Building Artificial Intelligence in Robots and the Ethics of Healthcare Robots

#### **Chapter 7**

## Artificial Brain for the Humanoid-Nurse Robots of the Future: Integrating PsyNACS© and Artificial Intelligence

*Hirokazu Ito, Tetsuya Tanioka, Michael Joseph S. Diño, Irvin L. Ong and Rozzano C. Locsin*

#### **Abstract**

Robots in healthcare are being developed rapidly, as they offer wide-ranging medical applications and care solutions. However, it is quite challenging to develop high-quality, patient-centered, communication-efficient robots. This can be attributed to a multitude of barriers such as technology maturity, diverse healthcare practices, and humanizing innovations. In order to engineer an ideal Humanoid-Nurse Robots (HNRs), a profound integration of artificial intelligence (AI) and information system like nursing assessment databases for a better nursing care delivery model is required. As a specialized nursing database in psychiatric hospitals, the Psychiatric Nursing Assessment Classification System and Care Planning System (PsyNACS©) has been developed by Ito et al., to augment quality and safe nursing care delivery of psychiatric health services. This chapter describes the nursing landscape in Japan, PsyNACS© as a specialized nursing database, the HNRs of the future, and the future artificial brain for HNRs linking PsyNACS© with AI through deep learning and Natural Language Processing (NLP).

**Keywords:** PsyNACS©, artificial brain, humanoid-nurse robot, artificial intelligence, communication, nursing

#### **1. Introduction**

The nursing shortage in Japan has significantly increased in years [1]. Population aging coupled with a declining birthrate has greatly steered the upward demand for nursing professionals [2, 3]. In response, the Government of Japan has called for the adoption of the Internet of Things (IoT) and robots in healthcare [4, 5]. Japan's Act for the Mental Health and Welfare of Persons with Mental Disorders has been undertaken reducing chronic psychiatric hospital stay as much as possible and providing home care services. However, the nursing shortage has created can be more pressing healthcare issues in psychiatric hospitals where the length of average hospital stay is much longer in contrast with other countries [6, 7].

Psychiatric signs and symptoms provide dysfunctional evidence and pose as healthcare disadvantage to patients with mental illness [8–10]. These dysfunctions and disadvantages characterized by repeated exacerbations and remissions for patients with mental illness often follow a chronic course [11] wherein they maintain their lives in the community while being repeatedly admitted to and discharged from psychiatric hospitals [12, 13]. In addition, psychiatric nursing care situations have become more challenging as the number of people with dementia continues to increase with the population getting older.

Caring for patients with dementia is complex and requires specialized interventions [14]. In Japan, these situations influence psychiatric healthcare services therefore, psychiatric nursing practice requires early assessment during the acute phase, and effective health maintenance in the chronic phase to provide optimal nursing care for patients.

Access to health data is essential in clinical decision making. This can considerably improve interdisciplinary care and health outcomes of patients with psychiatric conditions, preventing unnecessary readmissions and unsafe discharges in psychiatric hospitals. In the absence of a functioning database, psychiatric nursing care becomes inefficient and fragmented, further creating an inadequate environment for IoT and robots in healthcare to thrive. On the whole, the current nursing landscape remains to exhibit countless unstructured challenges in attaining and sustaining quality psychiatric nursing care.

#### **2. The PsyNACS© database**

The need for a specialized nursing database for psychiatric hospitals in Japan prompted the development of the Psychiatric Nursing Assessment Classification System and Care planning System (PsyNACS©) to improve psychiatric nursing care services (Ito et al.) [15, 16]. This was a data-driven classification system of nursing assessment data for Japanese psychiatric healthcare which can be used in various patient care situations in psychiatric units.

In developing the system, a select group of experienced nurses (N = 664) working in psychiatric hospitals evaluated 211 assessment items for psychiatric nursing care derived from contemporary nursing theoretical models and frameworks. The results of the factor analysis of the final 209 assessment items generated 9 Patient Assessment Data (PAD) and 31 Cluster Assessment Data (CAD). Each PAD consisted of 2 to 5 CADs.

The PADs are simple categories for each corresponding CADs. The PADs include (a) psychological symptom and stress, (b) information about treatment, (c) function of eating and balance of water, (d) life and value, (e) vital signs and health assessment, (f) self-care, (g) social support, (h) activity, sleeping and mobility capability, and (i) Sexual function and sexual behavior.

PsyNACS© is designed to assist nurses to provide timely, effective and appropriate care for patients with mental illness. It can be a server-type, laptop-type, or web-type system. A server-type PsyNACS© installs a server in a psychiatric hospital and the laptop-type PsyNACS© can be used without an internet connection. Of interest, the web-type PsyNACS© is connected to a cloud server that enables online nursing care planning. These pathways address access to design care plans to meet the needs of the patient with mental illnesses for individualized care, including treatment, rehabilitation, and post-discharge welfare services. Since the PsyNACS© database deals with big data, it has a secure mechanism to gather healthcare information and other assessment data.

