AIMC Topic: Nursing Staff, Hospital

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Insights Into Factors Affecting Nurses' Knowledge of and Attitudes Toward AI and Implications for Successful AI Integration in Critical Care: Cross-Sectional Study.

JMIR nursing
BACKGROUND: Assessing the current landscape of nurses' knowledge and attitudes is a critical first step in facilitating a smooth and effective transition toward artificial intelligence (AI)-enhanced critical care. OBJECTIVE: This study aimed to asses...

Development of a Data-Based Method for Predicting Nursing Workload in an Acute Care Hospital: Methodological Study.

Journal of medical Internet research
BACKGROUND: Determining effective nurse staffing levels is crucial for ensuring quality patient care and operational efficiency within hospitals. Traditional workload prediction methods often rely on professional judgment or simple volume-based appro...

Invisible Scribes: Can Nurses Trust Ambient AI for Clinical Documentation?

Journal of continuing education in nursing
Ambient artificial intelligence listening tools promise faster nursing documentation and improved patient engagement, yet they introduce risks of hallucinations, omission, and bias when nurses are excluded from the design and oversight process. Empow...

Technology Integration to Support Nurses in an "Inpatient Room of the Future": Qualitative Analysis.

Journal of medical Internet research
BACKGROUND: The design and integration of technology within inpatient hospital rooms has a critical role in supporting nursing workflows, enhancing provider experience, and improving patient care. As health care technology evolves, there is a need to...

Integrating Nurse Preferences Into AI-Based Scheduling Systems: Qualitative Study.

JMIR formative research
BACKGROUND: Nurse scheduling is a complex challenge in health care, impacting both patient care quality and nurse well-being. Traditional scheduling methods often fail to consider individual preferences, leading to dissatisfaction, burnout, and high ...

Psychological hardiness, sleepiness, and fatigue as predictors of occupational errors in nurses: implications for enhancing nurse well-being and patient safety.

Industrial health
Nurses are at increased risk of making professional errors due to a combination of interrelated factors. We investigated the effects of sleepiness, fatigue, psychological hardiness, and demographic factors on the frequency of medical errors among act...

Identifying the Most Critical Predictors of Workplace Violence Experienced by Junior Nurses: An Interpretable Machine Learning Perspective.

Journal of nursing management
Workplace violence, defined as any disruptive behavior or threat to employees, seriously threatens junior nurses. Compared with senior nurses, junior nurses are more vulnerable to workplace violence due to inexperience, low professional recognition,...

Integration of Virtual Technology and Artificial Intelligence Improves Satisfaction, Patient Safety, and Nursing Workforce Efficiency.

Journal of nursing care quality
BACKGROUND: Virtual care technology including artificial intelligence (AI) may augment nursing functions creating flexibility in staffing that reduces workforce shortages and enhances patient safety.

Development and usability evaluation of a nurse-led clinical decision support system (AI-AntiDelirium) for management of intensive care unit delirium: A mixed methods study.

Applied nursing research : ANR
BACKGROUND: Clinical decision support systems (CDSS) have been identified to aid clinical decision-making, but few studies focus on the application of CDSS in intensive care unit (ICU) delirium, and particularly usability testing is not employed. We ...