Health Care Robotics: Qualitative Exploration of Key Challenges and Future Directions

Cardiac myxomas: causes, presentations, diagnosis, and management

Cardiac myxomas (CM) are one of the most common benign tumors which are typical in adults with a yearly incidence of 0.5-1 case per million individuals. This review article includes discussions based on existing literature on the role of interleukin interactions in the pathophysiology of cardiac myxoma which can lead to embolic complications, aneurysms, and CNS involvement. The objective of this narrative review was to study the variable clinical presentations of cardiac myxoma, its detection and diagnosis involving multiple modalities like genetic and hematological testing, echocardiography, CT, and MRI, of which transoesophageal echocardiogram shows excellent precision with a 90% to 96% accuracy in diagnosing CM. Individuals with the Carney complex are prone to such neoplasia. Cardiac myxomas are challenging to diagnose due to the ambiguity of their differential with thrombi. Myxomas can also be diagnosed by tumor markers like interleukin-6 and endothelial growth factors. The management of CM includes surgical excision like median sternotomy and robotic minimally invasive surgery. The use of robotic surgery in CM increased from 1.8% in 2012 to 15.1% in 2018. Tumor recurrences are uncommon but can occur due to inadequate surgical resection.

Acceptance of a robotic system for nursing care: a cross-sectional survey with professional nurses, care recipients and relatives

BACKGROUND

The end-users' acceptance is a core concept in the development, implementation and evaluation of new systems like robotic systems in daily nursing practice. So far, studies have shown various findings concerning the acceptance of systems that are intended to assist people with support or care needs. Not much has been reported on the acceptance of robots that provide direct physical assistance to nurses in bedside care. Therefore, this study aimed to investigate the acceptance along with ethical implications of the prototype of an assistive robotic arm aiming to support nurses in bedside care, from the perspective of nurses, care recipients and their relatives.

METHODS

A cross-sectional survey design was applied at an early stage in the technological development of the system. Professional nurses, care recipients and relatives were recruited from a university hospital and a nursing home in Germany. The questionnaire was handed out following either a video or a live demonstration of the lab prototype and a subsequent one-to-one follow-up discussion. Data analysis was performed descriptively.

RESULTS

A total of 67 participants took part in the study. The rejection of specified ethical concerns across all the respondents was 77%. For items related to both perceived usefulness and intention to use, 75% of ratings across all the respondents were positive. In the follow-up discussions, the participants showed interest and openness toward the prototype, although there were varying opinions on aspects such as size, appearance, velocity, and potential impact on workload.

CONCLUSIONS

Regarding the current state of development, the acceptance among the participants was high, and ethical concerns were relatively minor. Moving forward, it would be beneficial to explore the acceptance in further developmental stages of the system, particularly when the usability is tested.

Application of artificial intelligence (AI) to control COVID-19 pandemic: Current status and future prospects

The impact of the coronavirus disease 2019 (COVID-19) pandemic on the everyday livelihood of people has been monumental and unparalleled. Although the pandemic has vastly affected the global healthcare system, it has also been a platform to promote and develop pioneering applications based on autonomic artificial intelligence (AI) technology with therapeutic significance in combating the pandemic. Artificial intelligence has successfully demonstrated that it can reduce the probability of human-to-human infectivity of the virus through evaluation, analysis, and triangulation of existing data on the infectivity and spread of the virus. This review talks about the applications and significance of modern robotic and automated systems that may assist in spreading a pandemic. In addition, this study discusses intelligent wearable devices and how they could be helpful throughout the COVID-19 pandemic.

The Adoption of AI in Mental Health Care-Perspectives From Mental Health Professionals: Qualitative Descriptive Study

BACKGROUND

Artificial intelligence (AI) is transforming the mental health care environment. AI tools are increasingly accessed by clients and service users. Mental health professionals must be prepared not only to use AI but also to have conversations about it when delivering care. Despite the potential for AI to enable more efficient and reliable and higher-quality care delivery, there is a persistent gap among mental health professionals in the adoption of AI.

OBJECTIVE

A needs assessment was conducted among mental health professionals to (1) understand the learning needs of the workforce and their attitudes toward AI and (2) inform the development of AI education curricula and knowledge translation products.

METHODS

A qualitative descriptive approach was taken to explore the needs of mental health professionals regarding their adoption of AI through semistructured interviews. To reach maximum variation sampling, mental health professionals (eg, psychiatrists, mental health nurses, educators, scientists, and social workers) in various settings across Ontario (eg, urban and rural, public and private sector, and clinical and research) were recruited.

RESULTS

A total of 20 individuals were recruited. Participants included practitioners (9/20, 45% social workers and 1/20, 5% mental health nurses), educator scientists (5/20, 25% with dual roles as professors/lecturers and researchers), and practitioner scientists (3/20, 15% with dual roles as researchers and psychiatrists and 2/20, 10% with dual roles as researchers and mental health nurses). Four major themes emerged: (1) fostering practice change and building self-efficacy to integrate AI into patient care; (2) promoting system-level change to accelerate the adoption of AI in mental health; (3) addressing the importance of organizational readiness as a catalyst for AI adoption; and (4) ensuring that mental health professionals have the education, knowledge, and skills to harness AI in optimizing patient care.

CONCLUSIONS

AI technologies are starting to emerge in mental health care. Although many digital tools, web-based services, and mobile apps are designed using AI algorithms, mental health professionals have generally been slower in the adoption of AI. As indicated by this study's findings, the implications are 3-fold. At the individual level, digital professionals must see the value in digitally compassionate tools that retain a humanistic approach to care. For mental health professionals, resistance toward AI adoption must be acknowledged through educational initiatives to raise awareness about the relevance, practicality, and benefits of AI. At the organizational level, digital professionals and leaders must collaborate on governance and funding structures to promote employee buy-in. At the societal level, digital and mental health professionals should collaborate in the creation of formal AI training programs specific to mental health to address knowledge gaps. This study promotes the design of relevant and sustainable education programs to support the adoption of AI within the mental health care sphere.

Types, functions and mechanisms of robot-assisted intervention for fall prevention: A systematic scoping review

BACKGROUND

Any individual may experience accidental falls, particularly older adults. Although robots can prevent falls, knowledge of their fall-preventive use is limited.

OBJECTIVE

To explore the types, functions, and mechanisms of robot-assisted intervention for fall prevention.

METHODS

A systematic scoping review of global literature published from inception to January 2022 was conducted according to Arksey and O'Malley's five-step framework. Nine electronic databases, namely, PubMed, Embase, CINAHL, IEEE Xplore, the Cochrane Library, Scopus, Web of Science, PsycINFO, and ProQuest, were searched.

RESULTS

Seventy-one articles were found with developmental (n = 63), pilot (n = 4), survey (n = 3), and proof-of-concept (n = 1) designs across 14 countries. Six types of robot-assisted intervention were found, namely cane robots, walkers, wearables, prosthetics, exoskeletons, rollators, and other miscellaneous. Five main functions were observed including (i) detection of user fall, (ii) estimation of user state, (iii) estimation of user motion, (iv) estimation of user intentional direction, and (v) detection of user balance loss. Two categories of mechanisms of robots were found. The first category was executing initiation of incipient fall prevention such as modeling, measurement of user-robot distance, estimation of center of gravity, estimation and detection of user state, estimation of user intentional direction, and measurement of angle. The second category was achieving actualization of incipient fall prevention such as adjust optimal posture, automated braking, physical support, provision of assistive force, reposition, and control of bending angle.

