Development Issues of Healthcare Robots: Compassionate Communication for Older Adults with Dementia

Artificial intelligence technologies and compassion in healthcare: A systematic scoping review

Background

Advances in artificial intelligence (AI) technologies, together with the availability of big data in society, creates uncertainties about how these developments will affect healthcare systems worldwide. Compassion is essential for high-quality healthcare and research shows how prosocial caring behaviors benefit human health and societies. However, the possible association between AI technologies and compassion is under conceptualized and underexplored.

Objectives

The aim of this scoping review is to provide a comprehensive depth and a balanced perspective of the emerging topic of AI technologies and compassion, to inform future research and practice. The review questions were: How is compassion discussed in relation to AI technologies in healthcare? How are AI technologies being used to enhance compassion in healthcare? What are the gaps in current knowledge and unexplored potential? What are the key areas where AI technologies could support compassion in healthcare?

Materials and methods

A systematic scoping review following five steps of Joanna Briggs Institute methodology. Presentation of the scoping review conforms with PRISMA-ScR (Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews). Eligibility criteria were defined according to 3 concept constructs (AI technologies, compassion, healthcare) developed from the literature and informed by medical subject headings (MeSH) and key words for the electronic searches. Sources of evidence were Web of Science and PubMed databases, articles published in English language 2011-2022. Articles were screened by title/abstract using inclusion/exclusion criteria. Data extracted (author, date of publication, type of article, aim/context of healthcare, key relevant findings, country) was charted using data tables. Thematic analysis used an inductive-deductive approach to generate code categories from the review questions and the data. A multidisciplinary team assessed themes for resonance and relevance to research and practice.

Results

Searches identified 3,124 articles. A total of 197 were included after screening. The number of articles has increased over 10 years (2011, n = 1 to 2021, n = 47 and from Jan-Aug 2022 n = 35 articles). Overarching themes related to the review questions were: (1) Developments and debates (7 themes) Concerns about AI ethics, healthcare jobs, and loss of empathy; Human-centered design of AI technologies for healthcare; Optimistic speculation AI technologies will address care gaps; Interrogation of what it means to be human and to care; Recognition of future potential for patient monitoring, virtual proximity, and access to healthcare; Calls for curricula development and healthcare professional education; Implementation of AI applications to enhance health and wellbeing of the healthcare workforce. (2) How AI technologies enhance compassion (10 themes) Empathetic awareness; Empathetic response and relational behavior; Communication skills; Health coaching; Therapeutic interventions; Moral development learning; Clinical knowledge and clinical assessment; Healthcare quality assessment; Therapeutic bond and therapeutic alliance; Providing health information and advice. (3) Gaps in knowledge (4 themes) Educational effectiveness of AI-assisted learning; Patient diversity and AI technologies; Implementation of AI technologies in education and practice settings; Safety and clinical effectiveness of AI technologies. (4) Key areas for development (3 themes) Enriching education, learning and clinical practice; Extending healing spaces; Enhancing healing relationships.

Conclusion

There is an association between AI technologies and compassion in healthcare and interest in this association has grown internationally over the last decade. In a range of healthcare contexts, AI technologies are being used to enhance empathetic awareness; empathetic response and relational behavior; communication skills; health coaching; therapeutic interventions; moral development learning; clinical knowledge and clinical assessment; healthcare quality assessment; therapeutic bond and therapeutic alliance; and to provide health information and advice. The findings inform a reconceptualization of compassion as a human-AI system of intelligent caring comprising six elements: (1) Awareness of suffering (e.g., pain, distress, risk, disadvantage); (2) Understanding the suffering (significance, context, rights, responsibilities etc.); (3) Connecting with the suffering (e.g., verbal, physical, signs and symbols); (4) Making a judgment about the suffering (the need to act); (5) Responding with an intention to alleviate the suffering; (6) Attention to the effect and outcomes of the response. These elements can operate at an individual (human or machine) and collective systems level (healthcare organizations or systems) as a cyclical system to alleviate different types of suffering. New and novel approaches to human-AI intelligent caring could enrich education, learning, and clinical practice; extend healing spaces; and enhance healing relationships.

Implications

In a complex adaptive system such as healthcare, human-AI intelligent caring will need to be implemented, not as an ideology, but through strategic choices, incentives, regulation, professional education, and training, as well as through joined up thinking about human-AI intelligent caring. Research funders can encourage research and development into the topic of AI technologies and compassion as a system of human-AI intelligent caring. Educators, technologists, and health professionals can inform themselves about the system of human-AI intelligent caring.

Nursing and human-computer interaction in healthcare robots for older people: An integrative review

Objectives

This study examined the published works related to healthcare robotics for older people using the attributes of health, nursing, and the human-computer interaction framework.

Design

An integrative literature review.

