To synthesise the current research on long-term care workers' perceptions (i.e., attitudes, concerns, and expected functions) of robot-assisted care and their perceived effects of different types of robot-assisted care for older adults in long-term care facilities.

Scoping review.

A search was conducted in July 2024 using five databases. Articles published between 2010 and 2024 on the perceptions and/or perceived effects of robot-assisted care for older adults among frontline long-term care workers in long-term care facilities were identified. Additionally, the reference lists of the included articles were manually searched.

A five-step framework that guided the development of research questions, screening of studies, and synthesis and presentation of data was adopted. Two authors independently screened and analysed the identified articles. Conflicts were resolved through joint-discussions.

Forty-one articles were included in the review. Data were narratively synthesised into three categories: expected function of care robots, perceived effects of robot-assisted care, and attitudes and concerns regarding robot-assisted care. Subcategories were identified and presented in tabular form.

This review shows the physical, psychological, social, and practical benefits and limitations of different types of robot-assisted care. It also contributes to understanding long-term care workers' attitudes, concerns, and expectations regarding the function of robot-assisted care.

Having a priori discussion with long-term care workers about their expectations regarding using robot-assisted care is needed. Improvements in the design and in the digital literacy of the workers are also necessary.

This review provides an overview of the perceptions and perceived effects of different types of robot-assisted care among care workers in long-term care facilities. The findings provide practical implications and highlight areas in need of further studies.

Scoping Review (PRISMA-ScR) checklist.

No Patient or Public Contribution.

Isolated patients pose physical challenges to medical staff owing to the need for protective gear. Additionally, communication issues arise within isolation rooms, hampering patient care. Mobile robots offer potential solutions, allowing for contactless communication and efficient task delegation, thereby reducing the risk of cross-contamination and minimizing staff workload. This preliminary study assessed the usability, acceptability, and potential for improvement of mobile robots in clinical nursing scenarios, focusing on nurses' perspectives. A preliminary test was conducted using mobile robots in a simulated hospital environment with 30 experienced nurses responsible for isolated patient care. Data were collected through interviews, surveys, and scenario-based tasks. Two scenarios were designed to evaluate the usability and effectiveness of mobile robots in real-world nursing situations. Nurses regarded mobile robots as highly usable and useful in healthcare settings. Robots efficiently handled tasks like remote supply delivery and medication distribution. Nurses recognized the potential for improved communication and efficiency with mobile robots; however, concerns were raised about the robots' limitations in providing emotional support and potential safety issues during emergencies. This research emphasizes the promising role of mobile robots in enhancing healthcare delivery within isolation rooms. While these findings indicate the potential for mobile robots, careful planning, training, and scenario development are crucial for their safe and effective integration into clinical settings. Further research, tailored scenarios, and a reevaluation of the evolving role of nurses in a technology-augmented healthcare environment are necessary, emphasizing the importance of understanding the capabilities and limitations of robotic assistance in patient care.

Globally, the rates at which the aging population and the prevalence of chronic diseases are increasing are substantial. With declining birth rates and a growing percentage of older individuals, the demand for nursing staff is steadily rising. However, the shortage of nursing personnel has been a long-standing issue. In recent years, numerous researchers have advocated for the implementation of nursing robots as a substitute for traditional human labor.

This study analyzes hospital visitors' attitudes and priorities regarding the functional areas of artificial intelligence (AI) nursing robots based on the Kano model. Building on this analysis, recommendations are provided for the functional optimization of AI nursing robots, aiming to facilitate their adoption in the nursing field.

Using a random sampling method, 457 hospital visitors were surveyed between December 2023 and March 2024 to compare the differences in demand for AI nursing robot functionalities among the visitors.

A comparative analysis of the Kano attribute quadrant diagrams showed that visitors seeking hospitalization prioritized functional aspects that enhance medical activities. In contrast, visitors attending outpatient examinations focused more on functional points that assist in medical treatment. Additionally, visitors whose purpose was companionship and care emphasized functional aspects that offer psychological and life support to patients.

