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FDA Issues EUA for COVID-19 Predictive Analytics Platform

The technology deploys AI-based algorithms that use machine-learning to identify respiratory failure and hemodynamic instability

Clinicians and AI researchers from CLEEW Med announced today that the US Food and Drug Administration (FDA) issued an Emergency Use Authorization (EUA) for the firm’s CLEWICU system.

"Healthcare providers need more than simple analytics. Systems need to integrate into the provider's workflow, offering ease of use and actionable data. The CLEWICU platform is designed to enable healthcare providers to monitor patient predicted risk levels across all units in real-time allowing for smart decision making about clinical resource allocation, ensuring prompt, proactive and efficient patient care," said Gal Salomon, CLEW CEO.

CLEWICUE uses predictive screening information to identify patients at increased risk of being diagnosed with respiratory failure or hemodynamic instability, common complications in severe coronavirus disease 2019 (COVID-19.)

The technology deploys AI-based algorithms that use machine-learning to identify respiratory failure and/or hemodynamic instability. The tool has potential implications for resource management, given identification of likely severe cases can be done hours in advance. This allows for further evaluation and, potentially, early intervention.

The AI models were trained on about 100,000 patients in intensive care units. The algorithm scales to cope with patient volume surges while working to reduce a caregiver's exposure risk with infected patients.

The technology’s web application is designed for access to patient data and tools for worklist, unit and multiunit views, featuring unit occupancy and patient risk level. The tool is intended to integrate practice between local and remote teams, for better workflow and resource decision making.

“CLEW provides real-time AI analytics platforms designed to help providers make better informed clinical decisions by predicting life-threatening complications across various medical care settings. CLEW's goal is to provide solutions that could improve outcomes and safety, streamline patient care, and efficiently handle regulations and penalties, ultimately lowering the cost of care. Originally developed and proven in the ICU, CLEW will develop machine learning models that have the potential to optimize clinical resources and guide health care providers in predicting patient deterioration, across all care settings,” the announcement concluded.

Related Coverage:

AI Could Play Key Role in Sorting through COVID-19 Research

Published: MAY 07, 2020 | JARED KALTWASSER

As investigators around the world race to develop therapies and vaccines in response to the coronavirus disease 2019 (COVID-19) epidemic, scientists at Northwestern University said artificial intelligence can play a key role in helping to sort through the thousands of studies being reported.

In a new paper published in the Proceedings of the National Academy of Sciences, Brian Uzzi, PhD, and colleagues said existing methods of locating studies that are most likely to be replicable will not work in an emerging pandemic.

They noted the current system for evaluating studies, the Defense Advanced Research Project Agency’s Systematizing Confidence in Open Research and Evidence (DARPA SCORE) program, relies on human expertise, and tends to take nearly a year.

“The standard process is too expensive, both financially and in terms of opportunity costs,” Uzzi said in a statement.

In addition to slowing down the research being evaluated, the process also takes up the precious time of the scientists reviewing the studies, who could otherwise be conducting their own research.

Read the full story.