Joint Proposal to Reduce Adverse Events and Avoidable Hospital Readmissions Selected Among Top 25 Submissions
The CMS AI Health Outcomes challenge provides innovators with the opportunity to demonstrate how AI tools may be implemented to predict health outcomes and keep patients healthy in hopes of more AI tools being considered for potential use in CMS Innovation Center payment and service delivery models. More than 300 entities submitted proposed AI solutions to the Challenge.
The joint Geisinger/EarlySign proposal – Reducing Adverse Events and Avoidable Hospital Readmissions by Empowering Clinicians and Patients – will seek to apply advanced AI and Machine Learning algorithms to Medicare administrative claims data. Doing so can lead to the development of models that predict unplanned hospital and skilled nursing facility (SNF) admissions within 30 days of discharge and adverse events such as respiratory failure, postoperative pulmonary embolism or deep vein thrombosis, and postoperative sepsis before they occur.
“Approximately 4.3 million hospital readmissions occur each year in the U.S., costing more than $60 billion, with preventable adverse patient events creating additional clinical and financial burdens for both patients and healthcare systems,” said David Vawdrey, Ph.D., Chief Data Informatics Officer at Geisinger. “Together with our partner EarlySign, we have forged a dynamic team that is rapidly developing novel solutions to achieve the Quadruple Aim of improving the patient experience of care, improving the health of populations, reducing cost, and improving clinical care provider satisfaction.”
“Geisinger’s experience and substantial unified data architecture (UDA) is the perfect complement to EarlySign’s proprietary data repository and suite of AI tools, enabling the rapid development and validation of effective machine learning models,” said EarlySign CEO Dr. Jeremy Orr. “These models are designed to integrate seamlessly with current clinical workflows as a decision support tool that can help improve patient outcomes and decrease healthcare costs.”
CMS will announce Stage 2 finalists for the AI Health Outcomes Challenge in April 2020. The final awardees and grand prize winner will be revealed in September 2020.
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 Boston, MA. For more information, please visit https://www.earlysign.com/. Follow Medial EarlySign on LinkedIn: Medial EarlySign and Twitter: @MedialEarlySign
Geisinger is committed to making better health easier for the more than 1 million people it serves. Founded more than 100 years ago by Abigail Geisinger, the system now includes 10 hospital campuses, a health plan with more than half a million members, a research institute and the Geisinger College of Health Sciences, which includes schools of medicine, nursing and graduate education. With more than 25,000 employees and 1,700+ employed physicians, Geisinger boosts its hometown economies in Pennsylvania by billions of dollars annually. Learn more at geisinger.org or connect with us on Facebook, Instagram, LinkedIn and Twitter.
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