AI nursing robots serve various functional areas and cater to diverse audience groups. In the future, it is essential to thoroughly consider users' functional needs and implement targeted functional developments to maximize the effectiveness of AI nursing robots.

Mobile collaborative intelligent nursing robots have gained significant attention in the healthcare sector as an innovative solution to address the challenges posed by the increasing aging population and limited medical resources. This article provides a comprehensive overview of the research advancements in this field, covering hospital care, home older adults care, and rehabilitation assistance. In hospital settings, these robots assist healthcare professionals in tasks such as patient monitoring, medication management, and bedside care. For home older adults care, they enhance the older adults sense of security and quality of life by offering daily life support and monitoring. In rehabilitation, these robots provide services such as physical rehabilitation training and social interaction to facilitate patient recovery. However, the development of intelligent nursing robots faces challenges in technology, ethics, law, and user acceptance. Future efforts should focus on improving robots' perceptual and cognitive abilities, enhancing human-robot interaction, and conducting extensive clinical experiments for broader applications.

This study examined the anxiety levels of nurses and nurse candidates regarding humanoid nurse robots and artificial intelligence health technologies in perioperative patient care.

Descriptive and cross-sectional study.

The research was conducted with 158 intern students and 167 surgical nurses. Socio-demographic characteristics form, Questions Form Regarding Humanoid Nurse Robots and Artificial Intelligence Health Technologies, Artificial Intelligence Anxiety Scale and The Medical Artificial Intelligence Preparedness Scale were used. The independent t-test and one-way analysis of variance (ANOVA) were used. This study complied with Appendix S1.

The total scores on the Artificial Intelligence Anxiety Scale for nurses and nursing students are 73.089 ± 31.667 and 73.624 ± 28.029, respectively. The total scores on the Artificial Intelligence Readiness Scale for nurses and nursing students are 71.736 ± 15.064 and 72.183 ± 13.714, respectively. When comparing the sociodemographic characteristics and scale scores of nurses, a statistically significant difference was found between age and the Artificial Intelligence Anxiety Scale scores (p < 0.05). There was also a statistically significant difference between age, gender and work duration and the Artificial Intelligence Readiness Scale scores for nurses (p < 0.05).

Both groups exhibited moderate levels of anxiety and readiness regarding artificial intelligence. Comprehensive research is needed to elucidate the impact of artificial intelligence technologies on nursing professionals.

The proper use of Artificial Intelligence technologies can enhance the quality of patient care, alleviate the workload, increase patient and staff satisfaction and foster new perspectives on acceptance. With their integration into clinics, a patient-centred care environment will emerge, improving patient safety, outcomes and overall well-being. Thus, the anxieties of nurses and students towards artificial intelligence technologies will decrease, and their readiness will increase.

No Patient or Public Contribution.

The integration of AI and robotics in healthcare raises concerns, and additional issues regarding autonomous systems are anticipated. Effective communication is crucial for robots to be seen as "caring", necessitating advanced mechatronic design and natural language processing (NLP). This paper examines the potential of humanoid robots to autonomously replicate compassionate care. The study employs computational simulations using mathematical and agent-based modeling to analyze human-robot interactions (HRIs) surpassing Tetsuya Tanioka's TRETON. It incorporates stochastic elements (through neuromorphic computing) and quantum-inspired concepts (through the lens of Martha Rogers' theory), running simulations over 100 iterations to analyze complex behaviors. Multisensory simulations (visual and audio) demonstrate the significance of "dynamic communication", (relational) "entanglement", and (healthcare system and robot's function) "superpositioning" in HRIs. Quantum and neuromorphic computing may enable humanoid robots to empathetically respond to human emotions, based on Jean Watson's ten caritas processes for creating transpersonal states. Autonomous AI humanoid robots will redefine the norms of "caring". Establishing "pluralistic agreements" through open discussions among stakeholders worldwide is necessary to align innovations with the values of compassionate care within a "posthumanist" framework, where the compassionate care provided by Level 4 robots meets human expectations. Achieving compassionate care with autonomous AI humanoid robots involves translating nursing, communication, computer science, and engineering concepts into robotic care representations while considering ethical discourses through collaborative efforts. Nurses should lead the design and implementation of AI and robots guided by "technological knowing" in Rozzano Locsin's TCCN theory.

