Geisinger Joins Forces with Medial EarlySign to Detect and Prevent...
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Geisinger Joins Forces with Medial EarlySign to Detect and Prevent High-Burden Diseases

By Healthcare Tech Outlook | Friday, July 19, 2019

Medical earlysignOri Geva, Co-Founder and CEO

Healthcare organizations are migrating from volume to value-based care, and partnering with relevant solution providers to receive outcome-focused care delivery and prevent the onset of high-burden diseases.

FREMONT, CA: The healthcare sector has greatly benefited from machine learning, which has enabled it to glean useful insights from the troves of data and make accurate predictions. Organizations such as Medial EarlySign have leveraged machine learning to develop cutting edge solutions that aid in the early detection and prevention of diseases. The company recently collaborated with Geisinger and its Steele Institute for Health Innovation to develop and deploy machine learning-based solutions to identify patients with the risk of contracting chronic and high-burden diseases.

Healthcare organizations are migrating from volume to value-based care, and partnering with relevant solution providers such as Medial EarlySign has armed them with practical tools to provide outcome-focused care delivery and prevent the onset of high-burden diseases.

Medial EarlySign enables healthcare organizations to detect and prevent high-burden diseases such as lower GI disorders, prediabetic progression to diabetes, downstream diabetic complications, chronic kidney disease (CKD), first coronary artery disease (CAD), and conditions of equivalent risk in their patients. Its suite of software solutions, including AlgoMarkers, is designed to identify the subtle, early signs of high-risk patient trajectories in the current lab results and EHR data collected throughout routine care.

Medical EarlysignKaren Murphy, Ph.D., RN, Executive Vice President and Chief Innovation Officer

Partnering with Geisinger’s Steele Institute for Health Innovation, Medial EarlySign’s LGI-Flag solution will be deployed to help healthcare practitioners identify patients who are at risk for lower GI disorders. LGI-Flag is an advanced software solution that analyzes medical data, including the changes in routine blood tests, to flag the patients who need further evaluation. Medial EarlySign reflects Geisinger’s efforts to revolutionize healthcare delivery by incorporating robust solutions designed to improve health, patient experience, care delivery, and affordability.

“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, Co-founder, 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.”

The integrated health services organization, Geisinger is popular for its innovative utilization of EHR and development of cutting-edge care delivery models, including ProvenHealth Navigator, ProvenCare, and ProvenExperience. The company serves over 3 million patients across 45 countries and comprises more than 30,000 workers, including 1,600 employed physicians, 13 hospital campuses, two research centers, and 583,000 member health plans. Geisinger has won national awards for integration, quality, and service. Apart from patient care, the company is committed to medical education, research, and community service.

Karen Murphy, Ph.D., RN, Executive Vice President and Chief Innovation Officer at Geisinger, said, “Leveraging Geisinger’s performance as a national leader in healthcare and its culture of innovation with Medial EarlySign’s expertise in machine learning and data analytics will enable us to identify, evaluate and intervene with high-risk patients earlier. 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.”

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