TEL AVIV, Israel, April 28, 2020 /PRNewswire/ -- Medial EarlySign (earlysign.com) today announced the general availability of its new COVID Complications AlgoMarker™ which identifies individuals potentially at increased risk for having COVID-19 complications. With the goal of prioritizing patients for COVID-19 testing and treatment, the new AlgoMarker aids in triaging patients by reducing chart review time to determine whether they are at high risk for hospitalization, complications, and mortality.
Working together with health system partners in the US and Israel, the new AI algorithm has been shown to identify patients estimated to be at increased risk of suffering severe complications if infected with coronavirus. With data currently growing and updated daily to refine the model, EarlySign is making the new algorithm available to health systems at no cost for the software license and related services.
"Immediate action is critical to combat this pandemic, and we are taking urgent steps to massively impact human health," stated EarlySign CEO Jeremy Orr, MD, MPH. "Our goal is to provide health systems a fast, reliable, and no-cost approach to identify and connect with patients at highest risk. People must see their medical conditions addressed earlier while health systems can benefit from more efficient allocation of essential clinical resources."
"Our global community of healthcare providers comes together in times of crisis to provide care and comfort to those who are sick," commented Micah Thorpe, DO, Vice President Business Strategy for Northwest Permanente Medicine. "In partnership with EarlySign, we have rapidly deployed data assets to develop a functional solution that helps us meet our mission of delivering the highest quality and innovative care."
The COVID Complications AlgoMarker was developed on a very large US-based data set. This predictor is based on an earlier joint effort between Medial EarlySign and the Kahn-Sagol-Maccabi Research and Innovation Institute that used 27 years' worth of robust, anonymized electronic health record (EHR) data from leading Israeli HMO Maccabi Healthcare Services. Maccabi has 2.4 million members and runs over 100,000 patients visits a day. It has already applied that predictor to its own patient population, identifying approximately 40,000 people at high-risk for severe complications of COVID-19. Using the AlgoMarker, a health system's patient population is scored using their EHR data and is then stratified into three risk groups. This data is used to help test prioritization, decision support on triage of those patients reporting symptoms, and prioritization of follow up on home-care patients.
The model was originally trained to predict severe flu complications and adjusted based on global publications of COVID-19 risk factors and epidemiological records. Individuals are flagged by the model through analysis of dozens of routine medical parameters, including demographics, hospital admission history, prescribed medications, smoking history, and diagnoses associated with immunosuppression and chronic conditions such as respiratory and cardiac diseases.
"This initiative is consistent with EarlySign's mission to improve patient care and empower healthcare organizations by providing actionable clinical insights," added Ori Geva, Co-Founder and President of Commercial Strategy for Medial EarlySign. "We look forward to partnering with healthcare organizations across the healthcare continuum to help address this pandemic."
For immediate information about how to obtain the COVID Complications AlgoMarker, please contact EarlySign at [email protected].
About Medial EarlySign
Medial EarlySign helps healthcare systems with early detection and prevention of high-burden diseases. Their suite of outcome-focused software solutions (AlgoMarkers™) find subtle, early signs of high-risk patient trajectories in existing lab results and ordinary EHR data already collected during routine care. EarlySign's AlgoMarkers are currently helping clients identify patients at high risk for conditions such as lower GI disorders, prediabetic progression to diabetes, and downstream diabetic complications such as chronic kidney disease (CKD). The algorithmic models developed using the company's machine learning approach are supported by peer-reviewed research published by internationally recognized health organizations and hospitals. Founded in 2013, Medial EarlySign is headquartered in Tel Aviv, Israel with US headquarters in Colorado. For more information, please visit: https://earlysign.com/.
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SOURCE Medial EarlySign