CONCLUSIONS

Existing literature regarding robot-assisted intervention for fall prevention is in its infancy. Therefore, future research is required to assess its feasibility and effectiveness.

Human-Robot Collaboration for Healthcare: A Narrative Review

Robotic applications have often quickly transitioned from industrial to social. Because of this, robots can now engage with people in a natural way and blend in with their surroundings. Due to the lack of medical professionals, growing healthcare costs, and the exponential rise in the population of vulnerable groups like the ill, elderly, and children with developmental disabilities, the use of social robots in the healthcare system is expanding. As a result, social robots are employed in the medical field to entertain and educate hospitalized patients about health issues, as well as to assist the elderly and sick. They are also employed in the dispensing of medications, rehabilitation, and emotional and geriatric care. Thus, social robots raise the standard and effectiveness of medical care. This article explains how patients and healthcare professionals collaborate with robots in the healthcare industry. The objectives of this collaboration are to resolve moral and legal concerns, improve patient outcomes, and improve healthcare delivery. It has a broad range of uses, including telemedicine, rehabilitation, and robotic surgical support. Human-robot interaction is the term used to describe interactions between social robots and people. Many obstacles stand in the way of human-robot interaction in healthcare, including safety concerns, acceptability issues, appropriateness, usefulness, and the worry that robots may replace human carers. In the end, these difficulties result in a poor adoption rate for robotic technology. As a result, the applications and difficulties of human-robot interaction in healthcare are thoroughly evaluated in this research. This study also reviews future safety prospects from human-robot interaction in healthcare, as well as ethical and usability issues including privacy, trust, and safety, and our aims to provide a comprehensive overview of the use of robots in healthcare, including their applications, benefits, challenges, and prospects, to facilitate a deeper understanding of this evolving field.

The need to strengthen the evaluation of the impact of Artificial Intelligence-based decision support systems on healthcare provision

Despite the renewed interest in Artificial Intelligence-based clinical decision support systems (AI-CDS), there is still a lack of empirical evidence supporting their effectiveness. This underscores the need for rigorous and continuous evaluation and monitoring of processes and outcomes associated with the introduction of health information technology. We illustrate how the emergence of AI-CDS has helped to bring to the fore the critical importance of evaluation principles and action regarding all health information technology applications, as these hitherto have received limited attention. Key aspects include assessment of design, implementation and adoption contexts; ensuring systems support and optimise human performance (which in turn requires understanding clinical and system logics); and ensuring that design of systems prioritises ethics, equity, effectiveness, and outcomes. Going forward, information technology strategy, implementation and assessment need to actively incorporate these dimensions. International policy makers, regulators and strategic decision makers in implementing organisations therefore need to be cognisant of these aspects and incorporate them in decision-making and in prioritising investment. In particular, the emphasis needs to be on stronger and more evidence-based evaluation surrounding system limitations and risks as well as optimisation of outcomes, whilst ensuring learning and contextual review. Otherwise, there is a risk that applications will be sub-optimally embodied in health systems with unintended consequences and without yielding intended benefits.

Security Aspects of Social Robots in Public Spaces: A Systematic Mapping Study

BACKGROUND

As social robots increasingly integrate into public spaces, comprehending their security implications becomes paramount. This study is conducted amidst the growing use of social robots in public spaces (SRPS), emphasising the necessity for tailored security standards for these unique robotic systems.

METHODS

In this systematic mapping study (SMS), we meticulously review and analyse existing literature from the Web of Science database, following guidelines by Petersen et al. We employ a structured approach to categorise and synthesise literature on SRPS security aspects, including physical safety, data privacy, cybersecurity, and legal/ethical considerations.

RESULTS

Our analysis reveals a significant gap in existing safety standards, originally designed for industrial robots, that need to be revised for SRPS. We propose a thematic framework consolidating essential security guidelines for SRPS, substantiated by evidence from a considerable percentage of the primary studies analysed.

CONCLUSIONS

The study underscores the urgent need for comprehensive, bespoke security standards and frameworks for SRPS. These standards ensure that SRPS operate securely and ethically, respecting individual rights and public safety, while fostering seamless integration into diverse human-centric environments. This work is poised to enhance public trust and acceptance of these robots, offering significant value to developers, policymakers, and the general public.

The Stakeholders' Involvement in Healthcare 4.0 Services Provision: The Perspective of Co-Creation

Literature research on cocreation in healthcare indicates the theoretical sophistication of research on collaboration between healthcare professionals and patients. Our research continues in the new area of Health 4.0. Cocreation has become an essential concept in the value creation process; by involving consumers in the creation process, better results are achieved regarding product quality and alignment with customer expectations and needs. In addition, consumer involvement in the creation process improves its efficiency. Cocreation allows for more efficient diagnosis and treatment of patients, as well as better and more effective use of the skills and experience of the health workforce. Our main objective is to determine the scope and depth of the cocreation of health services based on modern technological solutions (Health 4.0). We selected four cases involving Health 4.0 solutions, verified the scale and scope of cocreation using them as examples, and used the cocreation matrix. We used literature, case studies, and interviews in our research. Our analysis shows that patients can emerge as cocreators in the value creation process in Health 4.0. This can happen when they are genuinely involved in the process and when they feel responsible for the results. The article contributes to the existing theory of service cocreation by pointing out the limited scope of patient involvement in the service management process. For cocreation in Health 4.0 to increase the effectiveness of medical services, it is necessary to implement the full scope of cocreation and meaningfully empower the patient and medical workers in the creation process. This article verifies the theoretical analysis presented in our team's previous article.

Co-Creation in Health 4.0 as a New Solution for a New Era

Previous research on co-creation in healthcare indicates that the use of co-creation in the design process of health solutions influences their greater acceptance and adaptation, resulting in greater efficiency of health services and higher usability of implemented health solutions. Analysis of adaptation and acceptance of new technologies reveals the problem of misunderstanding and the need for more trust in modern tools implemented in the healthcare system. The remedy may be the use of co-creation in the process of developing modern medical products and services. This article's main purpose is to explore the co-creation process in Health 4.0, which is understood as the development of healthcare through the application of methods and tools of the Fourth Industrial Revolution. The literature review provided insights for an analytical framework-the co-creation matrix. We analyzed the case of the Italian medical platform Paginemediche.it to reveal the actors' engagement in co-creation. The results demonstrated different levels of engagement in improving the efficiency of implementing medical and technological solutions. Both theoretical and practical analysis proved that the co-creation matrix helps more precisely define the scale and scope of co-creation in Health 4.0.

Intelligent Physical Robots in Health Care: Systematic Literature Review

BACKGROUND

Intelligent physical robots based on artificial intelligence have been argued to bring about dramatic changes in health care services. Previous research has examined the use of intelligent physical robots in the health care context from different perspectives; however, an overview of the antecedents and consequences of intelligent physical robot use in health care is lacking in the literature.

OBJECTIVE

In this paper, we aimed to provide an overview of the antecedents and consequences of intelligent physical robot use in health care and to propose potential agendas for future research through a systematic literature review.

METHODS

We conducted a systematic literature review on intelligent physical robots in the health care field following the guidelines of PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses). Literature searches were conducted in 5 databases (PubMed, Scopus, PsycINFO, Embase, and CINAHL) in May 2021, focusing on studies using intelligent physical robots for health care purposes. Subsequently, the quality of the included studies was assessed using the Mixed Methods Appraisal Tool. We performed an exploratory content analysis and synthesized the findings extracted from the included articles.

