there is a lack of a clear regulatory framework for such health interventions, as well as a general opportunity for service providers' active engagement. Similarly, applications that are based on AI should incorporate ways to monitor the measures taken by programmers to ensure the ethical, technical and clinical, and population quality of chatbots along with patient safety in each functionality. Models based on GPT and deep learning will have to mature and refine their responses and the quality of medical information and update their practices for more complex tasks to those searched in the PHC.

Health chatbots have a maturation period ahead, where the development methodologies applied to each use case are standardized. Meanwhile, for primary healthcare, those chatbots that manage to integrate with users, patients, health professionals, and service providers will be the ones that will have an impact on the systematization of repetitive PHC tasks, in which a lot of health systems' time and resources are invested nowadays.

The benefits will reach beyond the limits of health establishments and will go to other applications that, focused on improving the experience of people and patients, promote well-being over disease. In this way, they will impact the continuum of care, prevention, promotion, foresight, and personalization of medicine, together with intelligent collective public health work in real time. What will be seen soon in the healthcare domain for chatbots will be applications that include interaction on social networks, triage of patient symptoms, support and counseling before and after the clinical encounter, and as sources of information and support in organizing tasks, process management, appointments, files, medicines, and supply chain, among many other tasks. However, these benefits could be achieved mainly as a complement to a digital approach for healthcare providers, to aid health professionals and improve doctor-patient communication for low-severity conditions. This approach can be led by primary-care services adopters, whose interdisciplinary teams can jump the adoption barriers linked to culture change and technology transfer.

Finally, the challenge will be to assess the best fit for this technology within healthcare systems' settings, and PHC public policies, and how to manage the best approach to incorporate the daily profiles of health personnel who do not use technology, especially in countries with a lower level of development, which run the risk of not being able to integrate the value base that makes this technology possible, limiting themselves to becoming end users or even staying completely outside the new paradigm.