As a result, the completed database (**Figure 1**) was digitized so that nursing care plans can be accessed using a computer or laptop system.

*Artificial Brain for the Humanoid-Nurse Robots of the Future: Integrating PsyNACS©… DOI: http://dx.doi.org/10.5772/intechopen.96445*

**Figure 1.** *PsyNACS© login page.*

PysNACS© works at a lower cost as compared to hospital systems where expensive electronic medical records may not be feasible. The web-type PsyNACS© deals with aggregate patient information and health data. This calls for accurate recording and proper documentation of health information, psychiatric symptoms, and care plans. PsyNACS© is equipped with a database content filter that displays alerts for capturing inappropriate words, entries, or inputs.

Like with any medical and health records, information security and ethics remain to be the greatest concern. Both systems and risks management can be difficult tasks for nursing or any health professionals. In essence, only patients and their lawful representatives may request information. As a result, confidential personal information can be managed separately from the psychiatric nursing assessments and care plans to prevent unauthorized disclosure and leaks. PsyNACS© uses a dedicated internet line or a Virtual Private Network (VPN) to ensure information safety.

Handling patient information in healthcare settings requires additional levels of protection for privacy and confidentiality. A top the terminal management on the client end, a third-party risk management system such as antivirus software is highly recommended against data breaches. Other security features include establishing quality procedures for handling electronic information, setting identity and password management procedures for authorized access, clarifying server management procedures to oversee servers, and ensuring the security of communication paths.

PsyNACS© offers a holistic approach to psychiatric nursing assessment. It collects health information to have a better assessment of patient needs and determine the most appropriate nursing interventions. PsyNACS© is organized strategically into information blocks (**Figure 2**) for (a) common in psychiatry, (b) with dementia, (c) with complication, and (d) additional information. This is achieved by integrating the PADs as the *key areas* of psychiatric assessment (left column) and the CADs as the *subareas* with corresponding nursing assessment items (right column). Thus, patient-centered nursing care can be planned and delivered using the psychiatric assessment with 9 areas, 31 subareas, and 209 items.

The database prototype for PsyNACS© displays the recommended and relevant health information that has been entered recurrently into the system as individuallevel data. This requires active participation and utilization among professional nurses at the point of care. By weighing critical information from psychiatric nurses, it becomes possible for nurses to use the system with greater usability and functionality. The aggregate data in the database will grow eventually into big data, which can be analyzed for quality improvement.


#### **Figure 2.**

*PsyNACS© sample screen for area 1 - psychiatric symptom and stress.*

In practice PsyNACS© was evaluated at a selected psychiatric hospital in Japan. Ten nurse managers who were experts in manipulating electronic medical charts in their respective psychiatric hospital participated and answered the questionnaire. They entered patient information data using the laptop-type of PsyNACS©. Evaluative processes included operability and efficiency of the system determined through the survey questionnaire. Five of the 10 participants responded that the system was good. Four participants declared that the information input method was efficient. However, regarding time required for inputting data was found to be significantly different among individual participants. Familiarity of the system operation was the main determinant (presented at the International Conference on Ethics, Esthetics, and Empirics in Nursing, Songkhla, Thailand, July, 5–7. 2017). Integrating PsyNACS© into nursing practice will provide nurses with better access to health information that allow them to perform holistic assessment and provide quality care that is responsive to current standards and contexts of Japanese psychiatric nursing.

#### **3. Humanoid-nurse robots of the future**

The Government of Japan's direction for robots in healthcare strongly coincides with the Fourth Industrial Revolution (4IR) [17] and Society 5.0 [18] in which a massive integration of highly advanced and recognized disruptive technologies such as AI, IoT, and quantum computing is expected to flourish. Nevertheless, this is quite challenging for healthcare, more so in psychiatric nursing which is lagging behind the manufacturing and other service industries. In order to thrive, information and communication technology are crucial for using humanoid robots in healthcare area. Despite technological advancements, the maturity of existing ideologies of Humanoid-Nurse Robots (HNRs) are yet a forthcoming consideration [19].

