Geisinger AI to Target Patients at High Risk for Chronic Diseases

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Geisinger AI to Target Patients at High Risk for Chronic Diseases

Geisinger will use artificial intelligence (AI) solutions for early detection and prevention of high-burden disease. The technology will allow for better population health management in Geisinger’s health system.

 

The partnership with Medial EarlySign aims to develop and deploy a suite of machine learning-based solutions to identify individuals at risk for lower gastrointestinal (GI) disorders associated with chronic occult bleeding.

 

The software uses machine learning techniques to analyze medical and electronic health data that is collected as a part of routine care. Patients on a high-risk trajectory are identified through routine lab results (e.g., blood tests) and other early signs of risk. The software then flags these patients for providers to indicate which patients would benefit from further evaluation, preventive care, and possible disease management. Peer-reviewed research has validated the software’s machine learning strategies, and it has been internationally recognized by health organizations and hospitals.

 

“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,” Keith A. Boell, DO, associate chief quality officer at Geisinger said in a public statement. “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.”

 

The partnership with Geisinger and its Steele Institute for Health will help providers identify patients who are at risk for significant lower GI disorders, allowing for early intervention and preventive care strategies. Because Geisinger is one of the nation’s largest health service organizations and sees over three million patients, thousands will potentially benefit from early detection. This population health management strategy aligns with the Steele Institute of Health’s goal to transform healthcare delivery by implementing solutions that improve health, patient experience, care delivery, and affordability.

 

“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.”

 

Medial EarlySign’s machine learning platform has expanded its capabilities beyond lower GI disorders to include early detection of prediabetes, coronary artery disease, chronic kidney disease, and downstream diabetes complications. While other AI software and technologies are currently being used to predict risk of aortic aneurysmmacular degeneration, and hospital readmissions, there is hope that the partnership will help expand AI’s capabilities even further. They plan to look for additional opportunities to target preventive care and early detection to benefit patients and providers in identifying other chronic and acute diseases.

 

“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.”

 

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