Objectives: Early detection of subacute potentially catastrophic illnesses using available
data is a clinical imperative, and scores that report risk of imminent events in real time …

Background: The implementation and clinical impact of machine learning–based early
warning systems for patient deterioration in hospitals have not been well described. We …

At the beginning of the twentieth century, Ernest Amory Codman advocated for the “End
Result Idea” that hospitals follow their patients longitudinally to see the effects of their …

Introduction Unplanned transfer of trauma patients to the intensive care unit (ICU) carries an
associated increase in mortality, hospital length of stay, and cost. Trauma teams need to …

OBJECTIVES Emergency transfers (ETs), deterioration events with late recognition requiring
ICU interventions within 1 hour of transfer, are associated with adverse outcomes. We …

Background Predictive machine learning models have made use of a variety of scoring
systems to identify clinical deterioration in ICU patients. However, most of these scores …

Background Prior studies concentrated on unplanned intensive care unit (ICU) transfer to
gauge deterioration occurring shortly following hospital admission. However, examining …

After admission to emergency department (ED), patients with critical illnesses are transferred
to intensive care unit (ICU) due to unexpected clinical deterioration occurrence. Identifying …

Importance Recognizing and preventing patient deterioration is important for hospital safety.
Objective To investigate whether critical illness events (in-hospital death or intensive care …

Background Patients admitted from the emergency department to the wards, who progress
to a critically unwell state, may require expeditious admission to the intensive care unit. It …