We use cookies to understand how you use our site and to improve your experience. This includes personalizing content and advertising. To learn more, click here. By continuing to use our site, you accept our use of cookies. Cookie Policy.

Features Partner Sites Information LinkXpress hp
Sign In
Advertise with Us
ARAB HEALTH - INFORMA

Download Mobile App




AI-Driven Prediction Models Accurately Predict Critical Care Patient Deterioration

By HospiMedica International staff writers
Posted on 10 Apr 2024
Print article
Image: Novel machine-learning approach provides more time for intervention with fewer disruptions for caregivers (Photo courtesy of 123RF)
Image: Novel machine-learning approach provides more time for intervention with fewer disruptions for caregivers (Photo courtesy of 123RF)

An intelligent clinical surveillance platform, powered by AI-driven prediction models for patient deterioration and best practice clinical protocols, provides insights based on real-time patient physiology and allows caregivers the opportunity to offer proactive clinical care.

CLEW Medical, Inc. (Netanya, Israel) has developed the CLEW system, a revolutionary tool designed to alert medical staff to the likelihood of patient deterioration up to eight hours earlier than traditional monitoring systems. This advanced warning system enables timely interventions that can reduce complications and fatalities. The CLEW system is adept at predicting both high and low risks of respiratory failure and hemodynamic instability, which are among the most frequent issues impacting patients in intensive care units (ICUs). By providing an early warning when a patient is at high risk of experiencing one of these critical conditions, the system offers healthcare providers the crucial window they need to act before the patient's condition visibly worsens. Early interventions could mean preventing the need for drastic measures like mechanical ventilation for severe respiratory distress and assisting in managing hospital capacity by identifying potential bottlenecks caused by a sudden decline in the patient’s condition.

A comparative study in ICUs evaluated the effectiveness of the CLEW system against the accuracy and utility of alerts of the most widely used telemedicine and bedside monitoring systems. The findings not only highlighted its superior precision but also revealed that the CLEW system produced significantly fewer alarms—50 times less than its counterparts. In the high-pressure settings of ICUs, where staff are constantly managing critical situations, reducing alarm fatigue and the mental load on healthcare workers is crucial. The reduction in false alarms leads to fewer disruptions, contributing to a quieter, more serene ICU atmosphere. The research indicated that, on average, 98% of alarms from standard bedside monitors (equating to 147 out of 150 per patient per day) were false alarms. With its infrequent yet more accurate alerts, the CLEW system was recognized for its potential to alleviate ICU burnout syndrome by lowering unnecessary stress and tasks for the medical staff.

Gold Member
POC Blood Gas Analyzer
Stat Profile Prime Plus
Gold Member
STI Test
Vivalytic Sexually Transmitted Infection (STI) Array
New
Transcatheter Heart Valve
SAPIEN 3 Ultra
New
Mobile Power Procedure Chair
LeMans P360

Print article

Channels

Surgical Techniques

view channel
Image: (Left) An image of a 3D-printed material implanted in vivo for 4 weeks. (Right) A photo of coral (Photo courtesy of Dr Zhidao Xia and Jesus Cobaleda)

Revolutionary Coral-Inspired Material for Bone Repair Promotes Faster Healing

Bone defects caused by fractures, tumors, and non-healing injuries are major contributors to disability worldwide. Traditionally, doctors have used either a patient’s own bone (autograft) or donor bone... Read more

Patient Care

view channel
Image: The portable biosensor platform uses printed electrochemical sensors for the rapid, selective detection of Staphylococcus aureus (Photo courtesy of AIMPLAS)

Portable Biosensor Platform to Reduce Hospital-Acquired Infections

Approximately 4 million patients in the European Union acquire healthcare-associated infections (HAIs) or nosocomial infections each year, with around 37,000 deaths directly resulting from these infections,... Read more

Health IT

view channel
Image: First ever institution-specific model provides significant performance advantage over current population-derived models (Photo courtesy of Mount Sinai)

Machine Learning Model Improves Mortality Risk Prediction for Cardiac Surgery Patients

Machine learning algorithms have been deployed to create predictive models in various medical fields, with some demonstrating improved outcomes compared to their standard-of-care counterparts.... Read more

Point of Care

view channel
Image: The acoustic pipette uses sound waves to test for biomarkers in blood (Photo courtesy of Patrick Campbell/CU Boulder)

Handheld, Sound-Based Diagnostic System Delivers Bedside Blood Test Results in An Hour

Patients who go to a doctor for a blood test often have to contend with a needle and syringe, followed by a long wait—sometimes hours or even days—for lab results. Scientists have been working hard to... Read more
Copyright © 2000-2024 Globetech Media. All rights reserved.