The HNRs of the future has no single definition, morphology (form), and physiology (function). Rather than a concrete conception, the HNRs of the future is considered a product of the collective visions of nursing and healthcare leaders

#### *Artificial Brain for the Humanoid-Nurse Robots of the Future: Integrating PsyNACS©… DOI: http://dx.doi.org/10.5772/intechopen.96445*

as they reimagine the future of healthcare demands. In the context of psychiatric nursing and in older adult care in Japan, the HNRs are expected to assist and work with nurses in carrying out healthcare tasks and activities. Having a clear vision of HNRs as the supreme technological advancement in healthcare, the demands are for robots to be programmed in such a way that it can independently perform nursingrelated technical skills, and simultaneously demonstrate value-added expressions of humanness such as respect, compassion, empathy, and caring [20]. Therefore, HNRs are envisioned to be of high-quality, expressing patient-centeredness, and efficient with communication.

First, being of high-quality means that HNRs are data-driven achieved by linking PsyNACS© with HNRs. Credible health information should be captured from meaningful nurse–patient interactions. The PsyNACS© as the conversation database and along with relevant data such as electronic medical records, history and physical examination, laboratory, and radiology results can be linked to AI enhancing HNRs to acquire reliable databases regarding patients with mental illness and dementia. In addition to PsyNACS© integration with HNRs, the quality of HNRs is frequently influenced by the global proliferation of robots in healthcare, especially in hospitals, communities, and in home settings. Producing high-quality HNRs also means having data-driven policies and guidance on shared nurse–robot practices. Therefore, it becomes essential for nurses to play leadership roles in the design, implementation, and evaluation of nurse-robot partnerships [21] and eventually transform this into standards of nursing practices.

Secondly, HNRs have patient-centered designs [22, 23]. The ethical standards of human nurses are primarily grounded on the value of caring [24]. This impacts the quality of nursing care. In psychiatric nursing, nurses need to address both the physical and psychosocial needs of patients particularly those with mental illness and dementia. This poses greater challenges to HNRs which are originally designed as provider-centric in order to improve the efficiency of healthcare workers.

Patient-centered designs can be accomplished by focusing on patient needs and therapeutic conversational contents of professional nurses. Given that HNRs can be both a technological tool and a care partner, this will also entail looking into the code of ethics for nurses in Japan (Japanese Nursing Association, 2003). The current provision mentions nothing about HNRs; only focusing on collaboration among nursing and healthcare personnel. Therefore, the nurse-robot partnership should carefully consider and meet the nursing code of ethics [25].

Lastly, HNRs are capable of efficient communication. Currently designed/ developed robots usually engage in one-way communications – each time simply asking one-sided questions to attempt a dialog. Optimizing structured conversations are needed to elicit desired levels of engagement and participation. This can be achieved through the creation of a "Caring Dialogue Database" for HNRs to provide better information about the patients, and to share experiences of humanrobot interactions. Moreover, it is vital to generate a dialogical pattern that enables HNRs to demonstrate empathy particularly with people who have psychiatric illnesses [26].

The present-day advanced communication robot systems possess limited functionality in carrying conversations and keeping smooth communication pattern similar to humans, unless this system is connected to a cloud database with distinctive voice assistant services. Using a cloud database with big data capacity complicates information management and security features, increasing risks of data breach and leakage of electronic, sensitive, and confidential data. By installing data security systems, and protective features, HNRs can learn to express more sympathetic behavior over time by undergoing repeated cycles of information processing allowing for secured inputs and outputs of information through the cloud database.

#### **4. The future artificial "brain" for HNRs: associating PsyNACS© with AI systems**

What is the brain of a computer? The obligatory answer is the Central Processing Unit (CPU) that performs tons of rapid data processing operations and instructions per second [27]. This is the typical way to define the future artificial "brain" of the HNRs in layman's term. It is metaphorically straightforward to compare human beings with computers wherein human brains and computer processing units function similarly. The CPU or control system is the central nervous system, sensors as the afferent sensory system, and actuators as the efferent motor system [28]. However, this becomes quite difficult and complicated when the task is to describe robot "physiology" and features. For that reason, to successfully characterize the artificial brain of the HNRs in the future, it is critically important to understand the entity that it must emulate – *the professional nurse*.

Amisha et al. described artificial intelligence (AI) as using technology to generate a human-level cognition. In this chapter, the AI does not merely refer to the artificial "brain" of the robot, but rather it characterizes a feature that can understand human language and replicate the behaviors of a professional nurse. To achieve this, AI requires a specialized database like PsyNACS© as well as the capability to communicate verbally and nonverbally. Such ability to control, manage, and operate the HNRs is known as the AI system [29] (**Figure 3**).

HNRs should be able to establish trust and rapport with patients in a similar fashion as a professional human nurse does when fostering a nurse–patient relationship. To have a shared understanding of the patients' life experiences, the HNRs need to understand the patient's illnesses, and treatments. Like nurses, HNRs need to genuinely convey caring to patients and their families through the language of caring.

