Ethics, Artificial Intelligence, and Critical Care Nursing.

Resilience in critical care nurses-is it desirable?

Burnout Syndrome in ICU Caregivers: Time to Extinguish!

Family presence during resuscitation was introduced into clinical practice 30 years ago. Despite adoption of family-centered care by several health organizations and support for family presence during resuscitation by professional organizations such as the American Heart Association, critical care nurses' attitudes about family presence during resuscitation vary widely. To examine current evidence on critical care nurses' attitudes about, perceptions of, and behaviors related to practicing family presence during resuscitation. The method of Whittemore and Knafl guided the integrative review. Databases searched were CINAHL, PubMed, and Scopus. Articles included were English-language studies published from 2008 to 2022 that examined the perceptions of critical and emergency care nurses from adult units regarding family presence during resuscitation. Twenty-two articles were included. Levels and strength of evidence were assessed with the Johns Hopkins nursing evidence-based model. The articles in this integrative review included a total sample size of 4780 health care professionals; 3808 were critical and acute care nurses. Themes synthesized from current evidence included attitudes, benefits, barriers, demographic influence, cultural influence, and facilitators. Barriers and facilitators were associated with nursing practice in rural versus urban settings, age-related factors, years of experience, and unit-based differences in practice. Developing interventions to address identified factors can increase the practice of family presence during resuscitation in critical care settings. Several factors influence the practice of family presence during resuscitation in critical care settings. Nurse leaders should consider these factors to enhance the practice of family presence during resuscitation.

An ever-thorny issue: Defining key elements of critical care nursing and its relation to staffing.

The current role of nurses in the management of critically ill patients needing continuous renal replacement therapies is clearly fundamental. The care of these complex patients is typically shared by critical care and dialysis nurses: their precise duties may vary from country to country. To clarify this issue we conducted a national-level survey at a recent Italian course on nursing practices during continuous renal replacement therapies. A total of 119 questionnaires were analysed. The participants, who were equally divided between critical care and dialysis nurses, came from 44 different hospitals and 35 Italian cities. Overall, 23% of participants answered that "the dialysis staff" were responsible for continuous renal replacement therapies in the Intensive Care Unit, while 39% answered "the critical care nurse", and 38% "a shared organization". Interestingly, less than the half of participants claimed specific continuous renal replacement therapies training was provided to employees before handling an acute dialysis machine. Finally, about 60% of participants had experience of extra-corporeal membrane oxygenation machines used in conjunction with continuous renal replacement therapies. Workload coordination and management of critically ill patients undergoing continuous renal replacement therapies in Italy is not standardized. At present, the duties of critical care and dialysis nurses vary significantly across the country. They frequently overlap or leave gaps in the assistance received by patients. The role of nurses involved in the care of continuous renal replacement therapies patients in Italy currently requires better organization, possibly starting with intensive standardized training and educational programs.

The rapid evolution of artificial intelligence technologies, particularly in critical care nursing, presents opportunities and ethical challenges. Artificial intelligence has potential to enhance patient care and clinical decision-making, yet concerns regarding privacy, consent, bias, discrimination, and the dehumanization of care persist. To explore the intersection of artificial intelligence and ethics in nursing, with a focus on ethical implications for patient care and clinical decision-making. A comprehensive literature search was done for this narrative review to synthesize knowledge on artificial intelligence in nursing, incorporating insights from nursing, information technology, legal studies, and medicine. Artificial intelligence technologies are reshaping nursing workflows and can improve health care outcomes. However, these technologies introduce complex ethical concerns, including the risk for bias, data privacy issues, and the potential for reduced human interaction in patient care. Critical care nurses are uniquely positioned to leverage artificial intelligence effectively while identifying and mitigating risks related to its use. The involvement of critical care nurses in the development and application of artificial intelligence technologies is essential to ensure the accuracy, safety, and fairness of these tools. Critical care nurses must advocate for the ethical integration of artificial intelligence in health care, ensuring alignment with core nursing values such as autonomy, beneficence, nonmaleficence, justice, and veracity. By actively participating in discussions, monitoring artificial intelligence tools, and providing feedback, nurses can help to ensure that artificial intelligence technologies enhance patient care while upholding the ethical principles fundamental to nursing practice.

Artificial intelligence in critical care nursing: A scoping review.

