Technological machines and artificial intelligence in nursing practice

Ethical risks in robot health education: A qualitative study

BackgroundAs health education robots may potentially become a significant support force in nursing practice in the future, it is imperative to adhere to the European Union's concept of "Responsible Research and Innovation" (RRI) and deeply reflect on the ethical risks hidden in the process of intelligent robotic health education.AimThis study explores the perceptions of professional nursing professionals regarding the potential ethical risks associated with the clinical practice of intelligent robotic health education.Research designThis study adopts a descriptive phenomenological approach, employing Colaizzi's seven-step method for data analysis.Participants and research contextWe conducted semi-structured interviews with 17 nursing professionals from tertiary comprehensive hospitals in China.Ethical considerationsThis study has been approved by the Ethics Committee of the Second Affiliated Hospital of Nanjing University of Chinese Medicine, Jiangsu Provincial Second Chinese Medicine Hospital.FindingsNursing personnel, adhering to the principles of RRI and the concept of "person-centered" care, have critically reflected on the potential ethical risks inherent in robotic health education. This reflection has primarily identified six themes: (a) threats to human dignity, (b) concerns about patient safety, (c) apprehensions about privacy disclosure, (d) worries about implicit burdens, (e) concerns about responsibility attribution, and (f) expectations for social support.ConclusionsThis study focuses on health education robots, which are perceived to have minimal ethical risks, and provides rich and detailed insights into the ethical risks associated with robotic health education. Even seemingly safe health education robots elicit significant concerns among professionals regarding their safety and ethics in clinical practice. As we move forward, it is essential to remain attentive to the potential negative impacts of robots and actively address them.

Development and psychometric evaluation of the artificial intelligence attitude scale for nurses

BACKGROUND

Since artificial intelligence is transforming healthcare, targeted interventions aimed at optimizing its integration and use in clinical settings requires the assessment of nurses' attitudes towards AI.

AIM

To develop and validate an Artificial Intelligence Attitude Scale specifically for Turkish nurses.

METHOD

This methodological study was conducted between October 2024 and December 2024, and its sample consisted of 678 nurses working in Turkey. The item pool was developed through a comprehensive literature review. Data analysis included descriptive statistics, item analysis, and exploratory and confirmatory factor analyses, as well as assessments of convergent and divergent validity, correlation analysis, internal consistency reliability, and test-retest reliability.

RESULTS

The content validity index for the items ranged from 0.85 to 1.00. Exploratory factor analysis revealed that the eigenvalues for four factors were greater than one, and these four factors accounted for 77.28% of the total variance. The scale demonstrated an acceptable model fit, with a goodness of fit index of 0.921 and a root mean square error of approximation (RMSEA) of 0.064. Cronbach's alpha coefficients ranged from 0.93 to 0.95 across the subscales, indicating high internal consistency, with the scale showing convergent and divergent validity. In addition, the Artificial Intelligence Attitude Scale for Nurses was found to have high test-retest reliability. This study may offer valuable insights into nurses' attitudes toward digital technologies, thereby informing the trajectory of digital transformation in healthcare services.

The Role of Artificial Intelligence in Nursing Care: An Umbrella Review

Artificial intelligence (AI) is revolutionizing nursing by enhancing decision-making, patient monitoring, and efficiency. Machine learning, natural language processing (NLP), and predictive analytics claim to improve safety and automate tasks. However, a structured analysis of AI applications is necessary to ensure their effective implementation in nursing practice. This umbrella review aimed to synthesize existing systematic reviews on AI applications in nursing care, providing a comprehensive analysis of its benefits, challenges, and ethical implications. By consolidating findings from multiple sources, this review seeks to offer evidence-based insights to guide the effective and responsible integration of AI in nursing practice. A systematic umbrella review approach was employed following PRISMA guidelines. Multiple databases, including PubMed, CINAHL, Scopus, Web of Science, and IEEE Xplore, were searched for review articles published between 2015 and 2024. Findings were synthesized thematically to identify key trends, benefits, limitations, and research gaps. This review synthesized 13 studies, emphasizing AI's impact on clinical decision support, patient monitoring, nursing education, and workflow optimization. AI enhances early disease detection, minimizes diagnostic errors, and automates documentation, improving efficiency. However, data privacy risks, biases, ethical concerns, and limited AI literacy hinder integration. AI presents significant opportunities for improving nursing care, yet its successful implementation requires addressing ethical, legal, and practical challenges. Adequate AI training, robust data governance frameworks, and policies ensuring responsible AI use are essential for its integration into nursing practice. Future research should explore long-term AI impact, training models for nurses, and strategies to balance AI-driven efficiency with human-centered care.

