Latest AI and machine learning research in nursing for healthcare professionals.
This study evaluates the effectiveness of the Patient Report Template (PRT) in addressing inefficiencies in nursing workflows related to electronic health records (EHRs) and clinical decision support systems. The PRT aims to streamline patient handoffs, reduce charting time, enhance direct care hours, and improve patient safety. A survey was sent to 2,118 nurses at the University of Iowa Health Ca...
Automated detection of papilloedema using artificial intelligence (AI) and retinal images acquired through an ophthalmoscope for triage of patients with potential intracranial pathology could prove to be beneficial, particularly in resource-limited settings where access to neuroimaging may be limited. However, a comprehensive overview of the current literature on this field is lacking. We conducte...
The deployment of artificial intelligence (AI) in healthcare necessitates robust safety validation frameworks, particularly for systems directly inter...
Clinical and phenotypic data available to researchers are often found in spreadsheets or bespoke data models. Bridging these to enterprise data wareho...
Emergency department (ED) crowding strains patient care and drives up costs. Early decisions on the need for patient hospital admissions can allow for...
To explore how advocacy has been defined, conceptualised and implemented within nursing, midwifery and the allied health professions. A secondary aim ...
Point-of-care technologies (POCTs) have grown increasingly prevalent in clinical and at-home settings, offering various rapid diagnostic capabilities....
Sub-phenotyping cardiogenic shock (CS) patients using non-traditional clustering methods represents a step toward precision medicine, potentially impr...
Artificial intelligence (AI) tools are increasingly being integrated into nursing education to enhance learning and provide flexible academic assistan...
The integration of intelligent technologies in operating room nursing represents a rapidly evolving field requiring systematic analysis to understand ...
The use of Artificial Intelligence (AI) methods in palliative care research is increasing. Most AI palliative care research involves the use of routin...
The early recognition of clinical deterioration in hospital inpatients continues to be a major challenge in healthcare. In this work, we proposed an i...
Carbapenem resistance in Pseudomonas aeruginosa is increasing in intensive care units (ICUs). To enhance antimicrobial stewardship and infection contr...
There is great potential for artificial Intelligence (AI) and machine learning (ML) to support decision making in emergency departments (ED), however ...
Stroke care generates vast free-text records that slow chart review and hamper data reuse. Large language models (LLMs) have been trialed as a remedy ...
Healthcare professionals face an urgent need for AI literacy as artificial intelligence technologies rapidly transform clinical practice, yet nursing-...
Most evaluations of artificial intelligence (AI) in medicine rely on static, multiple-choice benchmarks that fail to capture the dynamic, sequential n...
Severe multiple sclerosis (MS) presents challenges for clinical research due to mobility constraints and specialized care needs. Traditional MRI studi...
Representatives of the trauma community have voiced a need for a new approach to developing clinical guidance. In this study, we test the initial acce...
Interest in the use of prediction models to support referrals to palliative care is surging. Few high-performing models have been developed, implement...