Intellectual work of the critical care nurse: applications from a qualitative study

As the healthcare sector stands on the brink of a technological revolution, critical care nursing faces the imperative and challenging task of navigating the integration of Artificial Intelligence (AI) and Machine Learning (ML) into its practices. This presentation delves into the transformative journey of incorporating these advanced technologies to enhance patient care, optimize workflows, and address the complexities of critical care environments. AI and ML are not just tools for innovation but are becoming essential components in the evolution of nursing care. They offer sophisticated solutions for real-time patient monitoring, diagnostic accuracy, and predictive analytics, contributing significantly to improved patient outcomes and operational efficiency. This presentation explores practical examples where AI and ML have been successfully implemented in critical care settings, demonstrating their potential to revolutionize patient care through personalized treatment plans, early detection of complications, and the reduction of manual tasks, thereby allowing nurses to focus more on patient care. Integrating AI and ML into nursing practice presents challenges such as ethical dilemmas, data privacy issues, the necessity for extensive training, and the development of cross-disciplinary teamwork. Overcoming these hurdles requires a focused approach: ongoing educational initiatives to enhance nurses' proficiency in AI and ML, strict adherence to ethical guidelines protecting patient information, and fostering teamwork across various healthcare domains to fully harness AI and ML's capabilities in critical care nursing. In conclusion, this presentation seeks to inspire and equip nursing professionals with the knowledge and tools necessary to lead the charge in adopting AI and ML technologies. By embracing these innovations, critical care nursing can advance to new heights of efficiency, precision, and patient-centric care, marking a new era in healthcare.

Compassion fatigue in critical care nurses and its impact on nurse-sensitive indicators in Saudi Arabian hospitals

Evidence-Based Review and Discussion Points

IntroductionThe integration of artificial intelligence (AI) in intensive care units (ICUs) presents both opportunities and challenges for critical care nurses. This study delves into the human factor, exploring how nurses with leadership roles perceive the impact of AI on their professional practice.ObjectiveTo investigate how nurses perceive the impact of AI on their professional identity, ethical considerations surrounding its use, and the shared meanings they attribute to trust, collaboration, and communication when working with AI systems.MethodsAn interpretive phenomenological analysis was used to capture the lived experiences of critical care nurses leading with AI. Ten nurses with leadership roles in various ICU specializations were interviewed through purposive sampling. Semi-structured interviews explored nurses’ experiences with AI, challenges, and opportunities. Thematic analysis identified recurring themes related to the human factor in leading with AI.FindingsThematic analysis revealed two key themes which are leading with AI: making sense of challenges and opportunities and the human factor in leading with AI. The two main themes have six subthemes which revealed that AI offered benefits like task automation, but concerns existed about overreliance and the need for ongoing training. New challenges emerged, including adapting to new workflows and managing potential bias. Clear communication and collaboration were crucial for successful AI integration. Building trust in AI hinged on transparency, and collaboration allowed nurses to focus on human-centered care while AI supported data analysis. Ethical considerations included maintaining patient autonomy and ensuring accountability in AI-driven decisions.ConclusionWhile AI presents opportunities for automation and data analysis, successful integration hinges on addressing concerns about overreliance, workflow adaptation, and potential bias. Building trust and fostering collaboration are fundamentals for AI integration. Transparency in AI systems allows nurses to confidently delegate tasks, while collaboration empowers them to focus on human-centered care with AI support. Ultimately, dealing with the ethical concerns of AI in ICU care requires prioritizing patient autonomy and ensuring accountability in AI-driven decisions.

Critical thinking and decision-making are essential for nurses to identify and analyze judgments for nursing care. Hence, this study aims to determine critical thinking and clinical decision-making among critical and non-critical care nurses. A cross-sectional study was conducted among 237 nurses using a self-administered questionnaire. This study discovered that critical and non-critical care nurses had a modest level of critical thinking and clinical decision-making. The findings of this study can serve as a guide for nursing administration employees in implementing effective techniques to enhance the critical thinking skills of nurses in on-the-job training and orientation programs for nursing staff.

The aims of this study was to evaluate the acceptance and readiness of critical care nurses to use artificial intelligence (AI). AI is increasingly incorporated into clinical practice, offering the potential to revolutionize healthcare and significantly impact nursing practices. Integration of AI into nursing practice depends on its acceptance and adoption, with the potential to transform health care, improve patients' outcomes, and support decision-making. A cross-sectional research design was used to collect data from critical care nurses in general intensive care units of Alexandria University Hospital, Egypt, and King Abdul-Aziz Hospital, Al-Ahsa, Saudi Arabia, from May to July 2024. The minimum sample size was 279. Data were collected from 475 using an electronic Extended Technology Acceptance Model questionnaire. Structural equation modeling was used to analyze the relationships between the seven key constructs, including perceived usefulness, perceived ease of use, perceived risks, external benefits, facilitating conditions, adoption intention, and readiness to use AI. This study adheres to the STROBE checklist. More than half of nurses in the study had never used AI. Most of them reported perceived usefulness and readiness for adopting AI. Critical care nurses' adoption intention has positive influences on their readiness to use AI. Perceived ease of use, perceived usefulness, external benefits, and facilitating conditions can positively impact their readiness and adoption of using AI. This study highlights the importance of increasing nurses' awareness of AI applications in nursing practices. The integration and adoption of AI into nursing practices will help improve the quality of patients' care, however, education and training is needed to promote knowledge and understanding of the topic Health policies should mandate a framework to ensure well-trained and confident usage of AI.

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