RESULTS

A total of 94 research articles were included in the review. Intelligent physical robots, including mechanoid, humanoid, android, and animalistic robots, have been used in hospitals, nursing homes, mental health care centers, laboratories, and patients' homes by both end customers and health care professionals. The antecedents for intelligent physical robot use are categorized into individual-, organization-, and robot-related factors. Intelligent physical robot use in the health care context leads to both non-health-related consequences (emotional outcomes, attitude and evaluation outcomes, and behavioral outcomes) and consequences for (physical, mental, and social) health promotion for individual users. Accordingly, an integrative framework was proposed to obtain an overview of the antecedents and consequences of intelligent physical robot use in the health care context.

CONCLUSIONS

This study contributes to the literature by summarizing current knowledge in the field of intelligent physical robot use in health care, by identifying the antecedents and the consequences of intelligent physical robot use, and by proposing potential future research agendas in the specific area based on the research findings in the literature and the identified knowledge gaps.

Medical artificial intelligence ethics: A systematic review of empirical studies

Background

Artificial intelligence (AI) technologies are transforming medicine and healthcare. Scholars and practitioners have debated the philosophical, ethical, legal, and regulatory implications of medical AI, and empirical research on stakeholders' knowledge, attitude, and practices has started to emerge. This study is a systematic review of published empirical studies of medical AI ethics with the goal of mapping the main approaches, findings, and limitations of scholarship to inform future practice considerations.

Methods

We searched seven databases for published peer-reviewed empirical studies on medical AI ethics and evaluated them in terms of types of technologies studied, geographic locations, stakeholders involved, research methods used, ethical principles studied, and major findings.

Findings

Thirty-six studies were included (published 2013-2022). They typically belonged to one of the three topics: exploratory studies of stakeholder knowledge and attitude toward medical AI, theory-building studies testing hypotheses regarding factors contributing to stakeholders' acceptance of medical AI, and studies identifying and correcting bias in medical AI.

Interpretation

There is a disconnect between high-level ethical principles and guidelines developed by ethicists and empirical research on the topic and a need to embed ethicists in tandem with AI developers, clinicians, patients, and scholars of innovation and technology adoption in studying medical AI ethics.

German medical students´ views regarding artificial intelligence in medicine: A cross-sectional survey

BACKGROUND

Medical students will likely be most impacted by the envisaged move to artificial intelligence (AI) driven digital medicine, and there is a need to better understand their views regarding the use of AI technology in medicine. This study aimed to explore German medical students´ views about AI in medicine.

METHODS

A cross-sectional survey was conducted in October 2019 with all new medical students at the Ludwig Maximilian University of Munich and the Technical University Munich. This represented approximately 10% of all new medical students in Germany.

RESULTS

A total of 844 medical students participated (91.9% response rate). Two thirds (64.4%) did not feel well informed about AI in medicine. Just over a half (57.4%) of students thought that AI has useful applications in medicine, particularly in drug research and development (82.5%), less so for clinical uses. Male students were more likely to agree with advantages of AI, and female participants were more likely to be concerned about disadvantages. The vast majority of students thought that when AI is used in medicine that it is important that there are legal rules regarding liability (97%) and oversight mechanisms (93.7%), that physicians should be consulted prior to implementation (96.8%), that developers should be able to explain to them the details of the algorithm (95.6%), that algorithms should use representative data (93.9%), and that patients should always be informed when AI is used (93.5%).

CONCLUSIONS

Medical schools and continuing medical education organisers need to promptly develop programs to ensure that clinicians are able to fully realize the potential of AI technology. It is also important that legal rules and oversight are implemented to ensure that future clinicians are not faced with a workplace where important issues around responsibility are not clearly regulated.

What do Older Adults Want from Social Robots? A Qualitative Research Approach to Human-Robot Interaction (HRI) Studies

This study investigates what older adults want from social robots. Older adults are often presented with social robots designed based on developers’ assumptions that only vaguely address their actual needs. By lacking an understanding of older adults’ opinions of what technology should or could do for them–and what it should not do–we risk users of robots not finding them useful. Social and humanistic research on the robotization of care argues that it is important to prioritize user needs in technology design and implementation. Following this urgent call, we investigate older adults’ experiences of and approach to social robots in their everyday lives. This is done empirically through a qualitative analysis of data collected from six group interviews on care robots with health care service users, informal caregivers (relatives), and professional caregivers (healthcare workers). Through this “Need-Driven-Innovation” study we argue that, to secure a functional and valuable technology-fit for the user, it is crucial to take older adults’ wishes, fears, and desires about technology into account when implementing robots. It is also crucial to consider their wider networks of care, as the people in these networks also often interact with the assistive technology service users receive. Our study shows that more qualitative knowledge on the social aspect of human-robot interaction is needed to support future robot development and use in the health and care field and advocates for the crucial importance of strengthening the position of user-centered qualitative research in the field of social robotics.

An Overview of the Current State and Perspectives of Pharmacy Robot and Medication Dispensing Technology

It has been widely reported that a large number of patients die from cases of errors in the issuing of medication prescriptions. These cases occur due to a wide range of things, but the common denominator in all of the cases is humans. A hospital pharmacy has a very critical task, especially with growing patient numbers. The increasing number of prescriptions needed to be filled daily reduces the amount of time that the staff can use to focus on each individual prescription, which may increase the human error ratio. The need for robotic-assisted pharmacies is arising from here to distribute drugs to eradicate or substantially reduce human error. The pharmacy robot is one of the most significant technologies that play a prominent role in the advancement of hospital pharmacy systems. The purpose of this review paper is to cover the pharmacy robot concept and the published literature reporting on pharmacy robot technology as one of the most important applications of artificial intelligence (AI) in pharmacology. Although the outcomes of the impact of the pharmacy robot have been increasingly beneficial in overall improvement, staff morale, and functionality of pharmacies, there are still mechanical errors occurring. The errors, in turn, require human intervention. The key takeaway from this study is that robots or machines cannot replace human duties in their entirety. This in turn means that those human interventions will have an impact on the workflow and throughput.

Machine Learning, Deep Learning, and Mathematical Models to Analyze Forecasting and Epidemiology of COVID-19: A Systematic Literature Review

COVID-19 is a disease caused by SARS-CoV-2 and has been declared a worldwide pandemic by the World Health Organization due to its rapid spread. Since the first case was identified in Wuhan, China, the battle against this deadly disease started and has disrupted almost every field of life. Medical staff and laboratories are leading from the front, but researchers from various fields and governmental agencies have also proposed healthy ideas to protect each other. In this article, a Systematic Literature Review (SLR) is presented to highlight the latest developments in analyzing the COVID-19 data using machine learning and deep learning algorithms. The number of studies related to Machine Learning (ML), Deep Learning (DL), and mathematical models discussed in this research has shown a significant impact on forecasting and the spread of COVID-19. The results and discussion presented in this study are based on the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. Out of 218 articles selected at the first stage, 57 met the criteria and were included in the review process. The findings are therefore associated with those 57 studies, which recorded that CNN (DL) and SVM (ML) are the most used algorithms for forecasting, classification, and automatic detection. The importance of the compartmental models discussed is that the models are useful for measuring the epidemiological features of COVID-19. Current findings suggest that it will take around 1.7 to 140 days for the epidemic to double in size based on the selected studies. The 12 estimates for the basic reproduction range from 0 to 7.1. The main purpose of this research is to illustrate the use of ML, DL, and mathematical models that can be helpful for the researchers to generate valuable solutions for higher authorities and the healthcare industry to reduce the impact of this epidemic.

