JMIR Nursing

Global nursing shortages require innovative care delivery models. Virtual nursing is a cutting-edge model being explored.

Aging populations and rising chronic illness prevalences are increasing demands for nursing care, while staff shortages threaten care quality. Robotics offer potential support, yet usability, workflow integration, and user acceptance remain major barriers.

Clinical decision support (CDS) tools embedded in electronic health records in the form of integrated clinical prediction rules provide a potentially effective intervention to reduce inappropriate antibiotic prescribing for acute respiratory infections. However, their effectiveness has been limited by workflow barriers and low adoption by health care providers. Nurses are well positioned to implement evidence-based protocols using CDS tools. In a multicenter randomized controlled trial, a nurse-led implementation strategy for acute respiratory infection integrated clinical prediction rules was evaluated for use in primary care and urgent care settings.

The integration of digital health technologies (DHTs) in clinical practice is accelerating, creating a need for nursing students to develop digital competencies aligned with professional expectations. In Quebec, curricular reforms aim to enhance digital health literacy, but data are limited on students’ preparedness.

Large language models (LLMs) have shown promising results on Japanese national medical and nursing examinations. However, no study has evaluated LLM performance on the Japanese Public Health Nurse National Examination, which requires specialized knowledge in community health and public health nursing practice.

Effective interprofessional collaboration (IPC) in patient discharge planning is essential for ensuring continuity of care, improving patient outcomes, and strengthening coordination among healthcare professionals. Nurses often serve as primary coordinators due to their continuous engagement in patient care. However, the implementation of IPC continues to face barriers at the individual, team, and organizational levels. Many hospitals have adopted digital tools, such as Integrated Patient Progress Notes (IPPN), to facilitate information sharing. Nevertheless, the use of these tools to support IPC remains suboptimal and has been insufficiently explored, particularly within the Indonesian digital health context.

The majority of diabetic wound patients live in the community, facing challenges like a shortage of nurses, limited access to healthcare, and insufficient resources. Strategies such as specialist networks, patient monitoring, and online care platforms are crucial to improving diabetic wound management in the community.

Patients undergoing cancer treatment experience significant symptom burden. The standard process of symptom management includes patient reporting and clinical response following symptom escalation. Emerging predictive symptom models utilize AI components of machine learning and deep learning to identify the risk of symptom deterioration, facilitating earlier intervention to prevent downstream effects. However, integrating predictive symptom models into clinical practice will require oncology nurses to adopt innovative approaches.

Leadership development programs in healthcare often fail due to their lack of adaptability to the schedules of busy clinicians. This study addresses the need for scalable, flexible programs tailored to nurse leaders.

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.