Can nurses remain relevant in a technologically advanced future?

Can digital leadership transform AI anxiety and attitude in nurses?

BACKGROUND

The lack of artificial intelligence applications in nursing education and the nursing profession in Turkey and the need for strategies for integrating artificial intelligence into the nursing profession continues. At this point, there is a need to transform the negative attitudes and anxiety that may occur in nurses.

OBJECTIVES

It was aimed to reorganize the professional transformation in this parallel by analyzing the effect of digital leadership perception, which is explained as how nurses approach digital technologies and innovations and their awareness of how and with which methods they can use these technologies on artificial intelligence anxiety and attitude in the nursing profession.

DESIGN

The study was designed as descriptive, correlational, and cross-sectional.

PARTICIPANTS

The research was conducted by reaching 439 nurses working in hospitals operating in three different regions of Turkey by simple random sampling method.

METHODS

In the first part of the data collection tool used in this study, digital leadership scale, artificial intelligence use anxiety, and artificial intelligence attitude scales were used, including questions determining the demographic information of nurses, their relationship with technology, artificial intelligence usage status and its importance in the profession.

RESULTS

It was determined that 29.8% of the nurses had a good relationship with technology, 66.3% knew about using artificial intelligence in health, and 27.3% wanted it to be more involved in their lives. It was determined that nurses' perceptions of digital leadership were at a medium level of 46.9% and a high level of 41.7%, 82.7% had a positive attitude towards artificial intelligence, and 82.7% had low or medium level anxiety when their artificial intelligence anxiety status was examined. There was a significant and negative relationship between digital leadership and AI anxiety (r = -0.434; p < 0.01), a significant and positive relationship between digital leadership and AI attitude (r = 0.468; p < 0.01), and a significant and negative relationship between AI attitude and AI anxiety (r = -0.629; p < 0.01). Finally, it was determined that nurses' perception of digital leadership indirectly affected AI anxiety through AI attitude (β = -0.230, 95% CI [-0.298, -0.165]).

CONCLUSION

It is suggested that the anxiety and attitude towards artificial intelligence can be transformed positively with the effect of digital leadership, and in this parallel, the digital leadership phenomenon should be evaluated as a practical implementation strategy in integrating artificial intelligence into the nursing profession.

CLINICAL RELEVANCE

Our study showed that artificial intelligence attitude has a mediating role in the indirect effect of the perception of digital leadership in nursing on AI anxiety. It was determined that nurses' digital leadership perception, artificial intelligence anxiety, and artificial intelligence attitude differed significantly with demographic variables.

Nurse managers' managerial innovation and it's relation to proactivity behavior and locus of control among intensive care nurses

BACKGROUND

The nursing profession is undergoing rapid transformation, requiring innovation in management approaches and proactive behaviors among staff. Nurse Managers play a vital role through managerial innovation, but its impacts on intensive care nurses' proactivity and locus of control remain underexplored.

OBJECTIVES

This study aimed to assess the levels of Nurse Managers' managerial innovation and relate it to proactivity behaviors and locus of control orientations among intensive care nurses.

METHODS

A cross-sectional correlational design was adopted, recruiting 242 intensive care nurses from Tanta University Hospital, Egypt. Participants completed standardized questionnaires measuring perceived managerial innovation, proactivity behavior, and locus of control.

RESULTS

Nurse Managers demonstrated moderately high innovation across all dimensions, especially in continuous learning and development (mean = 4.65) and advanced technology use (mean = 4.56). Nurses exhibited sound proactivity levels, particularly in adaptability (mean = 4.40) and planning (mean = 4.35). The majority of nurses showed an internal locus of control (64.5%). Managerial innovation had significant positive correlations with nurses' proactivity (r = 0.45, p < 0.001) and internal locus of control (r = 0.42, p < 0.001). Regression analysis revealed age, gender, experience, education, and ICU type as significant predictors of proactivity and locus of control.

CONCLUSION

Innovative nursing leadership positively influences staff's proactivity levels and perceived control over their practice. This underscores the vital role of nurse managers in creating empowering environments in intensive care.

How does training given to pediatric nurses about artificial intelligence and robot nurses affect their opinions and attitude levels? A quasi-experimental study

PURPOSE

This study was conducted to investigate the effect of training provided to pediatric nurses on their knowledge and attitude levels about artificial intelligence and robot nurses.

DESIGN AND METHODS

In this study, a single-group pre- and post-test quasi-experimental design was used. Data were collected from pediatric nurses working in Training and Research Hospital located in western Turkey. Forty-three pediatric nurses participated in the study. The study data were collected using the "Pediatric Nurses' Descriptive Characteristics Form", "Artificial Intelligence Knowledge Form", and "Artificial Intelligence General Attitude Scale".

RESULTS

The mean scores of the participating pediatric nurses obtained from the Artificial Intelligence Knowledge Form before, right after and one month after the training were 41.16 ± 14.95, 68.25 ± 13.57 and 69.06 ± 13.19, respectively. The mean scores they obtained from the Positive Attitudes towards Artificial Intelligence subscale of the Artificial Intelligence General Attitude Scale before and after the training were 3.43 ± 0.54 and 3.59 ± 0.60, respectively whereas the mean scores they obtained from its Negative Attitudes towards Artificial Intelligence subscale were 2.68 ± 0.67 and 2.77 ± 0.75, respectively.

CONCLUSIONS

It was determined that the training given to the pediatric nurses about artificial intelligence and robot nurses increased the nurses' knowledge levels and their artificial intelligence attitude scores, but this increase in the artificial intelligence attitude scores was not significant.

PRACTICE IMPLICATIONS

The use of artificial intelligence and robotics or advanced technology in pediatric nursing care can be fostered.