The Role of Humans in Surgery Automation

Innovation in healthcare promises unparalleled potential in optimizing the production, distribution, and use of the health workforce and infrastructure, allocating system resources more efficiently, and streamline care pathways and supply chains. A recent innovation contributing to this is robot-assisted surgeries (RAS). RAS causes less damage to the patient's body, less pain and discomfort, shorter hospital stays, quicker recovery times, smaller scars, and less risk of complications. However, introducing a robot in traditional surgeries is not straightforward and brings about new risks that conventional medical instruments did not pose before. For instance, since robots are sophisticated machines capable of acting autonomously, the surgical procedure's outcome is no longer limited to the surgeon but may also extend to the robot manufacturer and the hospital. This article explores the influence of automation on stakeholder responsibility in surgery robotization. To this end, we map how the role of different stakeholders in highly autonomous robotic surgeries is transforming, explore some of the challenges that robot manufacturers and hospital management will increasingly face as surgical procedures become more and more automated, and bring forward potential solutions to ascertain clarity in the role of stakeholders before, during, and after robot-enabled surgeries (i.e. a Robot Impact Assessment (ROBIA), a Robo-Terms framework inspired by the international trade system 'Incoterms', and a standardized adverse event reporting mechanism). In particular, we argue that with progressive robot autonomy, performance, oversight, and support will increasingly be shared between the human surgeon, the support staff, and the robot (and, by extent, the robot manufacturer), blurring the lines of who is responsible if something goes wrong. Understanding the exact role of humans in highly autonomous robotic surgeries is essential to map liability and bring certainty concerning the ascription of responsibility. We conclude that the full benefits the use of robotic innovations and solutions in surgery could bring to healthcare providers and receivers cannot be realized until there is more clarity on the division of responsibilities channeling robot autonomy and human performance, support, and oversight; a transformation on the education and training of medical staff, and betterment on the complex interplay between manufacturers, healthcare providers, and patients.

Artificial Intelligence Tools for Suicide Prevention in Adolescents and Young Adults

Background:

Artificial Intelligence is making a significant transformation in human lives. Its application in the medical and healthcare field has been also observed making an impact and improving overall outcomes. There has been a quest for similar processes in mental health due to the lack of observable changes in the areas of suicide prevention. In the last five years, there has been an emerging body of empirical research applying the technology of artificial intelligence (AI) and machine learning (ML) in mental health.

Objective:

To review the clinical applicability of the AI/ML-based tools in suicide prevention.

Methods:

The compelling question of predicting suicidality has been the focus of this research. We performed a broad literature search and then identified 36 articles relevant to meet the objectives of this review. We review the available evidence and provide a brief overview of the advances in this field.

Conclusion:

In the last five years, there has been more evidence supporting the implementation of these algorithms in clinical practice. Its current clinical utility is limited to using electronic health records and could be highly effective in conjunction with existing tools for suicide prevention. Other potential sources of relevant data include smart devices and social network sites. There are some serious questions about data privacy and ethics which need more attention while developing these new modalities in suicide research.

Artificial intelligence and Psychiatry: An overview

The burden of mental illness both in world and India is increasing at an alarming rate. Adding to it, there has been an increase in mental health challenges during covid-19 pandemic with a rise in suicide, loneliness and substance use. Artificial intelligence can act as a potential solution to address this shortage. The use of artificial intelligence is increasingly being employed in various fields of mental health like affective disorders, psychosis, and geriatric psychiatry. The benefits are various like lower costs, wider reach but at the same time it comes with its own disadvantages. This article reviews the current understanding of artificial intelligence, the types of Artificial intelligence, its current use in various mental health disorders, current status in India, advantages, disadvantages and future potentials. With the passage of time and digitalization of the modern age, there will be an increase in the use of artificial intelligence in psychiatry hence a detailed understanding will be thoughtful. For this, we searched PubMed, Google Scholar, and Science Direct, China national Knowledge Infrastructure (CNKI), Globus Index Medicus search engines by using keywords. Initial searches involved the use of each individual keyword while the later searches involved the use of more than one word in different permutation combinations.

Lost in the diffusion chasm: Lessons learned from a failed robot project in the public sector

Public sector organizations increasingly engage in robotic innovation projects to assist or substitute for humans in service delivery. However, transitioning small-scale development projects into a large-scale context is a notoriously difficult task that often fails, with many promising robotic projects becoming lost in the diffusion “chasm.” We investigate a failed robotic diffusion project to analyze what went wrong and what can be learned from it. Despite an increased interest in learning from public sector digitalization failure, little attention has been paid to how and why seemingly successful service robot initiatives fail to move beyond the pilot stage. We identify three types of explanations for diffusion failure using an in-depth case study of a service robot initiative in the Danish eldercare sector that had a high degree of management support and commitment from key stakeholders. Our analysis demonstrates how the failure was caused by interrelated and context-specific reasons regarding the lack of technological maturity of the service robot (technology-oriented explanations), inadequate problem-solution fit in the conceptual design (scope-oriented explanations), and misalignment between the robot company and public sector organization mindsets (competing logic-oriented explanations). We outline the lessons learned for public sector digitalization and discuss the paradox between the hype surrounding robot innovations and their slow diffusion.

Embedded ethics: a proposal for integrating ethics into the development of medical AI

The emergence of ethical concerns surrounding artificial intelligence (AI) has led to an explosion of high-level ethical principles being published by a wide range of public and private organizations. However, there is a need to consider how AI developers can be practically assisted to anticipate, identify and address ethical issues regarding AI technologies. This is particularly important in the development of AI intended for healthcare settings, where applications will often interact directly with patients in various states of vulnerability. In this paper, we propose that an ‘embedded ethics’ approach, in which ethicists and developers together address ethical issues via an iterative and continuous process from the outset of development, could be an effective means of integrating robust ethical considerations into the practical development of medical AI.

Nurses’ View towards the Use of Robotic during Pandemic COVID-19 in Indonesia: A Qualitative Study

Background: Rapid advances in artificial intelligence and robotics have alleviated difficulties for patients, hospitals, and the industry as a whole. However, the health care system is identically human-centered at its core, and many healthcare professions may not be ready to work with robots. Understanding nurses' views toward robotics can help integrate robotic technologies into future patient care. Objectives: This study aimed to explore how nurses view using robotics during the COVID-19 pandemic. Methods: This study used a qualitative descriptive technique to registered nurses who provide direct care to the patients with COVID-19 recruited from two hospitals in Indonesia. Purposive sampling was used to select respondents with criteria of those who had worked for at least one year and were willing to participate—the analysis used qualitative content analysis. Results: A total of 20 female nurses with an average age of 32.8 ± 4.0 years participated in this study. The qualitative findings revealed three themes with nine sub-themes, namely the use of robotic in nursing care (sub-theme: reducing the risk of COVID-19 transmission, monitoring patients remotely, and helping in providing care), the burden of using robotic in nursing care (sub-theme: digital literacy in nursing care, culture difference in providing care, changing care practice habits, and safety concern, and attitude toward robotic in nursing care (sub-theme: negative response). Conclusions: This study explored nurses' views on the usage of robotics during the pandemic COVID-10. It implies that a strategic plan would have many benefits and limitations, such as nursing care burden, negative attitude, and cultural awareness.

