Detecting patient deterioration using artificial intelligence in a rapid response system.

Kyung-Jae Cho; Oyeon Kwon; Joon-myoung Kwon; Yeha Lee; Hyunho Park; Ki-Hyun Jeon; Kyung-Hee Kim; Jinsik Park; Byung-Hee Oh

Study

Emerging Classic

Detecting patient deterioration using artificial intelligence in a rapid response system.

March 25, 2020

Cho K-J, Kwon O, Kwon J-myoung, et al. Crit Care Med. 2020;48(4):e285-e289.

View more articles from the same authors.

This study compared an artificial intelligence (AI)-based early warning system using machine learning with conventional trigger methods for predicting deterioration among hospitalized patients, defined as in-hospital cardiac arrest resulting in ICU admissions. The AI system accurately predicted deterioration and was more accurate than conventional methods, demonstrating its potential effectiveness in EHR-based rapid response systems.

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See More About The Topic

Intensive Care Units

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Failure to rescue

Rapid Response Team

Detecting patient deterioration using artificial intelligence in a rapid response system.

eSIMPLER: a dynamic, electronic health record-integrated checklist for clinical decision support during PICU daily rounds.

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Reducing hospital harm: establishing a command centre to foster situational awareness.

Critical care resource nurse team: a patient safety and quality outcomes model.

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