HOTSPOT: An ad hoc teamwork platform for mixed human-robot teams

Ad hoc teamwork is a research topic in multi-agent systems whereby an agent (the "ad hoc agent") must successfully collaborate with a set of unknown agents (the "teammates") without any prior coordination or communication protocol. However, research in ad hoc teamwork is predominantly focused on agent-only teams, but not on agent-human teams, which we believe is an exciting research avenue and has enormous application potential in human-robot teams. This paper will tap into this potential by proposing HOTSPOT, the first framework for ad hoc teamwork in human-robot teams. Our framework comprises two main modules, addressing the two key challenges in the interaction between a robot acting as the ad hoc agent and human teammates. First, a decision-theoretic module that is responsible for all task-related decision-making (task identification, teammate identification, and planning). Second, a communication module that uses natural language processing to parse all communication between the robot and the human. To evaluate our framework, we use a task where a mobile robot and a human cooperatively collect objects in an open space, illustrating the main features of our framework in a real-world task.

Nurses' knowledge of, attitudes towards and awareness of the metaverse, and their future time perspectives: a cross-sectional study

BACKGROUND

The metaverse is a new and developing technology used in the field of healthcare. The perception of future explains time as a psychological phenomenon rather than a physical one. This study aimed to determine nurses' thoughts of the metaverse and their perceptions of future.

METHODS

The study in which the cross-sectional descriptive design was used was conducted with nurses working in a hospital in Trkiye from September 2022 to December 2022. Face-to-face interviews were conducted with 374 nurses who were chosen using the convenience sampling method. Personal Identification Form, Metaverse Scale (MS) and Future Time Perspective Scale (FTPS) were used to collect data. The Statistical Package for Social Sciences (SPSS) for Windows 25.0 program was used to analyse the data.

RESULTS

The findings revealed that 81.6% of the nurses believed that they could provide patient education using the metaverse in the future, whereas 46% believed that they could do virtual nursing. The mean scores obtained from the FTPS and MS by the nurses were 3.45 (SD = 0.37) and 3.74 (SD = 0.56), respectively. There was a weak positive relationship between perception of future, and knowledge of, attitudes towards and awareness of the metaverse (r = 0.157, p = 0.002), and a weak, positive relationship between internet use duration and MS (r = 0.169, p = 0.001).

CONCLUSIONS

This study underscores the potential of the metaverse in nursing, revealing that nurses are optimistic about its application in patient education and virtual care. We recommend the development of specialized training programs to equip nurses with the necessary skills and knowledge to effectively utilize the metaverse in healthcare settings.

Evolution of Chatbots in Nursing Education: Narrative Review

Background

The integration of chatbots in nursing education is a rapidly evolving area with potential transformative impacts. This narrative review aims to synthesize and analyze the existing literature on chatbots in nursing education.

Objective

This study aims to comprehensively examine the temporal trends, international distribution, study designs, and implications of chatbots in nursing education.

Methods

A comprehensive search was conducted across 3 databases (PubMed, Web of Science, and Embase) following the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) flow diagram.

Results

A total of 40 articles met the eligibility criteria, with a notable increase of publications in 2023 (n=28, 70%). Temporal analysis revealed a notable surge in publications from 2021 to 2023, emphasizing the growing scholarly interest. Geographically, Taiwan province made substantial contributions (n=8, 20%), followed by the United States (n=6, 15%) and South Korea (n=4, 10%). Study designs varied, with reviews (n=8, 20%) and editorials (n=7, 18%) being predominant, showcasing the richness of research in this domain.

Conclusions

Integrating chatbots into nursing education presents a promising yet relatively unexplored avenue. This review highlights the urgent need for original research, emphasizing the importance of ethical considerations.

Developing digital competencies of nursing professionals in continuing education and training - a scoping review

Introduction

The German health and care system is transforming due to advancing digitalization. New technological applications in nursing, such as social and assistance robotics, artificial intelligence and legal framework conditions are increasingly focused in numerous research projects. However, the approaches to digitalization in nursing are very different. When integrating technologies such as robotics and artificial intelligence into nursing, it is particularly important to ensure that ethical and human aspects are taken into account. A structured classification of the development of digitalization in nursing care is currently hardly possible. In order to be able to adequately deal with this digital transformation, the acquisition of digital competences in nursing education programs is pivotal. These include the confident, critical and creative use of information and communication technologies in a private and professional context. This paper focuses on the question which specific training offers already exist at national and international level for nursing professions to acquire digital competences.

Methods

A scoping review according to the PRISMA scheme was conducted in the PubMed and CINAHL databases. The search period for the scoping review extended from 2017 to 2024.

Results

The selection of the studies took place by inclusion and exclusion criteria and the content-related orientation of the publications. After reviewing the titles and abstracts, eight studies were included. Of these, four were published in German-speaking countries and another four in international English-language journals.

Discussion

The topic of digitization of the nursing professions and the question of how nurses can acquire digital competences is gaining international attention. Nevertheless, the research on explicit continuing education programs for nursing professions is still undifferentiated. No specific continuing education offer for the development of digital competences of nursing professionals was identified. Many authors remained at the meta-level when developing methodological concepts for the acquisition of digital competences. The systematic integration of digitalization into higher education and continuing vocational training is mentioned in the publications. The development of theory- and research-based educational frameworks, which can be used as a basis for curricula in nursing studies and continuing education, is highly recommendable.

A Systematic Review of the Application of Artificial Intelligence in Nursing Care: Where are We, and What's Next?

Background

Integrating Artificial Intelligence (AI) into healthcare has transformed the landscape of patient care and healthcare delivery. Despite this, there remains a notable gap in the existing literature synthesizing the comprehensive understanding of AI's utilization in nursing care.

Objective

This systematic review aims to synthesize the available evidence to comprehensively understand the application of AI in nursing care.

Methods

Studies published between January 2019 and December 2023, identified through CINAHL Plus with Full Text, Web of Science, PubMed, and Medline, were included in this review. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines guided the identification, screening, exclusion, and inclusion of articles. The convergent integrated analysis framework, as proposed by the Joanna Briggs Institute, was employed to synthesize data from the included studies for theme generation.

