Background and objective Early identification of patients at risk of deterioration can prevent
life-threatening adverse events and shorten length of stay. Although there are numerous …

The early identification of vulnerable patients has the potential to improve outcomes but
poses a substantial challenge in clinical practice. This study evaluated the ability of an …

Objective To identify factors influencing implementation of machine learning algorithms
(MLAs) that predict clinical deterioration in hospitalized adult patients and relate these to a …

Despite significant technical advances in machine learning (ML) over the past several years,
the tangible impact of this technology in healthcare has been limited. This is due not only to …

Research-based AI-driven systems to predict clinical deterioration are increasingly being
developed, but few are being implemented into clinical workflows. Escobar et al.(AAM) …

This review highlights the importance of technology applied to every single step of the chain
of survival to improve outcomes in cardiac arrest. Further research is needed to understand …

Background: The hospital-at-home (HAH) model is a viable alternative for conventional in-
hospital stays worldwide. Serum electrolyte abnormalities are common in acute patients …

Objective Surveillance algorithms that predict patient decompensation are increasingly
integrated with clinical workflows to help identify patients at risk of in-hospital deterioration …

Abstract Background and Objectives Prediction of patient deterioration is essential in
medical care, and its automation may reduce the risk of patient death. The precise …

Background Wireless vital sign sensors are increasingly being used to monitor patients on
surgical wards. Although early warning scores (EWSs) are the current standard for the …