Background: Artificial intelligence (AI) provides opportunities to identify the health risks of
patients and thus influence patient safety outcomes. Objective: The purpose of this …

The implementation of artificial intelligence (AI) and machine learning (ML) techniques in
healthcare has garnered significant attention in recent years, especially as a result of their …

There is little known about how academic medical centers (AMCs) in the US develop,
implement, and maintain predictive modeling and machine learning (PM and ML) models …

Background Chaotic monitor alarm management generates a large number of alarms, which
result in alarm fatigue. Intensive care unit (ICU) nurses are caretakers of critically ill patients …

Large language models (LLMs) have shown promise for task-oriented dialogue across a
range of domains. The use of LLMs in health and fitness coaching is under-explored …

Ten years after the publication of a landmark article in AACN Advanced Critical Care, alarm
fatigue continues to be an issue that researchers, clinicians, and organizations aim to …

Continuous vital signs monitoring in post-surgical ward patients may support early detection
of clinical deterioration, but novel alarm approaches are required to ensure timely …

Respiratory rate is the sentinel and arguably most important vital sign because its normal
values are breached before those of other vital signs in nearly all states of clinical decline …

Objective To develop and validate a prediction model of acute kidney injury (AKI) of any
severity that could be used for AKI surveillance and management to improve clinical …

Introduction: With the development of medical technology, clinical alarms from various
medical devices, which are rapidly increasing, are becoming a new problem in intensive …