Results

A total of 337 records were identified from databases. Among them, 35 duplicates were removed, and 302 records underwent eligibility screening. After applying inclusion and exclusion criteria, eleven studies were deemed eligible and included in this review. Through data synthesis of these studies, six themes pertaining to the use of AI in nursing care were identified: 1) Risk Identification, 2) Health Assessment, 3) Patient Classification, 4) Research Development, 5) Improved Care Delivery and Medical Records, and 6) Developing a Nursing Care Plan.

Conclusion

This systematic review contributes valuable insights into the multifaceted applications of AI in nursing care. Through the synthesis of data from the included studies, six distinct themes emerged. These findings not only consolidate the current knowledge base but also underscore the diverse ways in which AI is shaping and improving nursing care practices.

Sustainable integration of digitalisation in nursing education-an international scoping review

Introduction

Trainees and teachers at nursing schools as well as nursing professionals are increasingly facing new challenges as a result of the digital transformation. Opportunities for the entire care system exist in the improvement of care quality and communication between those involved. However, this change also harbours risks, such as the use of immature digital applications in the care sector, data theft and industrial espionage. In order to be able to exploit the potential of digitalisation despite these risks, it is necessary to integrate relevant aspects such as digital skills into nursing training. The aim of this study is to investigate the extent to which the sustainable integration of digitalisation in nursing education is discussed.

Methods

The methods of the systematic literature and database search were carried out in the form of a scoping review according to the PRISMA scheme. The PubMed and CINAHL databases were used for this purpose. The search period covered the years 2017-2023.

Findings

After screening the titles and abstracts using inclusion and exclusion criteria, 13 studies were included in the synthesis of findings. The international literature focuses on content areas that highlight trends in digitalisation-related training in nursing. These focal points include concept development, considering the heterogeneity of demand constellations, as well as the reflexive reorientation of existing competences, whereby the technological competence of teachers is not disregarded. Other focal points relate to the initiation of digital skills in training and maintaining the employability of older nursing staff through professional development.

Discussion

The literature research shows that there is a rudimentary discussion about digitalisation and curricular developments in nursing training in an international context, while the discourse in the German-language literature is less advanced. Among the sustainability desiderata derived from the literature is the involvement of nursing professionals in the development, testing and implementation of digital technologies. Only through active cooperation between nursing professionals and nursing sciences can the topic of digitalisation be integrated into the education and training of professional nursing in a targeted and future-oriented manner, whereby the focus should always be on the ability to deal with digital technologies and the associated change.

National survey on understanding nursing academics' perspectives on digital health education

AIM

This study explored the knowledge and confidence levels of nursing academics in teaching both the theories and practical skills of digital health in undergraduate nursing programs.

DESIGN

A cross-sectional study.

METHODS

A structured online survey was distributed among nursing academics across Australian universities. The survey included two sections: (1) the participants' demographics and their nursing and digital health teaching experience; (2) likert scales asking the participants to rate their knowledge and confidence to teach the theories and practical skills of four main themes; digital health technologies, information exchange, quality and digital professionalism.

RESULTS

One hundred and nineteen nursing academics completed part one, and 97 individuals completed part two of the survey. Only 6% (n = 5) of the participants reported having formal training in digital health. Digital health was mainly taught as a module (n = 57, 45.9%), and assessments of theory or practical application of digital health in the nursing curriculum were uncommon, with 79 (69.9%) responding that there was no digital health assessment in their entry to practice nursing programs. Among the four core digital health themes, the participants rated high on knowledge of digital professionalism (22.4% significant knowledge vs. 5.9% no knowledge) but low on information exchange (30% significant knowledge vs. 28.3% no knowledge). Statistically significant (p < .001) associations were found between different themes of digital health knowledge and the level of confidence in teaching its application. Nursing academics with more than 15 years of teaching experience had a significantly higher level of knowledge and confidence in teaching digital health content compared with those with fewer years of teaching experience.

CONCLUSION

There is a significant gap in nursing academics' knowledge and confidence to teach digital health theory and its application in nursing. Nursing academics need to upskill in digital health to prepare the future workforce to be capable in digitally enabled health care settings.

IMPLICATIONS FOR THE PROFESSION

Nursing academics have a limited level of digital knowledge and confidence in preparing future nurses to work in increasingly technology-driven health care environments. Addressing this competency gap and providing sufficient support for nursing academics in this regard is essential.

IMPACT

What problem did the study address? Level of knowledge and confidence among nursing academics to teach digital health in nursing practice. What were the main findings? There is a significant gap in nursing academics' knowledge and confidence to teach digital health theory and its application in nursing. Where and on whom will the research have an impact? Professional nursing education globally.

REPORTING METHOD

The STROBE guideline was used to guide the reporting of the study.

PATIENT OR PUBLIC CONTRIBUTION

The call for participation from nursing academics across Australia provided an introductory statement about the project, its aim and scope, and the contact information of the principal researcher. A participant information sheet was shared with the call providing a detailed explanation of participation. Nursing academics across Australia participated in the survey through the link embedded in the participation invite.

Towards human-centered AI and robotics to reduce hospital falls: finding opportunities to enhance patient-nurse interactions during toileting

Introduction: Patients who are hospitalized may be at a higher risk for falling, which can result in additional injuries, longer hospitalizations, and extra cost for healthcare organizations. A frequent context for these falls is when a hospitalized patient needs to use the bathroom. While it is possible that "high-tech" tools like robots and AI applications can help, adopting a human-centered approach and engaging users and other affected stakeholders in the design process can help to maximize benefits and avoid unintended consequences. Methods: Here, we detail our findings from a human-centered design research effort to investigate how the process of toileting a patient can be ameliorated through the application of advanced tools like robots and AI. We engaged healthcare professionals in interviews, focus groups, and a co-creation session in order to recognize common barriers in the toileting process and find opportunities for improvement. Results: In our conversations with participants, who were primarily nurses, we learned that toileting is more than a nuisance for technology to remove through automation. Nurses seem keenly aware and responsive to the physical and emotional pains experienced by patients during the toileting process, and did not see technology as a feasible or welcomed substitute. Instead, nurses wanted tools which supported them in providing this care to their patients. Participants envisioned tools which helped them anticipate and understand patient toileting assistance needs so they could plan to assist at convenient times during their existing workflows. Participants also expressed favorability towards mechanical assistive features which were incorporated into existing equipment to ensure ubiquitous availability when needed without adding additional mass to an already cramped and awkward environment. Discussion: We discovered that the act of toileting served more than one function, and can be viewed as a valuable touchpoint in which nurses can assess, support, and encourage their patients to engage in their own recovery process as they perform a necessary and normal function of life. While we found opportunities for technology to make the process safer and less burdensome for patients and clinical staff alike, we believe that designers should preserve and enhance the therapeutic elements of the nurse-patient interaction rather than eliminate it through automation.