Artificial intelligence and the NHS: a qualitative exploration of the factors influencing adoption

Background

AI has the potential to improve healthcare. However, there is limited research investigating the factors which influence the adoption of AI within a healthcare system.

Research aims

I aimed to use innovation theory to understand the barriers and facilitators that influence AI adoption in the NHS; and to explore solutions to overcome these barriers, and examine these factors, particularly within radiology, pathology and general practice.

Methodology

Twelve semi-structured, one-to-one interviews were conducted with key informants. Interview data were analysed using thematic analysis.

Findings

A range of barriers and facilitators to the adoption of AI within the NHS were identified, including IT infrastructure and language clarity. Several solutions to overcome the barriers were proposed by participants, including education strategies and innovation champions.

Conclusion

Future research should explore the importance of IT infrastructure in supporting AI adoption, examine the terminology around AI and explore specialty-specific barriers to AI adoption in greater depth.

Positive Turn in Elder-Care Workers' Views Toward Telecare Robots

Robots have been slowly but steadily introduced to welfare sectors. Our previous observations based on a large-scale survey study on Finnish elder-care workers in 2016 showed that while robots were perceived to be useful in certain telecare tasks, using robots may also prove to be incompatible with the care workers’ personal values. The current study presents the second wave of the survey data from 2020, with the same respondents (N = 190), and shows how these views have changed for the positive, including higher expectations of telecare robotization and decreased concerns over care robots’ compatibility with personal values. In a longitudinal analysis (Phase 1), the positive change in views toward telecare robots was found to be influenced by the care robots’ higher value compatibility. In an additional cross-sectional analysis (Phase 2), focusing on the factors underlying personal values, care robots’ value compatibility was associated with social norms toward care robots, the threat of technological unemployment, and COVID-19 stress. The significance of social norms in robot acceptance came down to more universal ethical standards of care work rather than shared norms in the workplace. COVID-19 stress did not explain the temporal changes in views about robot use in care but had a role in assessments of the compatibility between personal values and care robot use. In conclusion, for care workers to see potential in care robots, the new technology must support ethical standards of care work, such as respectfulness, compassion, and trustworthiness of the nurse–patient interaction. In robotizing care work, personal values are significant predictors of the task values.

Robots and risk of COVID-19 workplace contagion: Evidence from Italy

This work investigates the cross-industry relationship between robot adoption and the risk of contracting COVID-19 in the workplace in Italy. Using a novel dataset on the risk of workplace contagion, we show that industries employing more robots tend to exhibit lower risks, thereby providing some empirical support for the widely held, but so far untested, hypothesis that robots can help mitigate the risk of contagion among workers by reducing the need for physical interactions. While we acknowledge the relevance of robots in the fight against COVID-19 and their possible role in enhancing the resilience of economic systems against future pandemics, we also thoroughly discuss a series of potential trade-offs between workplace safety and employment conditions that could arise (especially in the short run) due to a substantial increase in the rate of robot adoption.

Robots and risk of COVID-19 workplace contagion: Evidence from Italy

This work investigates the cross-industry relationship between robot adoption and the risk of contracting COVID-19 in the workplace in Italy. Using a novel dataset on the risk of workplace contagion, we show that industries employing more robots tend to exhibit lower risks, thereby providing some empirical support for the widely held, but so far untested, hypothesis that robots can help mitigate the risk of contagion among workers by reducing the need for physical interactions. While we acknowledge the relevance of robots in the fight against COVID-19 and their possible role in enhancing the resilience of economic systems against future pandemics, we also thoroughly discuss a series of potential trade-offs between workplace safety and employment conditions that could arise (especially in the short run) due to a substantial increase in the rate of robot adoption.

Conceptualizing the digitalization of healthcare work: A metaphor-based Critical Interpretive Synthesis

The digitalization of healthcare work has gained center stage in academic debates spanning disciplines as diverse as medicine, sociology and STS. The different analytical interests and methodological traditions of these three strains of scholarship have, however, resulted in quite diverging approaches to this issue. Points of interest have ranged from the (disattended) promise of increased efficiency of healthcare work, to dynamics of task delegation, (re-)professionalization and (re-)distribution of invisible work, to the disruption of informal organization. Instead of studying these dynamics in practice, in this paper we foreground the potentiality for theory-making inherent in the systematic cross-contamination of different theoretical and disciplinary perspectives. We perform a Critical Interpretive Synthesis (CIS) centering the ways the digitalization of healthcare work has been investigated in recent STS, sociological and medical literature. To open up assumptions and insights intrinsic to each body of literature for scholars and practitioners in other fields, we propose here a metaphor-based variation on CIS approaches. We probe, in turn, what slime molds can teach us about STS's focus on interconnections and materiality, how we can better understand sociological analyses of invisible work exploring them through theatrical performances, and which lessons river engineering offers concerning medical scholarship's discussion of efficiency and proper healthcare work. Thinking through these metaphors, we conceptualize the digitalization of healthcare work as a phenomenon spanning, at once, the directionality of technological innovation trajectories and the open-endedness of situated changes in work practices. Based on our analysis, we propose focusing on technological scripts, and various forms of invisible work and informal organization as entry points into the study of the tension between directionality and open-endedness in the context of the digitalization of healthcare work.

Views on Using Social Robots in Professional Caregiving: Content Analysis of a Scenario Method Workshop

BACKGROUND

Interest in digital technologies in the health care sector is growing and can be a way to reduce the burden on professional caregivers while helping people to become more independent. Social robots are regarded as a special form of technology that can be usefully applied in professional caregiving with the potential to focus on interpersonal contact. While implementation is progressing slowly, a debate on the concepts and applications of social robots in future care is necessary.

OBJECTIVE

In addition to existing studies with a focus on societal attitudes toward social robots, there is a need to understand the views of professional caregivers and patients. This study used desired future scenarios to collate the perspectives of experts and analyze the significance for developing the place of social robots in care.

METHODS

In February 2020, an expert workshop was held with 88 participants (health professionals and educators; [PhD] students of medicine, health care, professional care, and technology; patient advocates; software developers; government representatives; and research fellows) from Austria, Germany, and Switzerland. Using the scenario methodology, the possibilities of analog professional care (Analog Care), fully robotic professional care (Robotic Care), teams of robots and professional caregivers (Deep Care), and professional caregivers supported by robots (Smart Care) were discussed. The scenarios were used as a stimulus for the development of ideas about future professional caregiving. The discussion was evaluated using qualitative content analysis.

RESULTS

The majority of the experts were in favor of care in which people are supported by technology (Deep Care) and developed similar scenarios with a focus on dignity-centeredness. The discussions then focused on the steps necessary for its implementation, highlighting a strong need for the development of eHealth competence in society, a change in the training of professional caregivers, and cross-sectoral concepts. The experts also saw user acceptance as crucial to the use of robotics. This involves the acceptance of both professional caregivers and care recipients.

CONCLUSIONS

The literature review and subsequent workshop revealed how decision-making about the value of social robots depends on personal characteristics related to experience and values. There is therefore a strong need to recognize individual perspectives of care before social robots become an integrated part of care in the future.

