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Geisinger taps EarlySign for new range of AI solutions to identify patients at risk of high-burden diseases

Multiyear partnership to apply lower GI algorithms as first step in improving patient outcomes through personalized and population health strategies


News provided by

Geisinger; Medial EarlySign

Jul 10, 2019, 10:06 ET

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DANVILLE, Pa. and TEL AVIV, Israel, July 10, 2019 /PRNewswire/ -- Medial EarlySign™ (earlysign.com), a leader in machine learning-based solutions to aid in early detection and prevention of high-burden diseases, today announced it is partnering with Geisinger and its Steele Institute for Health Innovation to develop and deploy a suite of machine learning-based solutions to identify individuals at risk of a range of chronic and high-burden diseases.

Partnering with Geisinger's Steele Institute for Health Innovation, EarlySign's LGI-Flag™ solution will be deployed to help healthcare practitioners identify patients who are at risk for significant lower GI disorders. LGI-Flag is an advanced software solution that analyzes medical data, including changes in routine blood tests, to flag those who will benefit from further evaluation. Reflecting the Steele Institute's efforts to transform healthcare delivery by implementing solutions that improve health, patient experience, care delivery and affordability, the partnership will also explore additional opportunities to benefit patients and their care providers.

"Leveraging Geisinger's performance as a national leader in healthcare and its culture of innovation with EarlySign's expertise in machine learning and data analytics will enable us to identify, evaluate and intervene with high-risk patients earlier," said Karen Murphy, PhD, RN, executive vice president and chief innovation officer at Geisinger. "This collaboration will help us potentially save lives and improve the care we provide patients by deepening our experience with AI and identifying new ways to integrate it into daily clinical care."

LGI-Flag has been used in healthcare systems around the world since 2015 to identify patients at risk for lower GI disorders associated with chronic occult bleeding. The technology will form the basis for Geisinger and EarlySign to address similar opportunities with other acute and chronic diseases. The partnership will benefit from Geisinger's innovation infrastructure, unique data assets and world-class care teams. 

"EarlySign's technology and the LGI-Flag solution will potentially assist our teams to more quickly identify significant lower GI disorders and intervene earlier than we historically have been able to," said Keith A. Boell, DO, associate chief quality officer at Geisinger. "We look forward to advancing our use of this technology while leveraging our experience to help more patients benefit from these life-changing medical advances."

"We are delighted to be partnering with Geisinger, which shares our commitment to innovation and to offering the most effective care for challenging health issues," said Ori Geva, cofounder and CEO of Medial EarlySign. "This is the first step of our ultimate goal: enabling healthcare systems to identify and connect with those high-risk patients and engage with them early enough via interventions that may prevent or delay disease progression."

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, downstream diabetic complications, first coronary artery disease (CAD) and equivalent events, and chronic kidney disease (CKD).  As healthcare systems transition from volume to value-based care, EarlySign partners with health care organizations to support outcome-focused care delivery, while potentially preventing or delaying the onset of high-burden diseases, downstream complications, and their associated costs. The company's machine learning platform has been supported by peer-reviewed research published by internationally recognized health organizations and hospitals. Founded in 2009, Medial EarlySign is headquartered in Tel Aviv, Israel with US headquarters in Boston, MA. For more information, please visit http://us.earlysign.com/.

Follow Medial EarlySign on LinkedIn: Medial EarlySign and Twitter: @MedialEarlySign

Media Relations Contact:
Ellie Hanson
[email protected] 
929-222-8006

About Geisinger
Geisinger is an integrated health services organization widely recognized for its innovative use of the electronic health record and the development of innovative care delivery models such as ProvenHealth Navigator®, ProvenCare® and ProvenExperience®. As one of the nation's largest health service organizations, Geisinger serves more than 3 million residents throughout 45 counties in central, south-central and northeast Pennsylvania, and also in southern New Jersey at AtlantiCare, a Malcolm Baldrige National Quality Award recipient. In 2017, the Geisinger Commonwealth School of Medicine and Geisinger Jersey Shore Hospital became the newest members of the Geisinger Family. The physician-led system is comprised of approximately 30,000 employees, including nearly 1,600 employed physicians, 13 hospital campuses, two research centers, and a 583,000-member health plan, all of which leverage an estimated $12.7 billion positive impact on the Pennsylvania and New Jersey economies. Geisinger has repeatedly garnered national accolades for integration, quality and service. In addition to fulfilling its patient care mission, Geisinger has a long-standing commitment to medical education, research and community service. For more information, visit www.geisinger.org. 

CONTACT: Matthew Van Stone
[email protected]
570-808-3344

SOURCE Geisinger; Medial EarlySign

Related Links

https://www.geisinger.org

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