Nursing Perceptions of Robotic Technology in Healthcare: A Pretest-Posttest Survey Analysis Using an Educational Video

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.

Advancing nursing practice with artificial intelligence: Enhancing preparedness for the future

AIM

This article aimed to explore the role of AI in advancing nursing practice, focusing on its impact on readiness for the future.

DESIGN AND METHODS

A position paper, the methodology comprises three key steps. First, a comprehensive literature search using specific keywords in reputable databases was conducted to gather current information on AI in nursing. Second, data extraction and synthesis from selected articles were performed. Finally, a thematic analysis identifies recurring themes to provide insights into AI's impact on future nursing practice.

RESULTS

The findings highlight the transformative role of AI in advancing nursing practice and preparing nurses for the future, including enhancing nursing practice with AI, preparing nurses for the future (AI education and training) and associated, ethical considerations and challenges. AI-enabled robotics and telehealth solutions expand the reach of nursing care, improving accessibility of healthcare services and remote monitoring capabilities of patients' health conditions.

Nursing Informatics Competency and Self-Efficacy in Clinical Practice among Nurses in Palestinian Hospitals

This study purposed to determine the levels of nursing informatics competency and self-efficacy in clinical practice and influencing factors on self-efficacy among Palestinian nurses in hospitals. A descriptive-correlational design was adopted. The nurses who worked in the North West Bank of Palestine (N = 331) were recruited. The data were collected using the Self-Assessment of Nursing Informatics Competencies Scale (SANICS) which consists of 30 items rated on a 5-point Likert scale, ranging from 1(not competent) to 5 (expert), and scored by calculating the mean as follows: novice/low (1.00-2.59), beginner/moderate (2.60-3.39), and competent/high (3.40-5.00); and the New General Self-Efficacy Scale (NGSE) that consists of eight items rated on a 5-point Likert scale, ranging from 1(strongly disagree) to 5(strongly agree) and scored according to the average of the scale, whereas the average of > 3 indicated high self-efficacy, and ≤ 3 reflected low self-efficacy. The data were collected during the period from September to November 2020. Findings showed that the total mean score for the nursing informatics competency scale was 2.9 (SD = 0.7), which indicated that the nurses had a moderate level of nursing informatics competency. The average score for the self-efficacy scale was 3.5 (SD = 0.8), which reflected that nurses had high self-efficacy. Self-efficacy in clinical practice increased with age and with nursing informatics competency. Thus, it is necessary to enhance nurses' informatics competency by developing continuous educational programs about this technology for nurses and engaging nurses in such programs to enhance their competencies in this system.

Nursing Interventions and Care Strategies for Patients with Coronary Heart Disease: A Comprehensive Review

Cardiovascular diseases are a major cause of death worldwide, and coronary heart disease (CHD) is a prevalent cardiovascular condition and a significant health burden for the population. In this disease, insufficient blood flow to the heart due to plaque accumulation in the coronary arteries causes chest pain, heart attack, and even death. So, it is vital to identify risk factors, prevention, appropriate treatment, and rehabilitation. Nurses play an indispensable role in managing and caring for patients with CHD. Indeed, they possess a deep understanding of the disease and its complexities, enabling them to provide comprehensive care to patients. Nurses monitor vital signs, administer medications, and perform diagnostic tests, ensuring patients receive timely and appropriate interventions. They also educate patients and their families about CHD, emphasizing lifestyle modifications, medication adherence, and self-care practices. Moreover, nurses offer emotional support, guiding patients through the physical and psychological challenges associated with CHD. Their expertise, compassion, and dedication significantly improve patient outcomes and overall quality of life. Nurses are responsible for assessing, diagnosing, and counseling patients on how to manage their disease, making them the front line of defense in preventing and addressing this serious condition. In the current study, we reviewed the literature to consider the best practices and emerging trends in nursing interventions and care strategies for patients with CHD.

The Impact of Self-Efficacy on Nurses' Well-Being: Does Digital Competence Matter?

PURPOSE

Drawing on person-environment fit theory and social cognitive theory, this study aimed to examine how self-efficacy affects nurses' workplace well-being via person-job fit and the moderating role of digital competence.

METHODS

A two-wave survey was conducted to collect data. Data were collected from six hundred and ninety-five nurses at three Chinese hospitals between May 2022 and September 2022. We employed hierarchical regression analysis and bootstrapping to analyze the data.

RESULTS

Self-efficacy positively influenced person-job fit (β = .55, p < .001), which positively affected nurses' workplace well-being (β = .32, p < .001). Person-job fit mediated the effect of self-efficacy on nurses' workplace well-being. Additionally, digital competence strengthened the positive impact of self-efficacy on person-job fit (β = .12, p < .001).

CONCLUSION

Recruiting nurses with both self-efficacy and digital competence benefits hospitals. It is critical for nurses to improve their digital competence for achieving person-job fit and attaining workplace well-being in the post-coronavirus disease 2019 (COVID-19) era.

The Synergistic Effect of Nurse Proactive Phone Calls With an mHealth App Program on Sustaining App Usage: 3-Arm Randomized Controlled Trial

BACKGROUND

Although mobile health application (mHealth app) programs have effectively promoted disease self-management behaviors in the last decade, usage rates have tended to fall over time.

