Session ID: 
231

AI-Powered Early Warning System to Improve Patient Safety

1:30pm - 2:30pm Thursday, March 8
Las Vegas - Venetian Convention Center
Palazzo D

Description

Early detection of physiologic deterioration in order to reduce in-hospital mortality and prevent unplanned transfers to the intensive care unit (ICU) is a National Patient Safety Goal. Utility of non-automated early warnings system (MEWS) and Rapid Response Teams (RRT) to reduce in-hospital mortality are often limited due to inadequate and inconsistent alerting mechanisms. We describe the development, validation, and implementation of an automated, real-time AI-Powered Early Warning System (EWS) model for predicting risk of unplanned ICU transfers or cardiopulmonary arrests outside the ICU. The automated model outperforms non-automated models and unaided clinician observation, leading to improved care and patient safety. Post-deployment evaluation led to adoption of novel features: 1) alerts delivered with contextual reasons and triggers to facilitate rapid targeted clinical assessment by end-users, and 2) enhanced filtering to improve targeting of patients amenable to intervention.

Learning Objectives: 

  • Summarize the scope and scale of preventable hospital deaths and unplanned transfers to the ICU
  • Distinguish between real-time automated EHR-based prediction models and rapid response team (RRT) protocols
  • Analyze process of developing, validating & implementing automated prediction models/early warning systems
  • Evaluate potential for automated prediction models to improve the early detection of at-risk hospital patients

Speaker(s): 

Medical Director,
Parkland Center for Clinical Innovation (PCCI)
Physician Scientist,
Parkland Center for Clinical Innovation (PCCI)
Continuing Education Credits: 
ABPM
1.00
CAHIMS
1.00
CME
1.00
CNE
1.00
CPHIMS
1.00
PDU
1.00

Audience: 

Clinical Informaticists
Nurse, CNO, CNIO
Physician, CMO, CMIO

Level: 

Introductory

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