#### **Figure 3.**

*The relationship between the humanoid-nurse robot and PsyNACS©.*

#### *Artificial Brain for the Humanoid-Nurse Robots of the Future: Integrating PsyNACS©… DOI: http://dx.doi.org/10.5772/intechopen.96445*

Nurses have self-consciousness that allows them to express their emotions, particularly demonstrating tender loving care without being coached by other people.

Artificial "brains" and artificial consciousness may well be necessary features for HNRs [30] in order to demonstrate initiative and express autonomously without any human inducement or mediation. While nursing care is fundamentally a human-to-human relationship, it becomes a nonhuman-to-human relationship in the case of HNRs [31]. This raises many controversial issues and ethical concerns for patient safety which must be addressed accordingly [24].

If HNRs are to support and care for patients directly, they must hold the same level of comprehensive judgment ability and responsiveness like that of a competent professional nurse who use any of the following processes, such as theory-based nursing care practice, the utility of the traditional nursing process of assessing, diagnosing, planning, implementing, and evaluating, and clinical decision-making, critical thinking, problem-solving, and rapid response and feedback. These processes guide professional practice while emphasizing the individuality of every patient during the practice of professional nursing care. An additional level of intelligence, skillfulness and competence [19] are required in the event that HNRs are assigned to care for patients autonomously or independently.

A successful nurse–patient relationship also relies on effective communication. The future artificial "brain" of HNRs is envisioned to have the capacity to convey a smooth conversation with appropriate patient-centered responses. For HNRs to have such a feature, it entails all the essences of AI such as Natural Language Processing (NLP), neural networks, and deep learning in generating voice contents [32–34]. In addition to verbal content, HNRs need to demonstrate nonverbal communication patterns that are important aspects of effective nurse–patient communications such as eye contact, proxemics, kinesics, expressions, and tone [35–38].

**Figure 3** shows the relationship between the robot and PsyNACS© with a conceptual diagram. It may seem therefore that both verbal and nonverbal messages are the life-bloods of successful therapeutic communication in psychiatric nursing. Due to the expected physiological intricacies of the artificial brain, the design and development of HNRs of the future calls for participatory dialog and trans-professional collaboration between healthcare professionals, technology engineers, and care stakeholders. Nursing professionals can provide critical inputs with empirical value at point-of-care. In particular, nurses can contribute to the development of the artificial "brain" for HNRs by sharing their professional knowledge, clinical expertise, care competencies, and nursing documentation that contains relevant and reliable information about the patient status, care plans and interventions, and health outcomes. These information can be organized and amassed using the PsyNACS© framework and database. As a result, a natural conversation *would be* possible between HNRs and humans (e.g., patients and their families) provided that the artificial brain, PsyNACS©, and AI are well-integrated. This allows HNRs of the future to communicate efficiently and respond appropriately and accurately to patients while carefully considering the all-inclusive situation comprising the patient condition, the psychiatric database, and the healthcare environment.

Insofar as robots can be considered as 'mere' artifacts of technological advancement, our trust in and reliance on HNRs must be based on functional and ethical criteria [39]. We can always judge the worth and value of HNRs if their functionalities are approached as means to an end. This teleological approach focuses on the end-result of the HNRs' function that is, whether HNRs have been successful or not in performing tasks. Looking at the outcome itself may overlook the intention of the HNRs. Using a deontological view, we can evaluate HNRs if it is doing the right thing. This also takes into account

the goodwill behind the motives and actions of HNRs. Lastly, nursing care is a virtue-driven human experience. We cannot simply assess the HNRs solely based on its obligated and consequential programming. Our evaluation should also consider the value systems in providing quality and safe professional nursing care. As mentioned, HNRs must affirm high-quality, patient-centered, and communication-efficient features. In this light, what can we learn about the value of HNRs in the context of psychiatric nursing care? – With efficiency and wholesome appreciation of being caring entities, *HNRs are more than robots and all the more so than mere tools!* [40].

#### **5. Conclusion**

In this chapter, we described the nursing landscape in Japan, PsyNACS©, and HNRs of the future in the context of psychiatric nursing. First, considering the nursing landscape in Japan facilitates a well-defined understanding of the current nursing and healthcare situations to guide the future of psychiatric nursing. Second, a data-driven approach is needed in addressing quality and safety issues in healthcare. We demonstrated how PsyNACS© originated from nursing research, and how we translated it into practice. It allows a secured holistic psychiatric nursing assessment for better care plans and services. The quality of PsyNACS© database content can be enhanced with repeated clinical use. Third, visionary leadership aids in reimagining the future of HNRs to be high-quality, patient-centered, and communication-efficient. Fourth, the artificial "brain" for the HNRs of the future might be incorporated the PsyNACS© database and AI with NLP, neural networks, and deep learning. Collaboration between healthcare professionals, technology engineers, and care stakeholders is essential for the development of HNRs capable of both verbal and nonverbal communication. In summary, integrating PsyNACS© with AI brings HNRs to greater heights – a better quality of nursing care than today.