OBJECTIVE

We used a case management approach led by a nurse and supported by a health-social partnership team with the aim of sustaining app usage among community-dwelling older adults and evaluated the outcome differences (i.e, self-efficacy, levels of depression, and total health service usages) between those who continued to use the app.

METHODS

This was a 3-arm randomized controlled trial. A total of 221 older adults with hypertension, diabetes, or chronic pain were randomized into 3 groups: mHealth (n=71), mHealth with interactivity (mHealth+I; n=74), and the control (n=76). The mHealth application was given to the mHealth and mHealth+I groups. The mHealth+I group also received 8 proactive calls in 3 months from a nurse to encourage use of the app. The control group received no interventions. Data were collected at preintervention (T1), postintervention (T2), and at 3 months' postintervention (T3) to ascertain the sustained effect.

RESULTS

A total of 37.8% of mHealth+I and 18.3% of mHealth group participants continued using the mHealth app at least twice per week until the end of the sixth month. The difference in app usage across the 2 groups between T2 and T3 was significant (χ²₁=6.81, P=.009). Improvements in self-efficacy (β=4.30, 95% CI 0.25-8.35, P=.04) and depression levels (β=-1.98, 95% CI -3.78 to -0.19, P=.03) from T1 to T3 were observed in the mHealth group participants who continued using the app. Although self-efficacy and depression scores improved from T1 to T2 in the mHealth+I group, the mean values decreased at T3. Health service usage decreased for all groups from T1 to T2 (β=-1.38, 95% CI -1.98 to -0.78, P<.001), with a marginal increase at T3.

CONCLUSIONS

The relatively low rates of mHealth app usage at follow-up are comparable to those reported in the literature. More work is needed to merge the technology-driven and in-person aspects of mHealth.

TRIAL REGISTRATION

ClinicalTrials.gov NCT03878212; https://clinicaltrials.gov/ct2/show/NCT03878212.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

RR2-10.1159/000509129.

Ethics 4.0: Ethical Dilemmas in Healthcare Mediated by Social Robots

This study examined people's moral judgments and trait perception toward a healthcare agent's response to a patient who refuses to take medication. A sample of 524 participants was randomly assigned to one of eight vignettes in which the type of healthcare agent (human vs. robot), the use of a health message framing (emphasizing health-losses for not taking vs. health-gains in taking the medication), and the ethical decision (respect the autonomy vs. beneficence/nonmaleficence) were manipulated to investigate their effects on moral judgments (acceptance and responsibility) and traits perception (warmth, competence, trustworthiness). The results indicated that moral acceptance was higher when the agents respected the patient's autonomy than when the agents prioritized beneficence/nonmaleficence. Moral responsibility and perceived warmth were higher for the human agent than for the robot, and the agent who respected the patient's autonomy was perceived as warmer, but less competent and trustworthy than the agent who decided for the patient's beneficence/nonmaleficence. Agents who prioritized beneficence/nonmaleficence and framed the health gains were also perceived as more trustworthy. Our findings contribute to the understanding of moral judgments in the healthcare domain mediated by both healthcare humans and artificial agents.

Nursing and Humanities as Disciplinary Worlds in Nursing: A Review of Graham McCaffrey's Nursing and Humanities 2020 Book

The author in this article provides a review and critique of Graham McCaffrey's book Nursing and Humanities.

Artificial intelligence-based intelligent surveillance for reducing nurses' working hours in nurse-patient interaction: A two-wave study

AIM

To explore the feasibility of applying artificial intelligence in nurse-patient interaction to assist nurses in grasping patient status and reducing their working hours.

BACKGROUND

Artificial intelligence has been reshaping the health care industry and has immense potential in nursing care, but there is still a lack of suitable artificial intelligence methods to improve the efficiency of the nurse-patient interaction that takes much time of nurses.

METHODS

An artificial intelligence-based intelligent surveillance system was developed to reduce nurses' working hours in nurse-patient interaction, and a two-wave follow-up design was adopted in this study. The data were collected in a nursing home in Guangzhou, China. The first and second waves of data were recorded in the same format on the same patients by the same nurses. The only difference is the deployment of artificial intelligence technology between the two waves of data.

RESULTS

Artificial intelligence-based intelligent surveillance can provide statistical health data for nurses to grasp the patients' status more intuitively, reducing the average nurse-patient interaction time per patient from 18 to 10 min. In addition, artificial intelligence's real-time response to the abnormal health status of patients not only avoids more serious secondary injuries for patients but also prevents nurses from consuming energy in detecting emergencies.

CONCLUSION

The application of artificial intelligence has great potential to reduce nurses' working hours in nurse-patient interaction. There are still many limitations in artificial intelligence technology at this stage, and it is not feasible to completely rely on artificial intelligence. However, as a tool to assist decision-making, it can still have beneficial impacts on nursing management.

IMPLICATIONS FOR NURSING MANAGEMENT

Artificial intelligence has great potential in daily nurse-patient interaction, and nursing facility managers and nurses need to be more open to this new technology.

The role of artificial intelligence in enhancing clinical nursing care: A scoping review

AIM

To present an overview of how artificial intelligence has been used to improve clinical nursing care.

BACKGROUND

Artificial intelligence has been reshaping the healthcare industry but little is known about its applicability in enhancing nursing care.

EVALUATION

A scoping review was conducted. Seven electronic databases (CINAHL, Cochrane Library, EMBASE, IEEE Xplore, PubMed, Scopus, and Web of Science) were searched from 1 January 2010 till 20 December 2020. Grey literature and reference lists of included articles were also searched.