Surveillance in Next-Generation Personalized Healthcare: Science and Ethics of Data Analytics in Healthcare

Advances in science and technology have allowed for incredible improvements in healthcare. Additionally, the digital revolution in healthcare provides new ways of collecting and storing large volumes of patient data, referred to as big healthcare data. As a result, healthcare providers are now able to use data to gain a deeper understanding of how to treat an individual in what is referred to as personalized healthcare. Regardless, there are several ethical challenges associated with big healthcare data that affect how personalized healthcare is delivered. To highlight these issues, this article will review the role of big data in personalized healthcare while also discussing the ethical challenges associated with it. The article will also discuss public health surveillance, its implications, and the challenges associated with collecting participants' information. The article will proceed by highlighting next generation technologies, including robotics and 3D printing. The article will conclude by providing recommendations on how patient privacy can be protected in next-generation personalized healthcare.

Digital Orientation of Health Systems in the Post-COVID-19 "New Normal" in the United States: Cross-sectional Survey

Background

Almost all health systems have developed some form of customer-facing digital technologies and have worked to align these systems to their existing electronic health records to accommodate the surge in remote and virtual care deliveries during the COVID-19 pandemic. Others have developed analytics-driven decision-making capabilities. However, it is not clear how health systems in the United States are embracing digital technologies and there is a gap in health systems’ abilities to integrate workflows with expanding technologies to spur innovation and futuristic growth. There is a lack of reliable and reported estimates of the current and futuristic digital orientations of health systems. Periodic assessments will provide imperatives to policy formulation and align efforts to yield the transformative power of emerging digital technologies.

Objective

The aim of this study was to explore and examine differences in US health systems with respect to digital orientations in the post–COVID-19 “new normal” in 2021. Differences were assessed in four dimensions: (1) analytics-oriented digital technologies (AODT), (2) customer-oriented digital technologies (CODT), (3) growth and innovation–oriented digital technologies (GODT), and (4) futuristic and experimental digital technologies (FEDT). The former two dimensions are foundational to health systems’ digital orientation, whereas the latter two will prepare for future disruptions.

Methods

We surveyed a robust group of health system chief executive officers (CEOs) across the United States from February to March 2021. Among the 625 CEOs, 135 (22%) responded to our survey. We considered the above four broad digital technology orientations, which were ratified with expert consensus. Secondary data were collected from the Agency for Healthcare Research and Quality Hospital Compendium, leading to a matched usable dataset of 124 health systems for analysis. We examined the relationship of adopting the four digital orientations to specific hospital characteristics and earlier reported factors as barriers or facilitators to technology adoption.

Results

Health systems showed a lower level of CODT (mean 4.70) or GODT (mean 4.54) orientations compared with AODT (mean 5.03), and showed the lowest level of FEDT orientation (mean 4.31). The ordered logistic estimation results provided nuanced insights. Medium-sized (P<.001) health systems, major teaching health systems (P<.001), and systems with high-burden hospitals (P<.001) appear to be doing worse with respect to AODT orientations, raising some concerns. Health systems of medium (P<.001) and large (P=.02) sizes, major teaching health systems (P=.07), those with a high revenue (P=.05), and systems with high-burden hospitals (P<.001) have less CODT orientation. Health systems in the midwest (P=.05) and southern (P=.04) states are more likely to adopt GODT, whereas high-revenue (P=.004) and investor-ownership (P=.01) health systems are deterred from GODT. Health systems of a medium size, and those that are in the midwest (P<.001), south (P<.001), and west (P=.01) are more adept to FEDT, whereas medium (P<.001) and high-revenue (P<.001) health systems, and those with a high discharge rate (P=.04) or high burden (P=.003, P=.005) have subdued FEDT orientations.

Conclusions

Almost all health systems have some current foundational digital technological orientations to glean intelligence or service delivery to customers, with some notable exceptions. Comparatively, fewer health systems have growth or futuristic digital orientations. The transformative power of digital technologies can only be leveraged by adopting futuristic digital technologies. Thus, the disparities across these orientations suggest that a holistic, consistent, and well-articulated direction across the United States remains elusive. Accordingly, we suggest that a policy strategy and financial incentives are necessary to spur a well-visioned and articulated digital orientation for all health systems across the United States. In the absence of such a policy to collectively leverage digital transformations, differences in care across the country will continue to be a concern.

Expectations and Perceptions of Healthcare Professionals for Robot Deployment in Hospital Environments During the COVID-19 Pandemic

Several challenges to guarantee medical care have been exposed during the current COVID-19 pandemic. Although the literature has shown some robotics applications to overcome the potential hazards and risks in hospital environments, the implementation of those developments is limited, and few studies measure the perception and the acceptance of clinicians. This work presents the design and implementation of several perception questionnaires to assess healthcare provider's level of acceptance and education toward robotics for COVID-19 control in clinic scenarios. Specifically, 41 healthcare professionals satisfactorily accomplished the surveys, exhibiting a low level of knowledge about robotics applications in this scenario. Likewise, the surveys revealed that the fear of being replaced by robots remains in the medical community. In the Colombian context, 82.9% of participants indicated a positive perception concerning the development and implementation of robotics in clinic environments. Finally, in general terms, the participants exhibited a positive attitude toward using robots and recommended them to be used in the current panorama.

Health information technology and digital innovation for national learning health and care systems

Health information technology can support the development of national learning health and care systems, which can be defined as health and care systems that continuously use data-enabled infrastructure to support policy and planning, public health, and personalisation of care. The COVID-19 pandemic has offered an opportunity to assess how well equipped the UK is to leverage health information technology and apply the principles of a national learning health and care system in response to a major public health shock. With the experience acquired during the pandemic, each country within the UK should now re-evaluate their digital health and care strategies. After leaving the EU, UK countries now need to decide to what extent they wish to engage with European efforts to promote interoperability between electronic health records. Major priorities for strengthening health information technology in the UK include achieving the optimal balance between top-down and bottom-up implementation, improving usability and interoperability, developing capacity for handling, processing, and analysing data, addressing privacy and security concerns, and encouraging digital inclusivity. Current and future opportunities include integrating electronic health records across health and care providers, investing in health data science research, generating real-world data, developing artificial intelligence and robotics, and facilitating public-private partnerships. Many ethical challenges and unintended consequences of implementation of health information technology exist. To address these, there is a need to develop regulatory frameworks for the development, management, and procurement of artificial intelligence and health information technology systems, create public-private partnerships, and ethically and safely apply artificial intelligence in the National Health Service.

Older adults' experiences and perceptions of living with Bomy, an assistive dailycare robot: a qualitative study

An aging global population and preference for aging-in-place pose the opportunity for home-based robots to assist older adults with their daily routines. However, there is limited research into the experiences of older adults using robots in their own homes. In this descriptive qualitative feasibility study, older self-supporting and community-dwelling adults with various age-related health needs used Bomy, a dailycare robot in their homes for up to one week. The study explored the usefulness of the robot and participants' perceptions and experiences of using it. Bomy reminded them of daily activities and delivered cognitive stimulation games. Semi-structured in-person interviews were conducted, and data were analyzed thematically. Findings revealed an acceptance toward robots and the value of assistive dailycare robots. Participants perceived Bomy as a companion and made suggestions for improvement, including resolving technical issues associated with long-term use. Future functions should be personalizable, to accommodate each user's health needs and could also include smoke detection and reading aloud functions. Dailycare robots show promising potential in elderly care, especially in providing reminders for medication, health and wellbeing. This study highlights the importance of co-design and testing robotics in the environments for which they have been developed. Widespread implementation of Bomy might be feasible in the future, with some further adjustments.