#### **Acknowledgements**

We thank all psychiatric nurses nationwide for their participation in the survey that led to the development of PsyNACS©. This was carried out with the support of JSPS KAKENHI Grant-in-Aid for Young Scientists B (JP16K20819) and the e-Tokushima Promotion Foundation Research Project Grant. We express our gratitude for all of your support.

#### **Conflict of interest**

The authors have no conflicts of interest directly relevant to the content of this article.

*Artificial Brain for the Humanoid-Nurse Robots of the Future: Integrating PsyNACS©… DOI: http://dx.doi.org/10.5772/intechopen.96445*

### **Author details**

Hirokazu Ito1 \*, Tetsuya Tanioka1 , Michael Joseph S. Diño2,3, Irvin L. Ong2,3 and Rozzano C. Locsin1,4

1 Tokushima University, Tokushima, Japan

2 Our Lady of Fatima University, Valenzuela City, Philippines

3 Johns Hopkins University, Baltimore, MD, USA

4 Florida Atlantic University, Christine E. Lynn College of Nursing, FL, USA

\*Address all correspondence to: h.itoh@tokushima-u.ac.jp

© 2021 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/ by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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#### **Chapter 8**

## Expectations and Ethical Dilemmas Concerning Healthcare Communication Robots in Healthcare Settings: A Nurse's Perspective

*Yuko Yasuhara*

### **Abstract**

This chapter describes expectations and ethical dilemmas concerning healthcare communication robots (HCRs) from a nurse's perspective. Ethical dilemmas in nursing settings are wide-ranging. When HCRs are introduced to long-term facilities and hospitals for patient communication, new kinds of ethical dilemmas may arise. Using interviews with healthcare providers, I examined the potential ethical dilemmas concerning the development and introduction of HCRs that may interact with older adults. This analysis was based on four primary issues from the nurses' perspective. Since HCRs will be used in healthcare settings, it is important to protect patient rights and maintain their safety. To this end, discussion and collaboration with an interdisciplinary team is crucial to the process of developing these robots for use among patients.

**Keywords:** healthcare communication robots, ethical dilemmas, nurse's perspective

### **1. Introduction**

Japan's declining birthrate and aging population are becoming increasingly serious issues. Indeed, the shrinkage of the working population continues unabated [1]. The Ministry of Health, Labour and Welfare [2] reported a future shortage of anywhere from 60,000 to 270,000 nurses in 2025. This shortage might make it difficult to provide sufficient patient care, especially for older adults who need long-term care [3].

Beyond this, as of early November 2020, coronavirus disease 2019 (COVID-19) became a major threat to global public health. Globally, the number of patients with COVID-19 is approximately 52 million [4]. Notably, COVID-19 is caused by the SARS-CoV-2 virus, which spreads among people, mainly when an infected person is in close contact with others [5]. Significantly, many COVID-19 clusters have been reported in clinical settings, including long-term facilities.

In Japan, the Ministry of Health, Labor and Welfare [6] recommends that people employ basic strategies to prevent the spread of infectious diseases, including COVID-19. These include hand washing, proper cough etiquette, wearing a mask,

and avoiding group gatherings in poorly ventilated spaces. Although potential vaccines are under development, it remains necessary to make lifestyle changes that extend to human interaction, recognizing the possibility that new infectious diseases may gain prevalence in the future.

Robots are attracting attention as a countermeasure for such serious situations. Of the various forms of human interaction, communication with others is important as it helps improve the quality of life (QOL) and sociality of older adults and patients with dementia. Accordingly, healthcare communication robots (HCRs) have the potential to support the needs of patient dialog as an alternative to healthcare providers, thereby preventing infections and addressing staff shortage situations.

Using HCRs for patient care is a collaborative process that requires not only engineers but also healthcare providers, such as nurses, who have a mandate to protect patient rights and maintain safety. Indeed, it is necessary to consider potential issues that may arise from this development. Thus, this paper discusses expectations and ethical dilemmas in relation to HCRs from the perspective of nurses.

#### **2. Expectations from healthcare communication robots**

Communication with others is important because it is satisfying and fosters a sense of connection. Especially, conversation with others achieves mutual understanding through shared experiences and feelings. However, in Japan, community relationship networks are becoming degraded by the progressively aging society and the trend of nuclear families, which have become serious local problems. Particularly among older adults who have lived alone or had physical functional disorder, social activity and conversation with others tend to decrease.

Notably, long-term facilities have seen a rise in dementia patients, and the behavioral and psychological symptoms of dementia (BPSD) may cause irritability and restlessness among patients [7]. When nurses care for older adults and patients with dementia, it is important that they take time to listen to them to provide appropriate, high-quality care in a way that suits the patient [8].