Scholars have become increasingly interested in incorporating robots into healthcare. While there is a growing body of research examining nurses' and patients' attitudes towards using robots in healthcare, no prior research has specifically explored their willingness to integrate service robots within the Egyptian healthcare context.

The aim of this study was twofold: (a) to explore the behavioral intentions of nurses to accept robots in their workplace, and (b) to examine the willingness of patients to use service robots in healthcare settings.

A mixed-methods study was conducted. Quantitative data were collected from 301 nurses using the Behavioral Intention to Accept Robots in the Workplace Scale and from 467 patients using the Service Robot Integration Willingness Scale through convenience sampling at three tertiary public hospitals in Port Said, Egypt. Qualitative data were obtained through in-depth, semi-structured interviews with 16 nurses, focusing on their perspectives and concerns regarding robot integration. Descriptive analyses were used to analyze quantitative data, and thematic analysis was used to analyze qualitative data.

Quantitative results indicated a moderate level of behavioral intention to use robots among nurses. Patients demonstrated low willingness to use service robots. In the qualitative analysis of the data obtained from the interviews with nurses, three categories (Concerns about Robots, Roles and Competencies, and Potential Benefits) and eight themes (interaction and emotions, maintenance and reliability, job insecurity, role clarity, competence in critical care, trustworthiness, reducing physical strain, and specialized applications) were identified.

The results of this study indicate that nurses' behavioral intention to accept service robots in healthcare settings is moderate and their acceptance is influenced by various factors related to their concerns about robots, roles and competencies, and potential benefits they could gain. Patients showed a low level of willingness to use service robots in healthcare settings.

Providing targeted educational programs to nurses and patients, assuring them with the provision of robust maintenance protocols, enhancing their confidence in the capabilities of robots, and defining clear roles for robots are crucial for the successful integration of robots into healthcare settings.

To evaluate nurses' workload during the coronavirus disease 2019 (COVID-19) pandemic and to identify their perception of medical serviced robots (MSRs) that may help with or replace the tasks of nurses in negative-pressure isolated wards and general wards.

Cross-sectional survey.

A researcher-made questionnaire was applied that assessed nurses' current workload and their attitudes towards and perceptions of MSRs. A visual analogue scale (VAS) from 1 to 10 was used to assess workload. Perceptions evaluated on a 5-point Likert scale ranging from one point for 'strongly disagree' to five points for 'strongly agree'. A higher VAS score indicated a higher workload. On the other hands, a higher Likert score indicated a more positive perception and three points was neutral. The questionnaire was conducted on 150 nurses in negative-pressure isolated wards for the management of COVID-19 and 150 nurses in general wards. Quota sampling technique was used as sampling technique. Data analysis was performed through independent t-tests, chi-square tests and two-tailed tests. The p-value <0.05 was interpreted to statistically significant.

Two hundred eighty-two participants responded and 142 belonged to the negative-pressure isolated ward. The overall response rate was 94%, and 94.7% in the negative isolated ward. The mean score ± standard deviation for nursing-related psychological stress at the current work site was 7.18 ± 1.58 points, and the mean score for physical workload was 7.65 ± 1.48. The need for MSRs was rated as 3.66 ± 0.86 out of 5. Overall, a positive attitude towards MSRs was confirmed, with no difference between ward groups. The overall ratings were 3.14 ± 1.15 for perceived availability and 3.26 ± 1.13 for perceived efficiency. Both nurse groups perceived that MSRs were most available and efficient for monitoring and measurements. Nurses in negative-pressure isolated wards perceived MSRs more positively than did nurses in general wards regarding setting of alarms (p = 0.003) and delivery of medical devices/materials (p = 0.013). Based on these results, functional development of MSRs associated with monitoring, measurements, setting of alarms and delivery should be prioritized. No patient or public contribution.