Impact and Challenges of Integrating Artificial Intelligence and Telemedicine into Clinical Ophthalmology

ABSTRACT

Aging populations and worsening burden of chronic, treatable disease is increasingly creating a global shortfall in ophthalmic care provision. Remote and automated systems carry the promise to expand the scale and potential of health care interventions, and reduce strain on health care services through safe, personalized, efficient, and cost-effective services. However, significant challenges remain. Forward planning in service design is paramount to safeguard patient safety, trust in digital services, data privacy, medico-legal implications, and digital exclusion. We explore the impact and challenges facing patients and clinicians in integrating AI and telemedicine into ophthalmic care-and how these may influence its direction.

Training the Next Industrial Engineers and Managers about Industry 4.0: A Case Study about Challenges and Opportunities in the COVID-19 Era

Training the next generation of industrial engineers and managers is a constant challenge for academia, given the fast changes of industrial technology. The current and predicted development trends in applied technologies affecting industry worldwide as formulated in the Industry 4.0 initiative have clearly emphasized the needs for constantly adapting curricula. The sensible socioeconomic changes generated by the COVID-19 pandemic have induced significant challenges to society in general and industry. Higher education, specifically when dealing with Industry 4.0, must take these new challenges rapidly into account. Modernization of the industrial engineering curriculum combined with its migration to a blended teaching landscape must be updated in real-time with real-world cases. The COVID-19 crisis provides, paradoxically, an opportunity for dealing with the challenges of training industrial engineers to confront a virtual dematerialized work model which has accelerated during and will remain for the foreseeable future after the pandemic. The paper describes the methodology used for adapting, enhancing, and evaluating the learning and teaching experience under the urgent and unexpected challenges to move from face-to-face university courses distant and online teaching. The methodology we describe is built on a process that started before the onset of the pandemic, hence in the paper we start by describing the pre-COVID-19 status in comparison to published initiatives followed by the real time modifications we introduced in the faculty to adapt to the post-COVID-19 teaching/learning era. The focus presented is on Industry 4.0. subjects at the leading edge of the technology changes affecting the industrial engineering and technology management field. The manuscript addresses the flow from system design subjects to implementation areas of the curriculum, including practical examples and the rapid decisions and changes made to encompass the effects of the COVID-19 pandemic on content and teaching methods including feedback received from participants.

Implementation of a Humanoid Robot as an Innovative Approach to Child Life Interventions in a Children's Hospital: Lofty Goal or Tangible Reality?

Introduction

This study reports the findings on how Child life specialists (CLSs) implemented an innovative approach to providing therapeutic support to pediatric patients.

Methods

Part of a larger study that uncovered themes about CLSs’ experiences while working with MEDi^®, this study reports the reflections that CLSs have about the process of implementation. Seven CLSs participated in semi-structured interviews. Content analysis was conducted on interview data and three themes were generated.

Results

The first was in regards to the adoption process whereby CLS challenges, successes, and surprises were revealed. Second, CLSs explained how using MEDi^® aligned with the roles and responsibilities of their profession. The third area of understanding was in CLS explanation of the friendly emotional impact MEDi^® seems to have on the hospital environment.

Conclusion

Child life specialists are encouraged to use the MEDi^® robot to support children at the bedside.

Service Robots in the Healthcare Sector

Traditionally, advances in robotic technology have been in the manufacturing industry due to the need for collaborative robots. However, this is not the case in the service sectors, especially in the healthcare sector. The lack of emphasis put on the healthcare sector has led to new opportunities in developing service robots that aid patients with illnesses, cognition challenges and disabilities. Furthermore, the COVID-19 pandemic has acted as a catalyst for the development of service robots in the healthcare sector in an attempt to overcome the difficulties and hardships caused by this virus. The use of service robots are advantageous as they not only prevent the spread of infection, and reduce human error but they also allow front-line staff to reduce direct contact, focusing their attention on higher priority tasks and creating separation from direct exposure to infection. This paper presents a review of various types of robotic technologies and their uses in the healthcare sector. The reviewed technologies are a collaboration between academia and the healthcare industry, demonstrating the research and testing needed in the creation of service robots before they can be deployed in real-world applications and use cases. We focus on how robots can provide benefits to patients, healthcare workers, customers, and organisations during the COVID-19 pandemic. Furthermore, we investigate the emerging focal issues of effective cleaning, logistics of patients and supplies, reduction of human errors, and remote monitoring of patients to increase system capacity, efficiency, resource equality in hospitals, and related healthcare environments.

Assessment of the Acceptability and Feasibility of Using Mobile Robotic Systems for Patient Evaluation

IMPORTANCE

Before the widespread implementation of robotic systems to provide patient care during the COVID-19 pandemic occurs, it is important to understand the acceptability of these systems among patients and the economic consequences associated with the adoption of robotics in health care settings.

OBJECTIVE

To assess the acceptability and feasibility of using a mobile robotic system to facilitate health care tasks.

DESIGN, SETTING, AND PARTICIPANTS

This study included 2 components: a national survey to examine the acceptability of using robotic systems to perform health care tasks in a hospital setting and a single-site cohort study of patient experiences and satisfaction with the use of a mobile robotic system to facilitate triage and telehealth tasks in the emergency department (ED). The national survey comprised individuals living in the US who participated in a sampling-based survey via an online analytic platform. Participants completed the national survey between August 18 and August 21, 2020. The single-site cohort study included patients living in the US who presented to the ED of a large urban academic hospital providing quaternary care in Boston, Massachusetts between April and August 2020. All data were analyzed from August to October 2020.

EXPOSURES

Participants in the national survey completed an online survey to measure the acceptability of using a mobile robotic system to perform health care tasks (facilitating telehealth interviews, acquiring vital signs, obtaining nasal or oral swabs, placing an intravenous catheter, performing phlebotomy, and turning a patient in bed) in a hospital setting in the contexts of general interaction and interaction during the COVID-19 pandemic. Patients in the cohort study were exposed to a mobile robotic system, which was controlled by an ED clinician and used to facilitate a triage interview. After exposure, patients completed an assessment to measure their satisfaction with the robotic system.

MAIN OUTCOMES AND MEASURES

Acceptability of the use of a mobile robotic system to facilitate health care tasks in a hospital setting (national survey) and feasibility and patient satisfaction regarding the use of a mobile robotic system in the ED (cohort study).

RESULTS

For the national survey, 1154 participants completed all acceptability questions, representing a participation rate of 35%. After sample matching, a nationally representative sample of 1000 participants (mean [SD] age, 48.7 [17.0] years; 535 women [53.5%]) was included in the analysis. With regard to the usefulness of a robotic system to perform specific health care tasks, the response of "somewhat useful" was selected by 373 participants (37.3%) for facilitating telehealth interviews, 350 participants (35.0%) for acquiring vital signs, 307 participants (30.7%) for obtaining nasal or oral swabs, 228 participants (22.8%) for placing an intravenous catheter, 249 participants (24.9%) for performing phlebotomy, and 371 participants (37.1%) for turning a patient in bed. The response of "extremely useful" was selected by 287 participants (28.7%) for facilitating telehealth interviews, 413 participants (41.3%) for acquiring vital signs, 192 participants (19.2%) for obtaining nasal or oral swabs, 159 participants (15.9%) for placing an intravenous catheter, 167 participants (16.7%) for performing phlebotomy, and 371 participants (37.1%) for turning a patient in bed. In the context of the COVID-19 pandemic, the median number of individuals who perceived the application of robotic systems to be acceptable for completing telehealth interviews, obtaining nasal and oral swabs, placing an intravenous catheter, and performing phlebotomy increased. For the ED cohort study, 51 individuals were invited to participate, and 41 participants (80.4%) enrolled. One participant was unable to complete the study procedures because of a signaling malfunction in the robotic system. Forty patients (mean [SD] age, 45.8 [2.7] years; 29 women [72.5%]) completed the mobile robotic system-facilitated triage interview, and 37 patients (92.5%) reported that the interaction was satisfactory. A total of 33 participants (82.5%) reported that their experience of receiving an interview facilitated by a mobile robotic system was as satisfactory as receiving an in-person interview from a clinician.

