JMIR Nursing

Patient safety investigation reports support organizational learning only when they are complete, usable, and sufficiently detailed. Conventional free-text reports are often inconsistent and may omit information needed for review and learning. Project NARRATE (Nursing AI-Refined for Accurate Transcription of Events) is a nursing-led ambient artificial intelligence workflow that uses prompts aligned with the World Health Organization Minimal Information Model for Patient Safety Incident Reporting and Learning Systems, Situation-Background-Assessment-Recommendation output, and visible missing-information cues to support structured supervisor reporting.

Assistive robots have been proposed to support nursing work by offloading routine, logistical, and physically demanding tasks. However, existing research has largely focused on postimplementation acceptance or usability, with fewer studies examining how nurses conceptualize assistive robots before routine deployment, particularly in settings where exposure remains limited.

Clinical AI decision support is being introduced into nursing practice; however, existing large language models (LLMs) demonstrate only moderate accuracy on complex clinical tasks, raising questions about the level of accuracy required for safe clinical use across varying levels of clinician experience and task complexity.

Socioemotional competencies (SEC) are essential for nurses to provide quality care and to maintain their own health. Historically underrepresented, nursing programs are changing their curricula to incorporate SEC development more strongly. Although it is recommended to practice SEC during clinical placement, studies on how to support nursing students in developing these competencies are lacking.

The prevention of medical errors depends on effective communication and teamwork between nurses, yet traditional nurse call systems create inefficiencies that result in higher staff workloads. Further research is needed to evaluate how digital health care technologies affect nursing workflows and whether they contribute to staff workload.

Family care partners manage care for individuals with Alzheimer disease and related dementias (ADRD), including hypertension management crucial for preventing cognitive decline. Nurses frequently interact with these caregiver-patient dyads but have limited evidence about caregivers’ real-world experiences of managing this comorbidity. Understanding these experiences is essential for developing nurse-led interventions that support both patients and caregivers.

Digital interventions can support sodium restriction in chronic kidney disease; yet, implementation and engagement barriers remain poorly understood.

Artificial intelligence (AI)–based nursing interventions are increasingly being used to manage chronic illnesses; however, their definitive impact on clinical outcomes remains inconclusive, necessitating a comprehensive evidence synthesis.

Training programs help dementia care staff handle residents’ behavioral symptoms in nursing homes and assisted living facilities. However, it is unclear how feeling well-prepared from such training relates to experiences of physical and verbal aggression from residents.

Nurses are pivotal as end users and implementers of digital mental health interventions (DMHIs). However, the successful translation of efficacious DMHIs into sustainable nursing practice is hindered by multifaceted implementation challenges.

Medical errors occur more frequently in health care than in other industries due to challenges in patient safety education for nurses and students. To address this, it is important to identify the factors and structures underlying clinical errors and apply these insights to educational programs. Medication-related errors are highly preventable with appropriate interventions, highlighting the importance of data-driven safety education. Previous research suggests that unit adaptation, rather than clinical experience alone, plays a critical role in error occurrence. Focusing on “adaptive performance,” an underexplored concept in nursing, can help identify new educational strategies and interventions.

Mobile health (mHealth) technology offers new approaches to improve women’s health by providing personalized monitoring and real-time guidance. As one of the most widely used social media platforms in China, WeChat has shown great potential in mHealth practice, yet systematic evidence on its application in women’s health care remains insufficient.