Hu et al explored the impact of comprehensive nursing model on swallowing function and quality of life in patients with ischemic stroke. They divided 172 patients into the control group (routine care) and the research group (comprehensive care), and used standard scales to evaluate the swallowing function, neurological deficit, anxiety and depression, daily living ability, and exercise of the two groups of patients before and after care. Changes in indicators of function, quality of life, and compliance. The results showed that compared with the control group, patients in the study group achieved significant improvements in various indicators, with a lower incidence of adverse reactions and higher satisfaction with care. These data suggest that the comprehensive nursing model can significantly improve the swallowing function, quality of life and satisfaction of patients with ischemic stroke. In the future, the development of comprehensive nursing models needs to focus on technological innovation, humanized services, continuing education and training, multi-disciplinary collaboration, optimal allocation of resources, standardized practice and evaluation, etc., in order to improve nursing effects and promote the improvement of medical service quality.

As the disabled population ages and the demand for care increases, Socially, the need for care robots is emerging but, perceptions of care robots among care recipients is unknown.

To determine the level of intention to use care robots among care recipients and identify predictors of intention to use care robots.

A cross-sectional survey was conducted using a convenience sample of 163 persons with disabilities from January to March 2022 at the Veterans Health Service Medical Center.

Overall, 64.42% of respondents intended to use care robots. Predictors included perceived behavioral control, participants' perceptions of the caregiver's burden, attitude toward robot use, subjective norms, and age.

These findings suggest that individuals who are community-dwelling desire the use care robots to maintain their independence and may provide useful insignt for the introduction various care robots in acute care and long-term care settings.

OCCUPATIONAL APPLICATIONSWe used a survey to evaluate the perceptions of nurses and nursing students on robotic technology for nursing care before and after reviewing an educational video that included examples of medical, care, and healthcare service robotic technology. We found that the perception of robotic technology was innately favorable and became more favorable after the video. It is beneficial for engineers to incorporate nurses' frontline knowledge into the design process from the beginning, while functional changes can be implemented since nurses comprise the largest group of healthcare professionals in hospitals and are the end users of technological devices. Educating nurses in state-of-the-art technology specific to what designers are developing can enable them to provide relevant insight. Designers and engineers can use this insight to create user-friendly, effective technology that improves not only patient care but also nurse job satisfaction.

The surge in older demographics has inevitably resulted in a heightened demand for health care, and a shortage of nursing staff is impending. Consequently, there is a growing demand for the development of nursing robots to assist patients with urinary and bowel elimination. However, no study has examined nurses' opinions of smart devices that provide integrated nursing for patients' urinary and bowel elimination needs.

This study aimed to evaluate the feasibility of the Smart Excretion Care System tethered to electronic medical records in a tertiary hospital and community care setting and discuss the anticipated reductions in the burden of nursing care.

Focus group interviews were conducted using the Consolidated Criteria for Reporting Qualitative Research (COREQ) guidelines. The interviews were conducted in March 2021 and involved 67 nurses who had worked at Seoul National University Bundang Hospital for more than 1 year and had experience in assisting patients with excretion care. Data were collected using purposive and snowball sampling methods.

A total of four themes relevant to the Smart Excretion Care System were found: (1) expected reductions in the burden of nursing care, (2) applicable indications (by departments and diseases), (3) preferred features/functions, and (4) expected benefits of using the Smart Excretion Care System in clinical facilities. Nurses from comprehensive nursing care wards had the highest burden when it came to excretion care. It was a common opinion that the Smart Excretion Care System would be very useful in intensive care units and should be applied first to patients with stroke or dementia.