Methods

A search strategy captured 55 eligible articles from databases (CINAHL, Embase, IEEE Xplore, and PubMed) and hand-searching approaches. Bibliometric and content analyses grounded on the health and nursing attributes and human-computer interaction framework were performed using MAXQDA. Finally, results were verified using critical friend feedback by a second reviewer.

Results

Most articles were from multiple authorship, published in non-nursing journals, and originating from developed economies. They primarily focused on applying healthcare robots in practice settings, physical health, and communication tasks. Using the human-computer interaction framework, it was found that older adults frequently served as the primary users while nurses, healthcare providers, and researchers functioned as secondary users and operators. Research articles focused on the usability, functionality, and acceptability of robotic systems. At the same time, theoretical papers explored the frameworks and the value of empathy and emotion in robots, human-computer interaction and nursing models and theories supporting healthcare practice, and gerontechnology. Current robotic systems are less anthropomorphic, operated through real-time direct and supervisory inputs, and mainly equipped with visual and auditory sensors and actuators with limited capability in performing health assessments.

Conclusion

Results communicate the need for technological competency among nurses, advancements in increasing healthcare robot humanness, and the importance of conscientious efforts from an interdisciplinary research team in improving robotic system usability and utility for the care of older adults.

Comparison of Subjective Facial Emotion Recognition and "Facial Emotion Recognition Based on Multi-Task Cascaded Convolutional Network Face Detection" between Patients with Schizophrenia and Healthy Participants

Patients with schizophrenia may exhibit a flat affect and poor facial expressions. This study aimed to compare subjective facial emotion recognition (FER) and FER based on multi-task cascaded convolutional network (MTCNN) face detection in 31 patients with schizophrenia (patient group) and 40 healthy participants (healthy participant group). A Pepper Robot was used to converse with the 71 aforementioned participants; these conversations were recorded on video. Subjective FER (assigned by medical experts based on video recordings) and FER based on MTCNN face detection was used to understand facial expressions during conversations. This study confirmed the discriminant accuracy of the FER based on MTCNN face detection. The analysis of the smiles of healthy participants revealed that the kappa coefficients of subjective FER (by six examiners) and FER based on MTCNN face detection concurred (κ = 0.63). The perfect agreement rate between the subjective FER (by three medical experts) and FER based on MTCNN face detection in the patient, and healthy participant groups were analyzed using Fisher's exact probability test where no significant difference was observed (p = 0.72). The validity and reliability were assessed by comparing the subjective FER and FER based on MTCNN face detection. The reliability coefficient of FER based on MTCNN face detection was low for both the patient and healthy participant groups.

Robots and Robotics in Nursing

Technological advancements have led to the use of robots as prospective partners to complement understaffing and deliver effective care to patients. This article discusses relevant concepts on robots from the perspective of nursing theories and robotics in nursing and examines the distinctions between human beings and healthcare robots as partners and robot development examples and challenges. Robotics in nursing is an interdisciplinary discipline that studies methodologies, technologies, and ethics for developing robots that support and collaborate with physicians, nurses, and other healthcare workers in practice. Robotics in nursing is geared toward learning the knowledge of robots for better nursing care, and for this purpose, it is also to propose the necessary robots and develop them in collaboration with engineers. Two points were highlighted regarding the use of robots in health care practice: issues of replacing humans because of human resource understaffing and concerns about robot capabilities to engage in nursing practice grounded in caring science. This article stresses that technology and artificial intelligence are useful and practical for patients. However, further research is required that considers what robotics in nursing means and the use of robotics in nursing.

Investigation of Methods to Create Future Multimodal Emotional Data for Robot Interactions in Patients with Schizophrenia: A Case Study

Rapid progress in humanoid robot investigations offers possibilities for improving the competencies of people with social disorders, although this improvement of humanoid robots remains unexplored for schizophrenic people. Methods for creating future multimodal emotional data for robot interactions were studied in this case study of a 40-year-old male patient with disorganized schizophrenia without comorbidities. The qualitative data included heart rate variability (HRV), video-audio recordings, and field notes. HRV, Haar cascade classifier (HCC), and Empath API© were evaluated during conversations between the patient and robot. Two expert nurses and one psychiatrist evaluated facial expressions. The research hypothesis questioned whether HRV, HCC, and Empath API© are useful for creating future multimodal emotional data about robot–patient interactions. The HRV analysis showed persistent sympathetic dominance, matching the human–robot conversational situation. The result of HCC was in agreement with that of human observation, in the case of rough consensus. In the case of observed results disagreed upon by experts, the HCC result was also different. However, emotional assessments by experts using Empath API© were also found to be inconsistent. We believe that with further investigation, a clearer identification of methods for multimodal emotional data for robot interactions can be achieved for patients with schizophrenia.

Characteristics of interactive communication between Pepper robot, patients with schizophrenia, and healthy persons

Background

Expressing enjoyment when conversing with healthcare robots is an opportunity to enhance the value of human robots with interactive capabilities. In clinical practice, it is common to find verbal dysfunctions in patients with schizophrenia. Thus, interactive communication characteristics may vary between Pepper robot, persons with schizophrenia, and healthy persons.