The Future of Artificial Intelligence in Mental Health Nursing Practice: An Integrative Review

Artificial intelligence (AI) has been increasingly used in delivering mental healthcare worldwide. Within this context, the traditional role of mental health nurses has been changed and challenged by AI-powered cutting-edge technologies emerging in clinical practice. The aim of this integrative review is to identify and synthesise the evidence of AI-based applications with relevance for, and potential to enhance, mental health nursing practice. Five electronic databases (CINAHL, PubMed, PsycINFO, Web of Science and Scopus) were systematically searched. Seventy-eight studies were identified, critically appraised and synthesised following a comprehensive integrative approach. We found that AI applications with potential use in mental health nursing vary widely from machine learning algorithms to natural language processing, digital phenotyping, computer vision and conversational agents for assessing, diagnosing and treating mental health challenges. Five overarching themes were identified: assessment, identification, prediction, optimisation and perception reflecting the multiple levels of embedding AI-driven technologies in mental health nursing practice, and how patients and staff perceive the use of AI in clinical settings. We concluded that AI-driven technologies hold great potential for enhancing mental health nursing practice. However, humanistic approaches to mental healthcare may pose some challenges to effectively incorporating AI into mental health nursing. Meaningful conversations between mental health nurses, service users and AI developers should take place to shaping the co-creation of AI technologies to enhance care in a way that promotes person-centredness, empowerment and active participation.

Effects of Automated Versus Conventional Ventilation on Quality of Oxygenation-A Substudy of a Randomized Crossover Clinical Trial

Background/Objectives: Attaining adequate oxygenation in critically ill patients undergoing invasive ventilation necessitates intense monitoring through pulse oximetry (SpO2) and frequent manual adjustments of ventilator settings like the fraction of inspired oxygen (FiO2) and the level of positive end-expiratory pressure (PEEP). Our aim was to compare the quality of oxygenation with the use of automated ventilation provided by INTELLiVENT-Adaptive Support Ventilation (ASV) vs. ventilation that is not automated, i.e., conventional pressure-controlled or pressure support ventilation. Methods: A substudy within a randomized crossover clinical trial in critically ill patients under invasive ventilation. The primary endpoint was the percentage of breaths in an optimal oxygenation zone, defined by predetermined levels of SpO2, FiO2, and PEEP. Secondary endpoints were the percentage of breaths in acceptable or critical oxygenation zones, the percentage of time spent in optimal, acceptable, and critical oxygenation zones, the number of manual interventions at the ventilator, and the number and duration of ventilator alarms related to oxygenation. Results: Of the 96 patients included in the parent study, 53 were eligible for this current subanalysis. Among them, 31 patients were randomized to start with automated ventilation, while 22 patients began with conventional ventilation. No significant differences were found in the percentage of breaths within the optimal zone between the two ventilation modes (median percentage of breaths during automated ventilation 19.4 [0.1-99.9]% vs. 25.3 [0.0-100.0]%; p = 0.963). Similarly, there were no differences in the percentage of breaths within the acceptable and critical zones, nor in the time spent in the three predefined oxygenation zones. Although the number of manual interventions was lower with automated ventilation, the number and duration of ventilator alarms were fewer with conventional ventilation. Conclusions: The quality of oxygenation with automated ventilation is not different from that with conventional ventilation. However, while automated ventilation comes with fewer manual interventions at the ventilator, it also comes with more ventilator alarms.

Advancing healthcare through mobile collaboration: a survey of intelligent nursing robots research

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.

Evaluation of neonatal nurses' anxiety and readiness levels towards the use of artificial intelligence

OBJECTIVEC

This is a cross-sectional and descriptive study to determine the levels of artificial intelligence anxiety and readiness of neonatal nurses.

DESIGN AND METHODS

The study included 107 neonatal nurses, with data collected between May and August 2023. Data were obtained using sociodemographic information, the Artificial Intelligence Anxiety Scale (AIAS) and the Medical Artificial Intelligence Readiness Scale (MAIRS). For the analyses, Kolmogorov-Smirnov test results were examined for normality assumptions of numerical variables and nonparametric statistical methods were used. The relationships between two independent numerical variables were analysed using Spearman's Rho Correlation coefficient, and the differences between two independent groups were analysed using Mann-Whitney U Analysis.