KEY ISSUES

Thirty-seven studies encapsulating the use of artificial intelligence in improving clinical nursing care were included in this review. Six use cases were identified - documentation, formulating nursing diagnoses, formulating nursing care plans, patient monitoring, patient care prediction such as falls prediction (most common) and wound management. Various techniques of machine learning and classification were used for predictive analyses and to improve nurses' preparedness and management of patients' conditions CONCLUSION: This review highlighted the potential of artificial intelligence in improving the quality of nursing care. However, more randomized controlled trials in real-life healthcare settings should be conducted to enhance the rigor of evidence.

IMPLICATIONS FOR NURSING MANAGEMENT

Education in the application of artificial intelligence should be promoted to empower nurses to lead technological transformations and not passively trail behind others.

Artificial intelligence and robot nurses: From nurse managers' perspective: A descriptive cross-sectional study

AIM

This research was planned to identify nurse managers' opinions on artificial intelligence and robot nurses.

BACKGROUND

As the concepts of artificial intelligence and robot nurses are becoming widespread in Turkey, nurse managers are expected to guide and cooperate with nurses in the future in regard to these technologies.

METHODS

The sample of the study consisted of 326 manager nurses, who were reached via the online questionnaire during the period of September to November 2021. A Nurse Managers Information Form and a Question Form on Artificial Intelligence and Robot Nurses were used to collect data. Data in this cross-sectional descriptive study were collected between September 2021 and November 2021 by the online survey method. The descriptive statistics of the data were analysed with numbers and percentages. The difference between the knowledge of artificial intelligence and robot nurses and demographic characteristics was analysed with the chi-square test.

RESULTS

According to the findings, 66.9% of the nurse managers reported having heard the concepts of artificial intelligence and robot nurses previously. 67.2% stated that they thought that robot nurses would benefit the nursing profession, but 86.2% voiced disbelief that robots would replace nurses.

CONCLUSIONS

The majority of the participating nurse managers reported that artificial intelligence and robot nurses would not replace nurses but would be beneficial for nurses and would reduce their workload.

IMPLICATIONS FOR NURSING MANAGEMENT

It should be ensured that the nurse managers plan the areas in the hospital where artificial intelligence and robot nurses will be used and determine the possible risks. Awareness should be increased with in-service trainings, and patient safety and ethical problems regarding the use of artificial intelligence and robot nurses should be identified.

Robots and Robotics in Nursing

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

Advantages of and Barriers to Crafting New Technology in Healthcare Organizations: A Qualitative Study in the COVID-19 Context

Nursing professionals are constantly required to adapt to technological changes, and especially so in the wake of COVID-19, which has prompted the development of new digital tools. A new and specific form of job crafting in relation to new technology has recently emerged in the literature; that is, adoption job crafting. However, little is known about this specific form of job crafting, especially within the pandemic context. We aim, in this study, to explore the advantages of and barriers to adoption job crafting. We used NVivo software to analyze 42 semi-structured interviews conducted during COVID-19. Our findings revealed that nurses had proactive and positive attitudes toward new technology (adoption job crafting) to enhance efficiency, sustainability, well-being, virtual teamwork, communication, and knowledge sharing. We also identified many barriers to adoption job crafting due to several organizational obstacles, such as the lack of human resource management practices, especially training, and the characteristics of the technology used. We contribute to the literature by documenting innovative cases of and barriers to adoption job crafting, which have not been explored before. These findings stress the necessity to adopt human resources practices, especially training, to foster positive job crafting among nurses and safeguard their adaptive expertise.

What Quality of Care Means? Exploring Clinical Nurses’ Perceptions on the Concept of Quality Care: A Qualitative Study

Quality is a multidimensional issue involving various features that depend on service performance and personal assessment. Clarifying the concept of quality is essential in order to further facilitate the understanding and improvement of quality in healthcare. The purpose of this study was to investigate how clinical nurses, providing care to adult medical patients, perceive and define the concept of quality nursing care. A descriptive qualitative research design was applied. A purposive sampling strategy was used to recruit nurses from the clinical sector of a general public hospital in Athens, Greece. Ten female nurses from the medical sector participated the study. Data collection was conducted through in-depth, semi-structured interviews. Conventional content analysis was used to analyze the verbatim data. Four categories were revealed from the data analysis, namely: (a) “Quality care is holistic care”, (b) “Good care is an interpersonal issue”, (c) “Leadership is crucial”, and (d) “Best care is our responsibility”. Quality care was defined as holistic care, addressing all patient needs with competency and aiming for the best patient outcomes. It was associated with communication, teamwork, good leadership, and personal commitment. By developing an in-depth and mutual understanding about what quality means, nurse leaders and practitioners may collaborate in finding common paths to support quality interventions and enhance quality nursing care in clinical practice.

Nurse preferences of caring robots: A conjoint experiment to explore most valued robot features

AIM

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.

METHODS

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.

RESULTS

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'.

CONCLUSIONS

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

Cuidado e inteligencia artificial: una reflexión necesaria

La enfermería no es ajena al cambio revolucionario que supone la introducción de la inteligencia artificial en el cuidado de la salud. A principios de 2021 se publicaron las conclusiones del think-tank internacional sobre la inteligencia artificial y la enfermería, en las que se reconoce la relevancia del uso de dichas tecnologías para aumentar y extender las capacidades de esta disciplina, entre ellas, el cuidado. Una valoración ponderada acerca del acierto de estas conclusiones exige, necesariamente, una reflexión epistemológica sobre el cuidado. En el presente artículo reflexionaremos sobre la incidencia de la inteligencia artificial en el cuidado de enfermería desde la perspectiva de la ética del cuidado y a la luz de los principales aportes del Samaritanus Bonus.

Technological Competency As Caring in Nursing: a Description, Analysis and Evaluation of The Theory

Introduction

When introducing and using technology in nursing, there is a danger that too much focus is placed on technology over caring for patients. The 'Technological competency as caring in nursing' theory can facilitate technology in caring, but the theory needs to be described, analysed and evaluated before it is used. The purpose of the literature review was to determine the possibility of applying the theory in education, research and practice, and whether the theory could be used to guide research into the use of electronic nursing record systems.