Objective

Two case studies aimed to describe the characteristics of interactive communications, 1) between Pepper as a healthcare robot and two patients with schizophrenia, and 2) between Pepper as a healthcare robot and two healthy persons.

Case Report

The "Intentional Observational Clinical Research Design" was used to collect data. Using audio-video technology, the conversational interactions between the four participants with the Pepper healthcare robot were recorded. Their interactions were observed, with significant events noted. After their interactions, the four participants were interviewed regarding their experience and impressions of interacting with the Pepper healthcare robot. Audio-video recordings were analyzed following the analysis and interpretation protocol, and the interview data were transcribed, analyzed, and interpreted.

Discussion

There were similarities and differences in the interactive communication characteristics between the Pepper robot and the two participants with schizophrenia and between Pepper and the two healthy participants. The similarities were experiences of human enjoyment while interacting with the Pepper robot. This enjoyment was enhanced with the expectancy of the Pepper robot as able to entertain, and possessing interactive capabilities, indicating two-way conversational abilities. However, different communicating characteristics were found between the healthy participants' impressions of the Pepper robot and the participants with schizophrenia. Healthy participants understood Pepper to be an automaton, with responses to questions often constrained and, on many occasions, displaying inaccurate gaze.

Conclusion

Pepper robot showed capabilities for effective communication pertaining to expressing enjoyment. The accuracy and appropriateness of gaze remained a critical characteristic regardless of the situation or occasion with interactions between persons with schizophrenia, and between healthy persons. It is important to consider that in the future, for effective use of healthcare robots with multiple users, improvements in the areas of the appropriateness of gaze, response time during the conversation, and entertaining functions are critically observed.

The Impact of Healthcare 4.0 on the Healthcare Service Quality: A Systematic Literature Review

Healthcare 4.0 is inspired by Industry 4.0 and its application has resulted in a paradigmatic shift in the field of healthcare. However, the impact of this digital revolution in the healthcare system on healthcare service quality is not known. The purpose of this study is to examine the impact of healthcare 4.0 on healthcare service quality. This study used the systematic literature review methodology suggested by Transfield et al. to critically examine 67 articles. The impact of healthcare 4.0 is analyzed in-depth in terms of the interpersonal, technical, environmental, and administrative aspect of healthcare service quality. This study will be useful to hospitals and other stakeholders to understand the impact of healthcare 4.0 on the service quality of health systems. Besides, this study critically analyses the existing literature and identifies research areas in this field and hence will be beneficial to researchers. Though there are few literature reviews in healthcare 4.0, this is the first study to examine the impact of Healthcare 4.0 on healthcare service quality.

Intentional Observational Clinical Research Design: Innovative Design for Complex Clinical Research Using Advanced Technology

The growing use of robots in nursing and healthcare facilities has prompted increasing research on human–robot interactions. However, specific research designs that can guide researchers to conduct rigorous investigations on human–robot interactions are limited. This paper aims to discuss the development and application of a new research design—the Intentional Observational Clinical Research Design (IOCRD). Data sources to develop the IOCRD were derived from surveyed literature of the past decade, focusing on clinical nursing research and theories relating robotics to nursing and healthcare practice. The distinction between IOCRD and other research design is the simultaneous data generation collected using advanced technological devices, for example, the wireless Bonaly-light electrocardiogram (ECG) to track heart rate variability of research subjects, robot application programs on the iPad mini to control robot speech and gestures, and Natural Language Processing programs. Even though IOCRD was developed for human–robot research, there remain vast opportunities for its use in nursing practice and healthcare. With the unique feature of simultaneous data generation and analysis, an interdisciplinary collaborative research team is strongly suggested. The IOCRD is expected to contribute guidance for researchers in conducting clinical research related to robotics in nursing and healthcare.

The Effect of Perceptions on Service Robot Usage Intention: A Survey Study in the Service Sector

The current age of artificial intelligence, along with the advent of robots, portends increased use of innovative technologies in the tourism industry, with higher levels of service innovation than in many other industries. In addition, factors such as an approaching worldwide pandemic have limited the amount of physical contact that people can have. So as a result, the use of service robots in service areas, such as tourism, has recently become controversial. In this study, accommodation customers’ perceptions of advantages and disadvantages about robots and the effect of the perceived value of their intention to use them were investigated. Within the scope of the research, data were collected from 1408 people living in various cities in Turkey through an online survey. The data were analyzed by structural equation modeling. As a result of the analyses, it was found that the perception of advantage and the perceived value affect the intention to use service robots positively and significantly. It has been determined that the perception of disadvantage affects the intention to use service robots negatively and significantly. The research results show that the accommodation companies should be innovative and rapidly transition to robotization, as in the manufacturing industry. Advanced technological innovation applications, such as service robots, will play an essential role in the revival of the tourism industry, especially during the global epidemic.