TEL AVIV, Israel, Jan. 13, 2020 /PRNewswire/ -- Medial EarlySign (earlysign.com), a leader in machine learning-based solutions to aid in the early detection and prevention of high-burden diseases, today announced its flu complications algorithm has been selected by Maccabi Healthcare Services as part of the Israeli healthcare organization's integrated strategy to enhance its flu vaccination campaign. The EarlySign investigational algorithm flags individuals at high risk for developing flu-related complications and is being used as part of a clinical study undertaken by Maccabi and EarlySign.
EarlySign's machine learning-based tool applies advanced algorithms to ordinary patient data, collected over the course of routine care. The flu complications algorithm uses this EHR data to identify and stratify unvaccinated individuals at high risk of developing flu-related complications, often requiring hospitalization.
Maccabi Healthcare Services is Israel's second largest HMO, covering approximately 2.3 million patients, operating 5 regional centers, including hundreds of branches and clinics throughout the country.
"According to the World Health Organization, flu kills between 250,000 and 500,000 people globally every year," said Prof. Varda Shalev, director of KSM Kahn-Sagol-Maccabi Research and Innovation Institute, founded by Maccabi Healthcare Services. "Due to the late arrival of influenza vaccines in Israel this year, the time we have to vaccinate patients this flu season - especially those at high risk for developing flu-related complications - is much shorter than usual. H1N1 flu could take a heavier toll this season, particularly on people at high risk for flu complications."
"We are delighted to extend our partnership with Maccabi in this clinical study to apply advanced machine learning solutions to help alleviate the human and financial cost of high-burden diseases," said Dr. Jeremy Orr, CEO of EarlySign. "This signifies another important step towards our ultimate goal - to help improve care and long-term survival rates of people at greatest risk through early identification and intervention."
Maccabi's clinical study using EarlySign's flu complications algorithm supports the Israeli HMO's commitment to investigating and implementing machine learning-based solutions to improve the health of populations. The program follows Maccabi's initial collaboration with EarlySign in 2016, to identify individuals at high risk of colorectal cancer who are non-compliant with screening guidelines.
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 in the course of 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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Director of Communications & Government Affairs
Maccabi Healthcare Services
SOURCE Medial EarlySign