Technological machines and artificial intelligence in nursing practice

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.

Ethical risks in robot health education: A qualitative study

BACKGROUND

As 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.

AIM

This study explores the perceptions of professional nursing professionals regarding the potential ethical risks associated with the clinical practice of intelligent robotic health education.

RESEARCH DESIGN

This study adopts a descriptive phenomenological approach, employing Colaizzi's seven-step method for data analysis.

PARTICIPANTS AND RESEARCH CONTEXT

We conducted semi-structured interviews with 17 nursing professionals from tertiary comprehensive hospitals in China.

ETHICAL CONSIDERATIONS

This 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.

FINDINGS

Nursing 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.

CONCLUSIONS

This 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.

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.].