JMIR Nursing

The healthcare sector faces a projected shortfall of 10 million workers by 2030. AI automation in areas such as patient education and initial therapy screening presents a strategic response to mitigate this shortage and reallocate medical staff to higher-priority tasks. However, current methods of evaluating early-stage healthcare AI chatbots are highly limited due to safety concerns and the amount of time and effort that goes into evaluating them.

The digitalization of health care in Germany holds great potential to improve patient care, resource management, and efficiency. However, strict data protection regulations, fragmented infrastructures, and resistance to change hinder progress. These challenges leave care institutions reliant on outdated paper-based workflows, particularly for patient data transmission, despite the pressing need for efficient tools to support health care professionals amid a nursing shortage and rising demand for care.

This review investigates the relationship between artificial intelligence (AI) use and the role of nurses in patient care. AI exists in health care for clinical decision support, disease management, patient engagement, and operational improvement and will continue to grow in popularity, especially in the nursing field.

This Research Letter discusses the impact of different file formats on ChatGPT-4's performance in the Japanese National Nursing Examination, highlighting the need for standardized reporting protocols to enhance the integration of AI in nursing education and practice.

Professionals in caring disciplines have been pivotal in advancing virtual care, which leverages remote technologies to deliver effective support and services from a distance. Educators in these caring professions are required to teach students the skills and competencies needed to provide high-quality and effective care. As virtual care becomes more integral, educators must equip students in these fields with both interpersonal and technological skills, bridging traditional hands-on learning with digital literacy. However, there is a gap in evidence exploring educators’ perceptions and experiences of teaching caring profession students about virtual caring skills within online environments.

The rising prevalence of urinary incontinence (UI) among older adults, particularly those living in nursing homes (NHs), underscores the need for innovative continence care solutions. The implementation of an unobtrusive sensor system may support nighttime monitoring of nursing home residents' movements and, more specifically, the agitation possibly associated with voiding events.

Globally, the rate at which the aging population and the prevalence of chronic diseases are increasing is remarkable. With declining birth rates and a growing percentage of elderly individuals, the demand for nursing staff is steadily rising. However, the shortage of nursing personnel has been a longstanding issue. In recent years, numerous researchers have advocated implementing nursing robots as a substitute for traditional human labour.

Optimal nurse staffing levels have been shown to impact patients’ prognoses and safety, as well as staff burnout. The predominant method for calculating staffing levels has been Patient-to-Nurse (P/N) ratios and Nursing Hours Per Patient Day (NHPPD). However, both methods fall short in addressing the dynamic nature of staffing needs that often fluctuate throughout the day as patients' clinical status changes, and new patients are admitted or discharged from the unit.

Background: Effective communication is vital in healthcare, especially for nursing students who are the future of healthcare delivery. In Iraq's nursing education landscape, characterized by challenges like resource constraints and infrastructural limitations, understanding communication modalities is crucial.

During the pandemic, health care providers implemented remote patient monitoring (RPM) for patients experiencing COVID-19. RPM is an interaction between health care professionals and patients who are in different locations, in which certain patient functioning parameters are assessed and followed up for a certain duration of time. The implementation of RPM in these patients aimed to reduce the strain on hospitals and primary care.

The ethics of artificial intelligence (AI) are increasingly recognized due to concerns such as algorithmic bias, opacity, trust issues, data security, and fairness. Specifically, machine learning algorithms, central to AI technologies, are essential in striving for ethically sound systems that mimic human intelligence. These technologies rely heavily on data, which often remain obscured within complex systems and must be prioritized for ethical collection, processing, and usage. The significance of data ethics in achieving responsible AI was first highlighted in the broader context of healthcare and subsequently in nursing. This presentation explores the principles of data ethics, drawing on relevant frameworks and strategies identified through a formal literature review. These principles apply to real-world and synthetic data in AI and machine learning contexts. Additionally, the data-centric AI paradigm is briefly examined, emphasizing its focus on data quality and the ethical development of AI solutions that integrate human-centered domain expertise. The ethical considerations specific to nursing are addressed, including four recommendations for future directions in nursing practice, research, and education and two hypothetical nurse-focused ethical case studies. The primary objectives are to position nurses to actively participate in AI and data ethics, thereby contributing to creating high-quality, relevant data for machine learning applications.

Nursing students’ learning during clinical practice is largely influenced by the quality of the guidance they receive from their nurse preceptors. Students that have attended placement in nursing home settings have called for more time with nurse preceptors and an opportunity for more help from the nurses for reflection and developing critical thinking skills. To strengthen students’ guidance and assessment and enhance students’ learning in the practice setting, it has also been recommended to improve the collaboration between faculties and nurse preceptors.