Methods

A literature search was conducted in PubMed, CINAHL, ScienceDirect, Google Scholar and Google Books, and supplemented with manual searching using the keywords 'Locsin', 'technology', 'caring' and 'nursing theory'. The criteria for inclusion were fully accessible articles and books in English on the relevant topics. The review process is shown in a PRISMA diagram. A hierarchy of evidence was used to evaluate the relative strength of the results. Pajnkihar's model was used to describe, analyse and evaluate the theory.

Results

A total of 26 hits were included in the final analysis. The theory in question meets the criteria of clarity, simplicity and complexity, adequacy, importance and significance; it can be tested; and it is useful in patient care that employs technology.

Discussion and conclusion

The theory is useful in nursing education, research and practice. The theory will be used to guide research on the perception of technological competency and care of internal medicine patients by nurses when using the electronic nursing record system in three Slovenian hospitals.

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.

Polish Nurses' Attitude to e-Health Solutions and Self-Assessment of Their IT Competence

In many countries, the implementation and dissemination of e-services for healthcare systems are important aspects of projects and strategies, as they contribute to significantly improving the access to such a system. The aim of the study is to analyze nurses' opinions on the application of the e-health solutions at work and the self-assessment of their IT competence. A linear stepwise regression allowed for the visualization of independent variables significantly influencing considerably the level of IT competency. Reduced IT competency was found in the group of nurses who rated the impact of the Internet and the new technologies as lower on the health care and general lives of modern people (β = 0.203; p < 0.0001), recommended e-health solutions to a lesser extent (β = 0.175; p < 0.0001), rated e-health solutions lower in relation to the patient (β = 0.149; p < 0.0001), and were older in age (β = 0.095; p = 0.0032). IT competence has become an indispensable requirement for nurses in fulfilling their professional roles. The quality of using new technologies in the work of nurses depends on their IT competence.

Artificial intelligence in nursing: Priorities and opportunities from an international invitational think-tank of the Nursing and Artificial Intelligence Leadership Collaborative

Aim

To develop a consensus paper on the central points of an international invitational think‐tank on nursing and artificial intelligence (AI).

Methods

We established the Nursing and Artificial Intelligence Leadership (NAIL) Collaborative, comprising interdisciplinary experts in AI development, biomedical ethics, AI in primary care, AI legal aspects, philosophy of AI in health, nursing practice, implementation science, leaders in health informatics practice and international health informatics groups, a representative of patients and the public, and the Chair of the ITU/WHO Focus Group on Artificial Intelligence for Health. The NAIL Collaborative convened at a 3‐day invitational think tank in autumn 2019. Activities included a pre‐event survey, expert presentations and working sessions to identify priority areas for action, opportunities and recommendations to address these. In this paper, we summarize the key discussion points and notes from the aforementioned activities.

Implications for nursing

Nursing's limited current engagement with discourses on AI and health posts a risk that the profession is not part of the conversations that have potentially significant impacts on nursing practice.

Conclusion

There are numerous gaps and a timely need for the nursing profession to be among the leaders and drivers of conversations around AI in health systems.

Impact

We outline crucial gaps where focused effort is required for nursing to take a leadership role in shaping AI use in health systems. Three priorities were identified that need to be addressed in the near future: (a) Nurses must understand the relationship between the data they collect and AI technologies they use; (b) Nurses need to be meaningfully involved in all stages of AI: from development to implementation; and (c) There is a substantial untapped and an unexplored potential for nursing to contribute to the development of AI technologies for global health and humanitarian efforts.

A Concept Analysis on the Use of Artificial Intelligence in Nursing

Artificial intelligence (AI) has a considerable present and future influence on healthcare. Nurses, representing the largest proportion of healthcare workers, are set to immensely benefit from this technology. However, the overall adoption of new technologies by nurses is quite slow, and the use of AI in nursing is considered to be in its infancy. The current literature on AI in nursing lacks conceptual clarity and consensus, which is affecting clinical practice, research activities, and theory development. Therefore, to set the foundations for nursing AI knowledge development, the purpose of this concept analysis is to clarify the conceptual components of AI in nursing and to determine its conceptual maturity. A concept analysis following Morse's approach was conducted, which examined definitions, characteristics, preconditions, outcomes, and boundaries on the state of AI in nursing. A total of 18 quantitative, qualitative, mixed-methods, and reviews related to AI in nursing were retrieved from the CINAHL and EMBASE databases using a Boolean search. Presently, the concept of AI in nursing is immature. The characteristics and preconditions of the use of AI in nursing are mixed between and within each other. The preconditions and outcomes on the use of AI in nursing are diverse and indiscriminately reported. As for boundaries, they can be more distinguished between robots, sensors, and clinical decision support systems, but these lines can become more blurred in the future. As of 2021, the use of AI in nursing holds much promise for the profession, but conceptual and theoretical issues remain.

Exploring the Lived Experience of Missed Nursing Care in Postgraduate Nursing Students in Iran

BACKGROUND

Missed care is a global phenomenon, which can include many clinical conditions that threaten the patients' safety in all countries and cultures, and also indicates the quality of nursing care. The nursing students' awareness and understanding of missed nursing care is of great importance. The current study aims to explore the lived experience of postgraduate nursing students in missed care.

METHODS

The current qualitative study was performed based on the interpretive phenomenological approach in Tehran, Iran, in February to December 2019. A total of 10 master's degree nursing students were selected through purposive sampling. A total of 10 semi-structured individual interviews were used to collect the data. The trail version of MAXQDA-10 software was used for coding. All interviews were recorded and codified, and the main themes were extracted from them using Dicklemann et al.'s (1989) analytical method.

RESULTS

Two main themes, five sub-themes, and 31 meaning units were obtained. The main themes included: "unfulfilled care" and "living in limbo".

CONCLUSION

Missed care, as unfulfilled care, is accompanied with living in limbo for nursing students, and this condition is influenced by organizational and personal factors. It seems that managers can prevent missed nursing care by supervising nursing care, reducing the nurses' workload, creating a sense of commitment to work, and enforcing ethical issues among nurses.