RESULTS

There was a statistically significant moderate negative correlation between participants' AIAS scores and MAIRS scores (r = -0.549). AIAS scores differed statistically significantly by age, education level, experience in neonatal care, knowledge about artificial intelligence, favouring the existence of AI-based technologies in neonatal clinics, and anxiety about artificial intelligence (p < 0.05). MAIRS scores differed statistically significantly (p < 0.05) by education level, having knowledge about artificial intelligence, favouring the existence of AI-based technologies in neonatal clinics, and anxiety about artificial intelligence.

CONCLUSION

Neonatal nurses' perceptions and attitudes towards AI technologies need to be better understood. Continuous training and support for neonatal nurses about AI technologies is important. This can enable them to effectively use AI technologies and contribute to improving the quality of patient care.

Artificial Intelligence in the Organization of Nursing Care: A Scoping Review

BACKGROUND

The integration of artificial intelligence (AI) in the organization of nursing care has continually evolved, driven by the need for innovative solutions to ensure quality of care. The aim is to synthesize the evidence on the use of artificial intelligence in the organization of nursing care.

METHODS

A scoping review was carried out based on the Joanna Briggs Institute methodology, following the PRISMA-ScR guidelines, in the MEDLINE, CINAHL Complete, Business Source Ultimate and Scopus® databases. We used ProQuest-Dissertations and Theses to search gray literature.

RESULTS

Ten studies were evaluated, identifying AI-mediated tools used in the organization of nursing care, and synthesized into three tool models, namely monitoring and prediction, decision support, and interaction and communication technologies. The contributions of using these tools in the organization of nursing care include improvements in operational efficiency, decision support and diagnostic accuracy, advanced interaction and efficient communication, logistical support, workload relief, and ongoing professional development.

CONCLUSIONS

AI tools such as automated alert systems, predictive algorithms, and decision support transform nursing by increasing efficiency, accuracy, and patient-centered care, improving communication, reducing errors, and enabling earlier interventions with safer and more efficient quality care.

Nurses' perspectives on privacy and ethical concerns regarding artificial intelligence adoption in healthcare

Background

With the increasing integration of artificial intelligence (AI) technologies into healthcare systems, there is a growing emphasis on privacy and ethical considerations. Nurses, as frontline healthcare professionals, are pivotal in-patient care and offer valuable insights into the ethical implications of AI adoption.

Objectives

This study aimed to explore nurses' perspectives on privacy and ethical concerns associated with the implementation of AI in healthcare settings.

Methods

We employed Van Manen's hermeneutic phenomenology as the qualitative research approach. Data were collected through purposive sampling from the December 7, 2023 to the January 15, 2024, with interviews conducted in Bengali. Thematic analysis was utilized following member checking and an audit trail.

Results

Six themes emerged from the research findings: Ethical dimensions of AI integration, highlighting complexities in incorporating AI ethically; Privacy challenges in healthcare AI, revealing concerns about data security and confidentiality; Balancing innovation and ethical practice, indicating a need to reconcile technological advancements with ethical considerations; Human touch vs. technological progress, underscoring tensions between automation and personalized care; Patient-centered care in the AI era, emphasizing the importance of maintaining focus on patients amidst technological advancements; and Ethical preparedness and education, suggesting a need for enhanced training and education on ethical AI use in healthcare.

Conclusions

The findings underscore the importance of addressing privacy and ethical concerns in AI healthcare development. Nurses advocate for patient-centered approaches and collaborate with policymakers and tech developers to ensure responsible AI adoption. Further research is imperative for mitigating ethical challenges and promoting ethical AI in healthcare practice.

Prevalence of turnover intention among emergency nurses worldwide: a meta-analysis

AIM

To explore the prevalence of turnover intentions among emergency nurses across the globe, decision-makers should be offered evidence-based assistance.

BACKGROUND AND INTRODUCTION

Compared with those of general nurses, the unique work environment and pressure significantly impact emergency nurses' turnover intention. High personnel turnover intention often hinders the provision of high-quality emergency services.