CONCLUSIONS AND RELEVANCE

In this study, a mobile robotic system was perceived to be acceptable for use in a broad set of health care tasks among survey respondents across the US. The use of a mobile robotic system enabled the facilitation of contactless triage interviews of patients in the ED and was considered acceptable among participants. Most patients in the ED rated the quality of mobile robotic system-facilitated interaction to be equivalent to in-person interaction with a clinician.

Abel: Integrating Humanoid Body, Emotions, and Time Perception to Investigate Social Interaction and Human Cognition

Humanoids have been created for assisting or replacing humans in many applications, providing encouraging results in contexts where social and emotional interaction is required, such as healthcare, education, and therapy. Bioinspiration, that has often guided the design of their bodies and minds, made them also become excellent research tools, probably the best platform by which we can model, test, and understand the human mind and behavior. Driven by the aim of creating a believable robot for interactive applications, as well as a research platform for investigating human cognition and emotion, we are constructing a new humanoid social robot: Abel. In this paper, we discussed three of the fundamental principles that motivated the design of Abel and its cognitive and emotional system: hyper-realistic humanoid aesthetics, human-inspired emotion processing, and human-like perception of time. After reporting a brief state-of-the-art on the related topics, we present the robot at its stage of development, what are the perspectives for its application, and how it could satisfy the expectations as a tool to investigate the human mind, behavior, and consciousness.

The impact of the introduction of artificial intelligence in radiology and its potential legal implications in the UK and Ireland

Artificial intelligence (AI) has been defined as a branch of computer science dealing with the capability and simulation of a machine to imitate intelligent human behaviour. Diagnostic radiology, being a computer-based service, is unsurprisingly at the forefront of the discussion of the use of AI in medicine. There are however differing schools of thought regarding its use; namely, will AI eventually replace the radiologist? Or indeed will it ever be fully capable of replacing radiology as a speciality, but rather be used as an aid to the profession whereby a human’s input will always be required?

Furthermore, what will the legal implications of AI in radiology mean to the profession? Who will be liable for missed diagnoses? Is it possible that the introduction of AI to radiology will in fact make the profession busier?

Transcending human frailties with technological enhancements and replacements: Transhumanist perspective in nursing and healthcare

As human beings age, they become weak, fragile, and feeble. It is a slowly progressing yet complex syndrome in which old age or some disabilities are not prerequisites; neither does loss of human parts lead to frailty among the physically fit older persons. This paper aims to describe the influences of transhumanist perspectives on human-technology enhancements and replacements in the transcendence of human frailties, including those of older persons, in which technology is projected to deliver solutions toward transcending these frailties. Through technologies including genetic screening and other technological manipulations, intelligent machines and augmented humans improve, maintain, and remedy human-linked susceptibilities. Furthermore, other technologies replace parts fabricated through inorganic-mechanical processes such as 3D-printing. Advancing technologies are reaching the summit of technological sophistication contributing to the transhumanist views of being human in a technological world. Technologies enhance the transcendence of human frailties as essential expressions of the symbiosis between human beings and technology in a transcendental world.

Can Robots Improve Testing Capacity for SARS-CoV-2?

There is currently increasing interest internationally in deploying robotic applications for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) testing, as these can help to reduce the risk of transmission of the virus to health care staff and patients. We provide an overview of key recent developments in this area. We argue that, although there is some potential for deploying robots to help with SARS-CoV-2 testing, the potential of patient-facing applications is likely to be limited. This is due to the high costs associated with patient-facing functionality, and risks of potentially adverse impacts on health care staff work practices and patient interactions. In contrast, back-end laboratory-based robots dealing with sample extraction and amplification, that effectively integrate with established processes, software, and interfaces to process samples, are much more likely to result in safety and efficiency gains. Consideration should therefore be given to deploying these at scale.

The Role of Healthcare Robotics in Providing Support to Older Adults: a Socio-ecological Perspective

PURPOSE OF REVIEW

In this review, we provide an overview of how healthcare robotics can facilitate healthy aging, with an emphasis on physical, cognitive, and social supports. We next provide a synthesis of future challenges and considerations in the development and application of healthcare robots. We organize these considerations using a socio-ecological perspective and discuss considerations at the individual, care partner, community healthcare, and healthcare policy levels.

RECENT FINDINGS

Older adults are the fastest growing segment of the US population. Age-related changes and challenges can present difficulties, for older adults want to age healthily and maintain independence. Technology, specifically healthcare robots, has potential to provide health supports to older adults. These supports span widely across the physical, cognitive, and social aspects of healthy aging.

SUMMARY

Our review suggests that while healthcare robotics has potential to revolutionize the way in which older adults manage their health, there are many challenges such as clinical effectiveness, technology acceptance, health informatics, and healthcare policy and ethics. Addressing these challenges at all levels of the healthcare system will help ensure that healthcare robotics promote healthy aging and are applied safely, effectively, and reliably.

Enablers and barriers to the implementation of socially assistive humanoid robots in health and social care: a systematic review

OBJECTIVES

Socially assistive humanoid robots are considered a promising technology to tackle the challenges in health and social care posed by the growth of the ageing population. The purpose of our study was to explore the current evidence on barriers and enablers for the implementation of humanoid robots in health and social care.

DESIGN

Systematic review of studies entailing hands-on interactions with a humanoid robot.

SETTING

From April 2018 to June 2018, databases were searched using a combination of the same search terms for articles published during the last decade. Data collection was conducted by using the Rayyan software, a standardised predefined grid, and a risk of bias and a quality assessment tool.

PARTICIPANTS

Post-experimental data were collected and analysed for a total of 420 participants. Participants comprised: older adults (n=307) aged ≥60 years, with no or some degree of age-related cognitive impairment, residing either in residential care facilities or at their home; care home staff (n=106); and informal caregivers (n=7).

PRIMARY OUTCOMES

Identification of enablers and barriers to the implementation of socially assistive humanoid robots in health and social care, and consequent insights and impact. Future developments to inform further research.

RESULTS

Twelve studies met the eligibility criteria and were included. None of the selected studies had an experimental design; hence overall quality was low, with high risks of biases. Several studies had no comparator, no baseline, small samples, and self-reported measures only. Within this limited evidence base, the enablers found were enjoyment, usability, personalisation and familiarisation. Barriers were related to technical problems, to the robots' limited capabilities and the negative preconceptions towards the use of robots in healthcare. Factors which produced mixed results were the robot's human-like attributes, previous experience with technology and views of formal and informal carers.

CONCLUSIONS

The available evidence related to implementation factors of socially assistive humanoid robots for older adults is limited, mainly focusing on aspects at individual level, and exploring acceptance of this technology. Investigation of elements linked to the environment, organisation, societal and cultural milieu, policy and legal framework is necessary.

PROSPERO REGISTRATION NUMBER

CRD42018092866.