Excretion care is one of the most burdensome tasks for nurses, increasing their workload. The development of the Smart Excretion Care System as a digital health intervention could help improve nurses' work efficiency, reduce their burden, and extend to caregivers and guardians.

Inpatients commonly experience problems with elimination due to incontinence, urinary retentions, and complications with indwelling catheters. Although elimination care (EC) is an important nursing area, few studies explore the burden of EC on nurses.

To identify the burden on EC by analyzing nurses' opinions using sequential explanatory mixed method.

This research was conducted using a sequential explanatory mixed-methods design. A total of 59 nurses at a tertiary hospital in South Korea participated in the study from January 1 to March 31, 2022. For quantitative analysis, information about number of delays of work due to EC, required time for serving bedpan or diaper changes, percentage of EC per shift, and percentage of patients who need EC was collected through a survey. For qualitative analysis, focus group interviews were conducted to identify factors that put a burden on EC. Important themes were derived by analyzing nurses' opinions on EC.

For nurses in intensive care units, general wards, and integrated nursing care wards, the number of work delays due to EC was 3.6 ± 1.5, 2.3 ± 1.2, and 4.8 ± 2.4 (p<0.01), respectively. The mean percentage of EC work out of total nursing tasks per shift was 36.2 ± 19.0, 29.3 ± 14.4, and 43.8 ± 14.1 (p=0.02), respectively. The mean percentage of patients requiring EC out of patients a nurse cares was 85.4 ± 16.6, 41.3 ± 26.1, and 58.8 ± 21.9 (p<0.01), respectively. Following qualitative analysis, four themes related to nurses' EC burden were derived: physical burden, frequent care needs, delay of other jobs due to EC, and complications. Among them, frequent care needs were found to be the primary factor requiring consideration to reduce nurses' burden.

This research found that EC is one of the most burdensome tasks that nurses want to avoid. To alleviate their burden, effective EC protocol or smart medical devices assisting with EC should be developed.

The healthcare sector has undergone significant transformation due to the rapid advancements in artificial intelligence and biotechnologies, presenting both opportunities and threats to the nursing profession. Posthumanism, as a critical perspective challenging anthropocentrism and emphasizing the increasingly blurred boundaries between humans and nonhumans, provides a novel lens to comprehend these technological advancements. In this commentary paper, I draw on the posthuman discourse to argue that in light of these technological forces, we need to contemplate the core values and fundamental patterns of knowing within the nursing discipline, reconfigure nursing scope, redefine its relations with other agents, and embrace a technology-integrated ecosystem of care.

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.

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.

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.

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.

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.

Although the older population has been rapidly growing, the availability of formal caregivers remains limited. Assistance provided by care robots has helped lower this burden; however, whether using a care robot while providing excretion care (EC) is quantitatively increasing or decreasing caregivers' physical care burden has not been extensively studied. This study aimed to quantitatively compare the physical burden experienced by caregivers while providing manual excretion care (MC) using a paper diaper versus robot-aided care (RC). Ten formal caregivers voluntarily participated in the experiment. MC and RC tasks were structuralized according to phases and classified by characteristics. The experiment was conducted in a smart care space. The physical load of formal caregivers was estimated by muscular activity and subjective rating of perceived physical discomfort. The results demonstrated that although the physical load on the lower back and upper extremities during the preparation and post-care phases were greater in RC than MC, RC markedly alleviated caregivers' physical load when performing front tasks. In the preparation-care phases, the physical loads on the lower back and upper extremities were approximately 40.2 and 39.6% higher in the case of RC than MC, respectively. Similar to the preparation-care phases, the physical loads on the lower back and upper extremities during post-care phases were approximately 39.5 and 61.7% greater in the case of RC than MC, respectively. On the other hand, in the front-care phases, the physical loads on the lower back and upper extremities were approximately 25.6 and 34.9% lower in the case of RC than MC, respectively. These findings can quantitatively explain the effectiveness and features of a care robot to stakeholders and provide foundational research data for the development of EC robots. This study emphasizes the implementation and promotion of the dissemination, popularization, and development of care robots to fulfill formal caregiving needs.

