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Novel machine-learning approach provides more time for intervention with fewer disruptions for caregivers

Boston, MA, April 8, 2024 – A scientific paper recently published by CHEST Journal, shares a study that finds CLEW Medical’s FDA-cleared AI-driven models for predicting patient deterioration were five times more accurate than alerts from a leading telemedicine system using data from two major US health systems.

The study was conducted in ICUs by renowned critical care leaders, Craig Lilly, MD, Vice Chair of Critical Care at UMass Memorial Medical Center and David Kirk, MD, Chief Clinical Integration Officer at WakeMed Health & Hospitals, with support from CLEW advisory board physicians and data science experts. The prediction models used in the study are a part of CLEW’s intelligent clinical surveillance platform. The study compared performance of this novel system to the accuracy and utility of alerts in the most widely utilized telemedicine and bedside monitoring systems.

The CLEW system, which notifies clinical staff that a patient is likely to deteriorate up to eight hours before other monitoring systems would indicate, creates opportunities for early intervention, which decreases complications and mortality. The study describes that “the transformation of even a small number of emergent responses to physiological instability events… would be expected to meaningfully improve ICU care delivery.” CLEW’s models provide predictions for high and low risk of respiratory failure and hemodynamic instability, two of the most common conditions affecting patient status in intensive care.

With notification that a patient is at high-risk of one of these situations, caregivers have time to intervene in advance of physiological signs of deterioration. This can help them potentially avoid emergency interventions, such as the use of a ventilator for acute respiratory distress. It also supports the health system with capacity management, by providing early identification of potential bottlenecks caused by unexpected deterioration.

In addition to demonstrating its superior accuracy, the study concluded that the CLEW system generated 50-times fewer alarms than other leading systems. In busy and overburdened critical care environments, this can greatly reduce alarm fatigue and the associated cognitive burden on caregivers. Fewer alarms mean fewer interruptions, which as the study notes “creates a more calm and peaceful ICU environment.” The study found that on average, 98 percent of bedside monitoring alarms (147 of 150 per patient day) were false positives. Due to its less frequent interruptions and greater accuracy, it concluded that the use of the CLEW system “is one of the few available options that improves ICU burnout syndrome by reducing unnecessary workload.”

The innovative CLEW system is the first of its kind to be cleared by the FDA as a class II medical device. It is currently in use at a number of leading academic and health system networks.

Download a summary of the study

About CLEW Medical, Inc.

CLEW offers the first FDA-cleared, class II medical device, AI-based clinical predictive models for critical care as a part of its intelligent clinical surveillance platform. Its ability to offer accurate, predictive insights around patient deterioration allows health systems to optimize patient interventions, decreasing mortality and reducing complications & readmissions. Its proprietary models offer an optimal balance of precision and sensitivity, which results in fewer high-risk notifications, reducing alarm fatigue amongst providers and clinicians.

Matter Health for CLEW Medical
Matt Robbins
T: 617.874.5203

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