METHODS

This study was conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Published and unpublished papers were identified through electronic searches of PubMed, Web of Science, EMBASE, CINAHL, and the Cochrane Library from their establishment until February 1, 2023. The literature included in this study may encompass cross-sectional studies and longitudinal studies. Two researchers independently screened the literature, extracted data, and assessed the quality of the included studies while using the tool developed by Hoy and colleagues in 2012. Stata 17.0 was used for all the statistical analyses.

RESULTS

This study included 12 articles by screening 744 articles, which included a total of 4400 nurses. All studies included in the analysis were cross-sectional. The overall prevalence of turnover intention among emergency nurses was 45%. Further analysis revealed that the turnover intention prevalence among emergency nurses in Asia was 54%, whereas in other regions, it was 38%. The turnover intention among younger nurses (61%) was significantly greater than that among older nurses (30%). Compared with the published scale, the self-developed scale resulted in a higher turnover intention rate of 52%, which was 41%.

CONCLUSION

The prevalence of emergency nurses' turnover intention is relatively high and shows an increasing trend, with noticeable variations across different regions and age groups. Notably, Asian nurses and those younger than 35.6 years exhibit a greater intention to turnover.

PATIENT OR PUBLIC CONTRIBUTION

There is no patient or public involvement, as this article is a meta-analysis.

IMPLICATIONS FOR NURSING AND HEALTH POLICY

Nursing managers, administrators, and policymakers must recognize the seriousness of high turnover intentions among emergency nurses and develop effective prevention strategies to address this issue globally.

Perceptions and attitudes of nurse practitioners toward artificial intelligence adoption in health care

Background

With the ever-increasing integration of artificial intelligence (AI) into health care, it becomes imperative to gain an in-depth understanding of how health care professionals, specifically nurse practitioners, perceive and approach this transformative technology.

Objectives

This study aimed to gain insights into nurse practitioners' perceptions and attitudes toward AI adoption in health care.

Methods

This qualitative research employed a descriptive and phenomenological approach using in-depth interviews. Data were collected through a semi-structured questionnaire with 37 nurse practitioners selected through purposive sampling, specifically Maximum Variation Sampling and Expert Sampling techniques, to ensure diversity in characteristics. Trustworthiness of the research was maintained through member checking and peer debriefing. Thematic analysis was employed to uncover recurring themes and patterns in the data.

Results

The thematic analysis revealed nine main themes that encapsulated nurse practitioners' perceptions and attitudes toward AI adoption in health care. These included nurse practitioners' perceptions of AI implementation, attitudes toward AI adoption, patient-centered care and AI, quality of health care delivery and AI, ethical and regulatory aspects of AI, education and training needs, collaboration and interdisciplinary relationships, obstacles in integrating AI, and AI and health care policy. While this study found that nurse practitioners held a wide range of perspectives, with many viewings AI as a tool to enhance patient care.

Conclusions

This research provides a valuable contribution to the evolving discourse surrounding AI adoption in health care. The findings underscore the necessity for comprehensive education and training in AI, accompanied by clear and robust ethical and regulatory guidelines to ensure the responsible integration of AI in health care practice. Furthermore, fostering collaboration and interdisciplinary relationships is pivotal for the successful incorporation of AI in health care. Policymakers should also address the challenges and opportunities that AI presents in the health care sector. This study enhances the ongoing conversation on AI adoption in health care by shedding light on the perspectives of nurses, thereby shaping future strategies for AI integration.

The Basics of Artificial Intelligence in Nursing: Fundamentals and Recommendations for Educators

BACKGROUND

Artificial intelligence (AI) offers exciting possibilities; however, AI is a double-edged sword. The adoption of this technology offers many benefits but also presents risks to academic integrity and appropriately prepared graduates. Many of today's nurse educators are from generations that are unlikely to possess an understanding of AI. This article provides fundamental knowledge needed to understand the current state of AI in nursing and offers recommendations to nurse educators on ways to responsibly incorporate AI technologies into nursing curricula.

METHOD

AI literature from PubMed, CINAHL, and Google Scholar was reviewed and synthesized.

RESULTS

Definitions, explanations, and applications to nursing education are outlined. Recommendations are made for AI implementation, along with ideas to avoid potential AI-enabled plagiarism and academic dishonesty.

CONCLUSION

As professionals, nurse educators should understand the basics of AI and be able to judge the appropriateness of integration and also recognize opportunities to embrace future application. [J Nurs Educ. 2023;62(12):716-720.].