Care robots have the potential to address the challenge of aging societies, such as labor shortages or the aging workforce. While previous studies have focused mainly on the productivity or workability of care robots, there has been an increasing need to understand the social value of care robots. This study attempted to identify the social values of care robots by conducting focus group interviews (FGIs) with twenty-four care recipients and caregivers and by using analytic hierarchy processes (AHPs) with thirteen individuals with expertise in the care service and care robot industries. Our results show that the labor- and health-related benefits, the technology innovation, and the provision of essential care work have the highest importance among the criteria of care robots' social values. The criteria that receive lowest priority are cost, the autonomy and needs of the care recipients, and the organizational innovation. Our study suggests that along with the private benefits and costs of care robots, their social values also need to be considered to improve the quality of care and to unlock the potential of the care robot industries.

Objective This scoping review maps the approach undertaken by nurses to influence the implementation of artificial intelligence in health care. It also provides evidence of how frequently nurses drive the implementation of artificial intelligence, and how often nurses collaborate within the technical team. Methods A systematic search using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines was undertaken from 24 July to 22 August 2020 to identify six records that met the inclusion criteria. Results Nurses influenced the implementation of artificial intelligence in health care by: problem solving; articulating contextual needs and priorities; providing real-world insight and solutions; providing examples of implementation; and determining end user satisfaction. There was one instance of nurses driving implementation, and four instances of nurses collaborating with a technical team approach. Conclusion The expertise of nurses must be sought to ensure artificial intelligence can effectively meet the highly context-specific demands of the healthcare environment.

To examine the relationships among effort ensuring robots' smooth operation (EERSO), time pressure, missed care, and nurses' turnover intention, and how robot performance moderates such relations.

Robots may reduce nurses' workload but typically still require some effort of nurses for robots' smooth operation. However, the negative impact of EERSO on nurses' workplace outcomes is unknown.

This study used a two-wave follow-up design. Data were collected in a medical center in Taiwan, with first wave collected in 2019 and second wave collected between 2019 and 2020. A total of 331 participants were followed through the two waves.

EERSO is positively linked to missed care and time pressure. Time pressure is also positively linked to missed care and turnover intention. Positive robot performance weakens the positive link between EERSO and time pressure.

Using robots may help reduce nurses' workload, but it also requires nurses' efforts to maintain robots' continuous operation, that is, EERSO. It may adversely impact nursing professional workplaces.

Nurse managers should reduce nurses' time pressure whilst suggest hospital managers to seek robots that require minimal EERSO.

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.

Due to the COVID pandemic and technological innovation, robots gain increasing role in nursing services. While studies investigated negative attitudes of nurses towards robots, we lack an understanding of nurses' preferences about robot characteristics. Our aim was to explore how key robot features compare when weighed together.

Cross-sectional research design based on a conjoint analysis approach. Robot dimensions tested were: (1) communication; (2) look; (3) safety; (4) self-learning ability; and (5) interactive behaviour. Participants were asked to rank robot profile cards from most to least preferred.

In order of importance, robot's ability to learn ranked first followed by behaviour, look, operating safety and communication. Most preferred robot combination was 'robot responds to commands only, looks like a machine, never misses target, runs programme only and behaves friendly'.

Robot self-learning capacity was least favoured by nurses showing potential fear of robots taking over core nurse competencies.