Predicted Influences of Artificial Intelligence on Nursing Education: Scoping Review

BACKGROUND

It is predicted that artificial intelligence (AI) will transform nursing across all domains of nursing practice, including administration, clinical care, education, policy, and research. Increasingly, researchers are exploring the potential influences of AI health technologies (AIHTs) on nursing in general and on nursing education more specifically. However, little emphasis has been placed on synthesizing this body of literature.

OBJECTIVE

A scoping review was conducted to summarize the current and predicted influences of AIHTs on nursing education over the next 10 years and beyond.

METHODS

This scoping review followed a previously published protocol from April 2020. Using an established scoping review methodology, the databases of MEDLINE, Cumulative Index to Nursing and Allied Health Literature, Embase, PsycINFO, Cochrane Database of Systematic Reviews, Cochrane Central, Education Resources Information Centre, Scopus, Web of Science, and Proquest were searched. In addition to the use of these electronic databases, a targeted website search was performed to access relevant grey literature. Abstracts and full-text studies were independently screened by two reviewers using prespecified inclusion and exclusion criteria. Included literature focused on nursing education and digital health technologies that incorporate AI. Data were charted using a structured form and narratively summarized into categories.

RESULTS

A total of 27 articles were identified (20 expository papers, six studies with quantitative or prototyping methods, and one qualitative study). The population included nurses, nurse educators, and nursing students at the entry-to-practice, undergraduate, graduate, and doctoral levels. A variety of AIHTs were discussed, including virtual avatar apps, smart homes, predictive analytics, virtual or augmented reality, and robots. The two key categories derived from the literature were (1) influences of AI on nursing education in academic institutions and (2) influences of AI on nursing education in clinical practice.

CONCLUSIONS

Curricular reform is urgently needed within nursing education programs in academic institutions and clinical practice settings to prepare nurses and nursing students to practice safely and efficiently in the age of AI. Additionally, nurse educators need to adopt new and evolving pedagogies that incorporate AI to better support students at all levels of education. Finally, nursing students and practicing nurses must be equipped with the requisite knowledge and skills to effectively assess AIHTs and safely integrate those deemed appropriate to support person-centered compassionate nursing care in practice settings.

INTERNATIONAL REGISTERED REPORT IDENTIFIER IRRID

RR2-10.2196/17490.

Predicted Influences of Artificial Intelligence on the Domains of Nursing: Scoping Review

BACKGROUND

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.

OBJECTIVE

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.

METHODS

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.

RESULTS

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.

CONCLUSIONS

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.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

RR2-10.2196/17490.

Guardians of humanity? The challenges of nursing practice in the digital age

Digital technologies have become a crucial factor in nursing. Given the fact that many tasks could also be done by robots or AI systems, the place for the nurse in this scenario is unclear. In what way and to what extent will the implementation of ever more sophisticated technology affect nursing practice? It is the aim of this paper to analyse the potential challenges of nursing practice in the digital age. The analysis is conducted through the lens of new materialism, a set of theoretical models that understand the relationship between humans and technology as dynamic and performative. According to this view, there is no prefixed essence of technology. Rather, the meaning of technology is enacted in concrete practice. The analysis shows that in past debates on technology use in nursing, the nurses' role has been defined as guardians of humanity, defending the patient against the dehumanizing effects of technology. This role has been transferred to the digital age, where it is the duty of nurses to cushion the negative effects of digital technology. As an alternative to this outdated role, nurses should be included in processes of technology design and policymaking. Enabling nursing professionals to shape the circumstances of a digitally enhanced holistic practice may empower their status within the healthcare system and also benefit the patient by contributing to a more person-centred care.

Intelligent humanoid robots expressing artificial humanlike empathy in nursing situations

Intelligent humanoid robots (IHRs) are becoming likely to be integrated into nursing practice. However, a proper integration of IHRs requires a detailed description and explanation of their essential capabilities, particularly regarding their competencies in replicating and portraying emotive functions such as empathy. Existing humanoid robots can exhibit rudimentary forms of empathy; as these machines slowly become commonplace in healthcare settings, they will be expected to express empathy as a natural function, rather than merely to portray artificial empathy as a replication of human empathy. This article works with a twofold purpose: firstly, to consider the impact of artificial empathy in nursing and, secondly, to describe the influence of Affective Developmental Robotics (ADR) in anticipation of the empathic behaviour presented by artificial humanoid robots. The ADR has demonstrated that it can be one means by which humanoid nurse robots can achieve expressions of more relatable artificial empathy. This will be one of the vital models for intelligent humanoid robots currently in nurse robot development for the healthcare industry. A discussion of IHRs demonstrating artificial empathy is critical to nursing practice today, particularly in healthcare settings dense with technology.

The disruptive power of Artificial Intelligence. Ethical aspects of gerontechnology in elderly care

Gerontechnology based on Artificial Intelligence (AI) is expected to fulfill the promise of the so-called 4p-medicine and enable a predictive, personalized, preventive, and participatory elderly care. Although empirical evidence shows positive health outcomes, commentators are concerned that AI-based gerontechnology could bring along the disruption of elderly care. A systematic conceptualization of these concerns is lacking. In this paper, such a conceptualization is suggested by analyzing the risks of AI in elderly care as "4d-risks": the depersonalization of care through algorithm-based standardization, the discrimination of minority groups through generalization, the dehumanization of the care relationship through automatization, and the disciplination of users through monitoring and surveillance. Based on the 4d-model, strategies for a patient-centered AI in elderly care are outlined. Whether AI-based gerontechnology will actualize the 4p-perspective or bring about the 4d-scenario depends on whether joint efforts of users, caregivers, care providers, engineers, and policy makers will be made.

Highlighting a Nursing Practice Model

Nursing’s contribution to the care of patients and families in the interdisciplinary arena requires clarity based on the scientific basis of the discipline. The robustness of a theoretical framework and practice model undergirds the patient experience and outcomes.