JMIR Nursing

Digital health refers to the field of knowledge and practice associated with the development and use of digital technologies to improve clinical practice and health outcomes. Knowledge of digital health technology is becoming essential for all nurses and health providers.

Artificial intelligence (AI) continues to expand into nursing and healthcare. Many examples of AI applications driven by machine or deep learning are in use already. Examples include wearable devices or automated alerts for risk prediction. AI tends to be promoted by non nurses, creating a risk that AI is not designed to best serve registered nurses who will be expected to use AI outcomes in practice. Community Health Nurses (CHNs) are a small but essential group providing health care in the community. CHNs’ familiarity with AI and their perceptions about its effect on their practice is unknown.

The demanding work environment of nurses in medical and emergency units often results in high stress, job dissatisfaction, and burnout. Adequate rest is crucial for maintaining nurses' physical health, mental clarity, and emotional resilience, yet it is often overlooked in these high-pressure settings. This qualitative study explores the perceptions of nurses at a hospital in Western Jamaica regarding the quality and duration of rest they receive and its impact on their professional, mental, physical, and personal well-being. The hospital was selected due to the unique challenges healthcare workers face in Jamaica, including limited resources, high patient loads, and frequent staff shortages, which may exacerbate rest-related issues.

Evidence-based practice is essential for delivering safe, high-quality nursing care, yet its implementation remains challenging due to barriers such as limited knowledge, lack of supportive organizational culture, and insufficient access to relevant knowledge at the point of care. Knowledge Management Systems (KMS) have the potential to bridge this gap by integrating evidence into the nursing process through technological support. Despite growing interest, the integration of KMS into daily nursing practice is still underexplored, especially from the perspective of frontline nurses.

Assessing the current landscape of nurses' knowledge and attitudes is a critical first step in facilitating a smooth and effective transition towards AI-enhanced critical care.

Digital technologies are increasingly being introduced into the healthcare system and in settings like hospitals and geriatric long-term care (LTC) facilities, offering potential benefits such as improved care quality, reduced workload or enhanced documentation processes. However, the success of these technologies depends also on the acceptance by the primary users, the nursing staff.

Background: The COVID-19 pandemic disrupted nursing education globally, particularly clinical practicums, reducing opportunities for hands-on learning. Newly graduated nurses have reported increased stress, reduced confidence, and a higher risk of burnout. However, few studies have examined the long-term mental health effects of these disruptions.

The integration of artificial intelligence (AI) in healthcare is changing nursing practice, and it calls for the acquisition of AI literacy by students, which includes knowledge, skills, and attitudes. An understanding of the effect of AI literacy on the well-being and empowerment of students is crucial in guiding effective educational strategies.

African Americans are disproportionately impacted by congestive heart failure (CHF). The impact includes a two and a half times greater hospitalization rate and a fourth of a day longer length of hospitalization than Caucasians, of which nursing care has been associated with nearly a 30% decrease in hospitalizations and readmissions. Prior studies have demonstrated that registered nurses (RNs), working in conjunction with electronic health record systems (EHRs) to conduct care tasks, may optimize length of stay in African Americans with CHF.

Home-visiting nurses have difficulty selecting appropriate pressure injury (PI) management despite using clinical practice guidelines in various home-visiting settings. Clinical decision support systems (CDSS) can help home-visiting nurses’ decision-making.

A significant gap exists among health professionals regarding telehealth due to limited knowledge and varying perceptions. This disparity is particularly pronounced in developing regions, where numerous barriers hinder its implementation.

Handovers represent a critical moment for patient safety, where the effective transfer of information between nurses is essential. In this context, digital documentation systems such as IDEAS (Identification, Diagnosis, Evolution, Activities, Support) have been implemented to standardize and enhance the quality of clinical handovers.