Population ageing, the rise of chronic diseases and the emergence of new viruses are some of the factors that contribute to an increasing share of gross domestic product dedicated to health spending. COVID-19 has shown that nursing staff represents the critical part of hospitalisation. Technological developments in robotics and artificial intelligence can significantly reduce costs and lead to improvements in many hospital processes. The proposed study aims to assess expectations, attitudes and ethical acceptability regarding the integration of socially assistive humanoid robots into hospitalised care workflow from patients' and healthcare professionals' perspectives and to compare them with the results of similar studies.

The study is designed as a cross-sectional survey, which will include three previously validated questionnaires, the Technology-Specific Expectation Scale (TSES), the Ethical Acceptability Scale (EAS) and the Negative Attitudes towards Robots Scale (NARS). The employees of a regional clinical centre will be asked to participate via an electronic survey and respond to TSES and EAS questionaries. Patients will respond to TSES and NARS questionaries. The survey will be conducted online.

Ethical approval for the study was obtained by the Medical Ethics Commission of the University Medical Center Maribor. Results will be published in a relevant scientific journal and communicated to participants and relevant institutions through dissemination activities and the ecosystem of the Horizon 2020 funded project HosmartAI (grant no. 101016834).

06 May 2021.

December 2021.

With robots being introduced into caregiving, particularly for older persons, various ethical concerns are raised. Among them is the fear of replacing human caregiving. While ethical concepts like well-being, autonomy, and capabilities are often used to discuss these concerns, this paper brings forth the concept of social dignity to further develop guidelines concerning the use of robots in caregiving. By social dignity, we mean that a person's perceived dignity changes in response to certain interactions and experiences with other persons. In this paper, we will first present the concept of social dignity, and then identify a niche where robots can be used in caregiving in an ethical manner. Specifically, we will argue that, because some activities of daily living are performed in solitude to maintain dignity, a care recipient will usually prefer robotic assistance instead of human assistance for these activities. Secondly, we will describe how other philosophical concepts, which have been commonly used to judge robotic assistance in caregiving for the elderly so far, such as well-being, autonomy, and capabilities, are less useful in determining whether robotic assistance in caregiving is ethically problematic or not. To conclude, we will argue that social dignity offers an advantage to the other concepts, as it allows to ask the most pressing questions in caregiving.

Robotics is one of the most emerging technologies today, and are used in a variety of applications, ranging from complex rocket technology to monitoring of crops in agriculture. Robots can be exceptionally useful in a smart hospital environment provided that they are equipped with improved vision capabilities for detection and avoidance of obstacles present in their path, thus allowing robots to perform their tasks without any disturbance. In the particular case of Autonomous Nursing Robots, major essential issues are effective robot path planning for the delivery of medicines to patients, measuring the patient body parameters through sensors, interacting with and informing the patient, by means of voice-based modules, about the doctors visiting schedule, his/her body parameter details, etc. This paper presents an approach of a complete Autonomous Nursing Robot which supports all the aforementioned tasks. In this paper, we present a new Autonomous Nursing Robot system capable of operating in a smart hospital environment area. The objective of the system is to identify the patient room, perform robot path planning for the delivery of medicines to a patient, and measure the patient body parameters, through a wireless BLE (Bluetooth Low Energy) beacon receiver and the BLE beacon transmitter at the respective patient rooms. Assuming that a wireless beacon is kept at the patient room, the robot follows the beacon's signal, identifies the respective room and delivers the needed medicine to the patient. A new fuzzy controller system which consists of three ultrasonic sensors and one camera is developed to detect the optimal robot path and to avoid the robot collision with stable and moving obstacles. The fuzzy controller effectively detects obstacles in the robot's vicinity and makes proper decisions for avoiding them. The navigation of the robot is implemented on a BLE tag module by using the AOA (Angle of Arrival) method. The robot uses sensors to measure the patient body parameters and updates these data to the hospital patient database system in a private cloud mode. It also makes uses of a Google assistant to interact with the patients. The robotic system was implemented on the Raspberry Pi using Matlab 2018b. The system performance was evaluated on a PC with an Intel Core i5 processor, while the solar power was used to power the system. Several sensors, namely HC-SR04 ultrasonic sensor, Logitech HD 720p image sensor, a temperature sensor and a heart rate sensor are used together with a camera to generate datasets for testing the proposed system. In particular, the system was tested on operations taking place in the context of a private hospital in Tirunelveli, Tamilnadu, India. A detailed comparison is performed, through some performance metrics, such as Correlation, Root Mean Square Error (RMSE), and Mean Absolute Percentage Error (MAPE), against the related works of Deepu et al., Huh and Seo, Chinmayi et al., Alli et al., Xu, Ran et al., and Lee et al. The experimental system validation showed that the fuzzy controller achieves very high accuracy in obstacle detection and avoidance, with a very low computational time for taking directional decisions. Moreover, the experimental results demonstrated that the robotic system achieves superior accuracy in detecting/avoiding obstacles compared to other systems of similar purposes presented in the related works.