However, the staffing of nurses in long-term facilities and nursing homes for older adults is lower than in acute care hospitals [9]. Due to this shortage of healthcare providers, it might be burdensome for staff to take sufficient time for dialog with older adults [10, 11].

Clearly, the quality of care for older adults may be suffering because of labor shortages, especially in long-term care settings. This quality of care may be expected to improve when healthcare workers have HCRs as partners. Moreover, HCRs may also provide patients with the opportunity to talk, even in situations where an infectious disease such as COVID-19 is concerned.

The Japanese government has already supported the introduction of HCRs to facilities for the elderly (such as nursing homes) as well as healthcare facilities [12] and hospitals [13]. While HCRs are still being developed and introduced in certain facilities, there are no HCRs specialized for older adults and patients with dementia [14]. Hence, it is necessary to improve the application that enables dialog with members of these demographics and to enhance the safety and features of the robots [15].

The development of HCRs capable of dialog and therapeutic communication is a future goal. Here, "dialog" is not just a conversation, but the recognition and respect for each other's values and establishing a relationship of trust.

This speaks to the larger need for the development of HCRs that can interact with the elderly, increase conversation opportunities for them, satisfy their desire *Expectations and Ethical Dilemmas Concerning Healthcare Communication Robots… DOI: http://dx.doi.org/10.5772/intechopen.96396*

for approval, maintain their sociality and sense of purpose, and improve their QOL. Furthermore, by collecting information from the cloud database of these robots, healthcare providers may be able to determine whether urgent or immediate care is necessary, allowing them to listen to the patients more intensely.

The acute care field is marked by the responsibility to care for patients suffering from threatening infectious diseases such as COVID-19. The risk of infection is very high for medical staff [16], who must find a way to take care of patients within the boundaries of time constraints, while also striving to prevent getting infected. Unsurprisingly, most medical staff find it difficult to take enough time to listen to patients' feelings, particularly when they are fighting the fear of COVID-19 infection [17, 18]. Thus, patients with COVID-19 may lose the opportunity to express themselves because they have limited time to talk to their medical staff and limited visits with family and friends.

Traditional (human) nurses are accustomed to listening to a patient's voice. However, in an emergency, HCRs may be able to note a patient's anxiety and complaints and provide them with appropriate care in response. If the HCR can be linked with information from thermography and electronic medical records, it will also be possible to observe simple physical conditions among patients. Thus, the HCR may also serve as an alternative to care supporters for people who have been in shelters for long periods due to large earthquakes, etc.

#### **3. Ethics required of nurses**

As recent years have seen the rapid development of robots and artificial intelligence (AI), ethical codes and guidelines have been issued by related academic societies largely in the engineering field [19, 20]. Ethical studies concerning AI and robots are also underway. UK-RAS network describes that the ethical concerns raised by robotics and autonomous systems (RAS) depend on their capabilities and domain of usage of Robotics, there are ethical issues such as Bias, Deception, Employment, Opacity, Safety, Oversight, and Privacy [21]. Of course, ethics are crucial to healthcare because healthcare workers must recognize dilemmas: using good judgment to make decisions informed by their values but also governed by the law.

A nurse, a type of healthcare provider, is a person who engages in providing care to persons with injuries and/or illnesses, and/or postpartum women, and/or assists in the provision of medical treatment under the license of the Ministry of Health, Labour and Welfare (Article 5 of the Act on Public Health Nurses, Midwives, and Nurses). Based on the Nursing Code of Ethics of the International Council of Nurses (ICN) [22], and the Japanese Nursing Association (JNA) [23], nurses are required to provide care while respecting human life, dignity, and rights according to the law.

However, just as patients are unique and vary in age and condition, nurses have their own cultural, religious, moral, and professional values. Thus, there are often conflicting values, disagreements, and ethical conflicts in nursing settings.

Ethical dilemmas in nursing settings are far-reaching. From time to time, nurses make ethical decisions by taking a variety of information into account to determine the best choice for the patient. Nurses can take appropriate actions when faced with an ethical dilemma by understanding and applying ethical guidelines such as the American Nurses Association's Code of Ethics [24], the ICN Code of Ethics for Nurses [22], and the JNA Code of Ethics [23].

In Japan, decisions about ethical dilemmas are informed by the six principles of ethics (Beneficence, Non-maleficence, Autonomy, Veracity, Justice, and Fidelity)


**Table 1.**

*Six principles of ethics [25–27].*

(**Table 1**) [25, 26]. These principles are familiar to nurses. Even after making ethical decisions, nurses reflect on those decisions and strive to increase their ethical sensitivity daily.

