Nursing

The Role of Machine Learning and Artificial Intelligence in Enhancing Critical Care Nursing Practice: A Scoping Review.

BACKGROUND: Artificial intelligence (AI) and machine learning (ML) are emerging as transformative tools in healthcare, with significant potential to enhance nursing practice, particularly in intensive care units (ICUs). ICUs pose complex challenges, including high patient acuity, ICU delirium, and nurse workload. These factors demand innovative technological solutions. AIM: This scoping review com...

Leveraging Artificial Intelligence to Improve Perioperative Staffing Consistency: A Quality Improvement Initiative at a Large Academic Medical Center.

A large academic medical center in the Pacific Northwest addressed perioperative staffing challenges by implementing a workflow with application of AI assistance to optimize daily assignments. The initiative integrated real-time and historical data to support consistent matching of staff competencies and procedure experience with scheduled procedures and surgeons. The workflow streamlined processe...

Evaluating the Accuracy, Empathy, and Readability of Generative AI Versus Registered Nurses in Discharge Planning: A Vignette-Based Study.

AIM: To compare the multidimensional performance of discharge instructions generated by generative AI (GPT-4) versus those created by clinical registe...

Multisite Implementation of a Digital Wound Model of Care: A Post-Implementation Multimethods Evaluation of Patient and Clinician Perspectives and Lessons Learned.

This study evaluated district-wide implementation of a digital wound model of care combining an artificial intelligence-enabled application with a vir...

Exploratory Analysis of Electrical Impedance Tomography Features Associated with Spirometric Airflow Limitation.

Introduction: Electrical impedance tomography (EIT) is a noninvasive, radiation-free imaging modality that provides real-time information on regional ...

Artificial intelligence in emergency medicine: from algorithmic performance to real-world clinical integration - understanding the implementation gap.

BACKGROUND: Emergency departments (EDs) operate under time pressure, diagnostic uncertainty, and cognitive overload. Artificial intelligence (AI)-driv...

The diagnostic accuracy of left ventricular ejection fraction assessment between visual and artificial intelligence-based algorithms on bedside ultrasound.

BACKGROUND: Focused cardiac ultrasound (FoCUS) has become the standard of care for bedside assessments of cardiac function. With the integration of ar...

Artificial Intelligence in Mental Health Nursing: A Bibliometric Analysis of Research Trends, Knowledge Structures and Emerging Themes.

INTRODUCTION: Artificial intelligence technologies are increasingly transforming mental healthcare through predictive analytics, language-based system...

Machine Learning-Driven Risk Clusters for Adverse Outcomes in Patients Hospitalized with Acute Heart Failure: Using a Retrospective Cohort Data.

AIM: Heart failure (HF) poses a growing public health burden, yet conventional risk stratification models fail to capture the multidimensional complex...

Guiding Doctoral Students in the Appropriate and Ethical Use of Artificial Intelligence for the Doctor of Nursing Practice Proposal Development.

Background: Developing a Doctor of Nursing Practice (DNP) project proposal is challenging as students balance clinical skill development and certifica...

Turkish Adaptation of the Nursing Students' Fear Toward Artificial Intelligence Scale: A Study of Validity and Reliability.

Background and Purpose: Most nursing students report having limited knowledge about artificial intelligence, which may lead to anxiety and fear regard...

The LoVe score: Development and validation of a bedside clinical index to identify patients at risk for prolonged invasive mechanical ventilation.

BACKGROUND: Early management decisions after intubation, such as humidification strategy or initiation of prevention bundles for ventilator-associated...

Enhancing the accuracy of bioimpedance-derived appendicular skeletal muscle mass in aged adults through machine learning.

PURPOSE: Appendicular Skeletal Muscle Mass (ASMM) estimation via Bioelectrical Impedance Analysis (BIA) is a high-quality and bedside-accessible metho...

Development of a machine learning prediction model for overactive bladder in female nurses: the NURS study.

PURPOSE: To develop and internally validate interpretable machine-learning models for identifying individuals with a higher probability of overactive ...

Non-inferiority analysis of a fetal heart rate artificial intelligence algorithm to registered nurse assessment.

INTRODUCTION: Cardiotocography (CTG) is widely used for monitoring fetal heart rate (FHR) and uterine activity (UA) during pregnancy and labor. Clinic...

Embodied AI in the operating room: a voice-interactive robotic scrub nurse for surgical instrument handoffs.

Embodied artificial intelligence may extend surgical robotics beyond teleoperation by enabling robots to interpret natural-language commands, perceive...

Avoiding the "Desert of the Real": Preserving Evidence-Based Nursing in the Age of AI.

AIM: A discussion of the implications of generative artificial intelligence (AI) evidence synthesis for evidence-based practice (EBP) among nurses res...

Deep Learning Versus LASSO-Based Machine Learning for 1-Year Survival After Surgery for Spinal Metastases: A JASA Multicenter Prospective Cohort Analysis.

STUDY DESIGN: Multicenter prospective cohort study; secondary analysis. OBJECTIVE: To evaluate predictors associated with 1-year survival after surger...

Revising the TIGER 2018 Health Informatics Competency Framework: Updated Core Competency Areas for Nurses in a Digital Health Era.

BACKGROUND: In 2018, the "TIGER International Recommendation Framework of Core Competencies in Health Informatics for Nurses" was published and used w...

Clinical phenotyping of bloodstream infections: a review of current evidence.

BACKGROUND: Bloodstream infections (BSIs) are a leading cause of morbidity and mortality, yet their clinical heterogeneity continues to challenge effe...