Artificial intelligence (AI) is set to transform the health system, yet little research to date has explored its influence on nurses-the largest group of health professionals. Furthermore, there has been little discussion on how AI will influence the experience of person-centered compassionate care for patients, families, and caregivers.

This review aims to summarize the extant literature on the emerging trends in health technologies powered by AI and their implications on the following domains of nursing: administration, clinical practice, policy, and research. This review summarizes the findings from 3 research questions, examining how these emerging trends might influence the roles and functions of nurses and compassionate nursing care over the next 10 years and beyond.

Using an established scoping review methodology, MEDLINE, CINAHL, EMBASE, PsycINFO, Cochrane Database of Systematic Reviews, Cochrane Central, Education Resources Information Center, Scopus, Web of Science, and ProQuest databases were searched. In addition to the electronic database searches, a targeted website search was performed to access relevant gray literature. Abstracts and full-text studies were independently screened by 2 reviewers using prespecified inclusion and exclusion criteria. Included articles focused on nursing and digital health technologies that incorporate AI. Data were charted using structured forms and narratively summarized.

A total of 131 articles were retrieved from the scoping review for the 3 research questions that were the focus of this manuscript (118 from database sources and 13 from targeted websites). Emerging AI technologies discussed in the review included predictive analytics, smart homes, virtual health care assistants, and robots. The results indicated that AI has already begun to influence nursing roles, workflows, and the nurse-patient relationship. In general, robots are not viewed as replacements for nurses. There is a consensus that health technologies powered by AI may have the potential to enhance nursing practice. Consequently, nurses must proactively define how person-centered compassionate care will be preserved in the age of AI.

Nurses have a shared responsibility to influence decisions related to the integration of AI into the health system and to ensure that this change is introduced in a way that is ethical and aligns with core nursing values such as compassionate care. Furthermore, nurses must advocate for patient and nursing involvement in all aspects of the design, implementation, and evaluation of these technologies.

RR2-10.2196/17490.

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.

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

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.

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).

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.

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.

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.

CRD42018092866.

Future nursing shortages and advanced technological developments mean that robots and automated devices could play a valuable role in nursing, but little has been published on their use, and outcomes, to date.

This integrative review identified how robots are currently used in nursing and the outcomes of those initiatives.

This study used integrative review using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses.

We searched the CINAHL, PubMed, Web of Science, and Scopus databases for peer-reviewed papers published in English from January 2010 to August 2018.

The five-stage review process by Whittemore and Knafl was used.

The 25 included papers showed that robots and automated devices were mainly used in nursing to deliver medication, monitor patients, and provide nursing treatments. The outcomes were evaluated in relation to patient safety, working time and workload, usability, and the end users' satisfaction. In addition, the costs, care outcomes, nurses' behaviour, and changes in working procedures were considered.

Robots and automated devices have the potential to develop nurses' work, but more research and critical evaluations are needed to find the most suitable devices and focus on the functions that will provide the best outcomes for nurses' work.