Many clinicians who participate in or lead in-hospital cardiac arrest (IHCA) resuscitations lack confidence for this task or worry about errors. Well-led IHCA resuscitation teams deliver better care, but expert resuscitation leaders are often unavai...
BACKGROUND: Fluid and electrolyte management for hospital inpatients has been identified by multiple reports to be suboptimal, with delegation of this task to the most junior members of a medical team, Foundation Year One (FY1) doctors, also known as...
OBJECTIVE: To assess clinician perceptions of a machine learning-based early warning system to predict severe sepsis and septic shock (Early Warning System 2.0).
BACKGROUND: High-quality and high-utility feedback allows for the development of improvement plans for trainees. The current manual assessment of the quality of this feedback is time consuming and subjective. We propose the use of machine learning to...
Journal of the American Medical Informatics Association : JAMIA
33325504
OBJECTIVE: The study sought to describe the prevalence and nature of clinical expert involvement in the development, evaluation, and implementation of clinical decision support systems (CDSSs) that utilize machine learning to analyze electronic healt...
BACKGROUND: Advances have been made in the use of artificial intelligence (AI) in the field of diagnostic imaging, particularly in the detection of fractures on conventional radiographs. Studies looking at the detection of fractures in the pediatric ...
BACKGROUND: Compensation plays a critical role in motivating staff and enhancing operational performance and human resource costs in hospitals. This study was aimed at investigating pay levels and the key factors influencing pay satisfaction in secon...