When HCRs are introduced to long-term facilities and hospitals, different ethical dilemmas might occur.

If the HCRs, in the near future, can use dialog to make autonomous decisions regarding patients, and serve to replace a human nurse, relevant ethical discussions must precede this change. For instance, one would logically consider the questions of whether HCRs can have a sense of ethics like human nurses, and whether the former can make ethical decisions in the midst of ethical conflicts within nursing settings.

#### **4. A nurse's perspective on ethical dilemmas regarding healthcare communication robots**

Our research currently uses the humanoid robot, Pepper (SoftBank Robotics Corp.) [28], in a long-term facility to develop an application for healthcare robots that can communicate with older adults based on principles of care. It also seeks to evaluate a program that can be run in a clinical context (developed by the Xing Company). However, in the implementation of this strategy, the communication function of Pepper's application has proven deficient.

It is important to understand the present HCRs' competency as well as other factors that may enhance this application, making it suitable for use among older adults. To explore HCR-related issues in healthcare settings, we interviewed five healthcare providers (nurses, caregivers, and physiotherapists) at three facilities about current usage issues with Pepper. From these results, I examined ethical dilemmas from the nurse's perspective concerning the development and introduction of HCRs that can interact with older adults. This analysis was based on four issues: burden on staff and insufficient support system, inadequate communication function, leakage of personal information and violation of right to privacy, and guaranteeing the safety and security of HCRs.

#### **4.1 Burden on staff and insufficient support system**

The complexity of the robot's operation, the ambiguity of the HCR support system, and the burden of preparation and cleanup of HCRs are some of the issues faced by the staff while working with HCRs. Pepper weighs approximately 30 kg (around 66 lbs.), stands 120 cm (approximately 47 inches) tall [28], and requires extra staff to prepare it for use and clean it. In addition, there are other issues related to its operational complexity and unclear support system (e.g., where to

#### *Expectations and Ethical Dilemmas Concerning Healthcare Communication Robots… DOI: http://dx.doi.org/10.5772/intechopen.96396*

check when the robot freezes). These issues sometimes occur due to the application's up-data. In many cases, a specific healthcare staff member accustomed to handling such equipment is in charge of making the introduction, placing additional burden on that staff member. At such times, staff support is required to facilitate interactions and conversations between humans and robots [29].

When HCRs are used in healthcare settings, it is important to avoid increasing the human burden and preventing the traditional nurse from being deprived of time to care for the patient. This is related to the ethical principle of justice. Nurses must decide the just or fair allocation of healthcare resources [25, 26]. With the introduction of robots, the principles of beneficence (providing good nursing to all patients), non-maleficence (avoiding harm caused from using HCRs), and justice (providing proper and fair nursing to all patients) should not come at the cost of staff conflict. Undue burden placed upon nurses, such as the aforementioned HCR handling and use requirements, may incline nurses to put an end to the introduction of robots in healthcare settings as they cannot provide adequate care and ensure the patient's safety. Indeed, convenience (which includes appropriate sizing) and generous support are key for HCR use. It is also necessary to have functions that can be used by medical professionals who are not well versed in robotics and/or engineering.

#### **4.2 Inadequate communication function**

A human nurse naturally changes the manner (speed, volume, delivery, tone) and content of their speech depending on the patient, the nurse's personal experience, and various other factors. Conversely, the current HCRs cannot change how they talk to patients. Thus, older adults and patients with dementia may give up the conversation, feel discouraged, and/or experience negative emotions because the timing of HCRs' utterances and the content of the response may be insufficient and the conversations may be unengaging. This has implications for the ethical principle of non-maleficence.

The challenge here is to set the goals for the HCRs' dialog function to include the examination of word choice (including the determination of inappropriate words). Clearly, the dialog function will rapidly improve in the future. However, traditional nurses are currently better placed to provide care to patients based on nursing ethics and while exercising professional responsibility.

Even during the clinical trials for HCR development, nurses must protect patients' rights. Patients should not be harmed; they should not experience negative feelings or feel discouraged by HCRs (the principle of non-maleficence). Nurses should ensure that patients receive the best care from HCRs and human nurses (the principle of beneficence). Furthermore, it is particularly important to solicit patients' opinions concerning their willingness or desire to interact with the HCRs (principle of autonomy); they should be permitted the personal liberty to determine their own decisions on whether to receive care from HCRs [25, 26]. Nurses give top priority to the safety of the subject and thereby play an advocacy role. Therefore, if patient rights and their ethical principles are violated, nurses may need to halt the promotion of robot development.

#### **4.3 Leakage of personal information and violation of right to privacy**

The third issue involves the collection of patient information stored in the cloud server or body of HCRs, and how this information is managed. Indeed, HCRs need to store information to a cloud server for improved functioning. A cloud server allows for information input from various sources, along with simultaneous

compilation and analysis [30]. This is significant, as there is a lot of information in the dialog between patients and HCRs.

The guidelines regarding AI and robots have included effective policies such as protection and promotion of human rights, safety, and privacy [19, 20, 31]. Nevertheless, in the near future, when HCRs use the cloud server to store big data collected from their patients, an information leakage accident may occur [32]. This issue could, for instance, arise due to some malfunction during the development stage.

The right to privacy does not have a legal basis in Japan. However, the right to privacy is recognized under the law of precedent as part of the pursuit of happiness referred to in Article 13 of the Constitution. In addition, personal information, in principle, cannot be provided to a third party (Article 23), except in cases where the allowance is based on laws and regulations (Article 23–1).

Nurses also have a duty to protect patients' privacy as a component of patient care (Article 42–2 of the Act on Public Health Nurses, Midwives, and Nurses). As stated in the code of nurse ethics, "Nurses should honor confidentiality and strive for the protection of personal information, while using appropriate discretion in the sharing of this information" [32]. Hence, it is important to safeguard against personal information leakage from HCRs or iCloud servers (the principles of fidelity, and non-maleficence).

#### **4.4 Guaranteeing the security/safety of healthcare communication robots**

The fourth issue is the need to ensure the safety of interactive robots. In healthcare settings, there are hazardous things that might result in daily medical accidents or incidents. A medical accident involving a nurse may happen while providing nursing care or while assisting medical treatment that involves medical interventions [33]. Healthcare institutions continue to improve their policies and framework to secure organization-wide safety [34]. Nurses consistently make patient safety a top priority (the principles of non-maleficence: avoiding harm caused by HCRs, and beneficence: providing better nursing to all patients). This consideration entails predicting potentially dangerous patient behavior and performing other forms of safety and risk management (the principle of non-maleficence).

Presently, there are no reported medical accidents due to the use of HCRs. Unless there is a guarantee that accidents due to patient falls or contact will not occur, and that the safety of nurses and medical staff will be ensured, the introduction of HCRs should not be viewed passively.

For instance, we must consider whether HCRs that can interact with older adults and patients with dementia need a self-propelled function and/or humanoid figures, and whether these things would enhance patient safety. Moreover, different cases must be studied along with the safety-related responsibilities they present.

#### **5. Conclusion**

This chapter discusses expectations and ethical dilemmas concerning the use of HCRs that will interact with patients in medical and welfare settings in the future. These considerations have been made from the nurses' perspective.

Conversation with others is important to human beings. However, appropriate reactions and responses are complex, not just for HCRs, but also for traditional nurses. This means that, HCRs require improved functions, including specifications concerning appropriate listening practices, conversation, behavior, etc.

*Expectations and Ethical Dilemmas Concerning Healthcare Communication Robots… DOI: http://dx.doi.org/10.5772/intechopen.96396*

Furthermore, nurses must continue to protect the rights and safety of patients in all instances and at all times. Thus, HCRs should not be allowed to infringe on these principles in healthcare settings.

In the future, HCRs may serve as patient interlocutors. Their conversation program may include AI with an interactive or transactive dialog function and the capacity to make decisions concerning ethical conflicts. To this end, discussion and collaboration with an interdisciplinary team is crucial to the process of developing these robots for use among patients.

#### **Acknowledgements**

This work was partially supported by JSPS KAKENHI Grant Number JP19K10735. Part of this work was presented to the Japan Society of Mechanical Engineers in 2020.

#### **Conflicts of interest**

The authors declare no conflicts of interest.

#### **Author details**

Yuko Yasuhara Department of Nursing Outcome Management, Institute of Biomedical Sciences, Tokushima University, Graduate School, Tokushima, Japan

\*Address all correspondence to: yaushara@tokushima-u.ac.jp

© 2021 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/ by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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*Edited by Kazuyuki Matsumoto*

This book deals with intelligent information processing systems related to natural language processing, text mining, web information processing, and nursing and caring robot technologies. It introduces the latest trends and past research results of researchers in a wide range of fields related to knowledge information processing, which is one of the ultimate goals of information processing technology and is necessary for making artificial brains useful in our society.

Published in London, UK © 2021 IntechOpen © yasinemir / iStock

Information Systems - Intelligent Information Processing Systems, Natural Language Processing, Affective

Computing and Artificial Intelligence, and an Attempt to Build a Conversational Nursing Robot

Information Systems

Intelligent Information Processing Systems,

Natural Language Processing, Affective

Computing and Artificial Intelligence,

and an Attempt to Build a Conversational

Nursing Robot

*Edited by Kazuyuki Matsumoto*