Geisinger, Medial EarlySign partnership one of seven entries chosen for final round
DANVILLE, PA – November 18, 2020 – Geisinger has been selected as one of seven finalists in the Centers for Medicare & Medicaid (CMS) Artificial Intelligence Health Outcomes Challenge.
Geisinger partnered with Medial EarlySign, a leader in machine learning-based solutions to aid in early detection and prevention of high-burden diseases, to use artificial intelligence (AI) and machine learning to predict unplanned hospital admissions, readmissions occurring soon after hospital discharge, and healthcare-associated complications. The two entities collaborated to develop models that predict the risk of these outcomes using Medicare administrative claims data.
“This partnership enabled cross-disciplinary collaboration where both Geisinger and Medial EarlySign leveraged their strong healthcare and data science expertise to solve problems that can help fundamentally transform the healthcare delivery system,” said Karen Murphy, Ph.D., R.N., Geisinger’s chief innovation officer and founding director of Geisinger’s Steele Institute for Health Innovation. “The types of predictive models and computer user interfaces developed through the CMS AI Health Outcomes Challenge have enormous potential to improve patient outcomes, enhance clinician satisfaction, and reduce healthcare costs.”
“Along with Geisinger, being chosen as a finalist is an accomplishment we are humbled to receive,” said Ori Geva, co-founder, and chief executive officer of Medial EarlySign. “Geisinger’s deep understanding and commitment to patient care augmented with our machine learning modeling allowed us to excel as a team. This challenge has proven to us the depth and value of our medical AI modeling framework and the power it gives our healthcare clients to handle complex clinical predictive modeling at scale.”
The CMS AI Health Outcomes Challenge launched in 2019 with more than 300 entities proposing AI solutions for predicting patient health outcomes for potential use by the CMS Center for Medicare and Medicaid Innovation. Submissions aimed to forecast a variety of outcomes, including unplanned admissions related to heart failure, pneumonia, chronic obstructive pulmonary disease, and various other high-risk conditions; and adverse events such as hospital-acquired infections, sepsis, and respiratory failure.
CMS evaluated each submission based on the model’s performance and how well innovators visually demonstrated how clinicians could use their model forecasts to improve patient care and outcomes. Clinicians from the American Academy of Family Physicians, a CMS partner in the AI Challenge, reviewed and evaluated the visual displays, and a panel of CMS senior leadership reviewed the assessments and selected the seven finalists.
In the final stage of the competition, the finalists will further refine their predictive models while addressing implicit algorithmic biases that impact health disparities. CMS will announce the grand prize winner and runner-up by the end of April 2021.
For more information on Geisinger’s work with artificial intelligence and machine learning, visit geisinger.org/innovation-steele-institute/innovative-partners/ai-and-deep-learning-lab.
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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 nine hospital campuses, a 550,000-member health plan, two research centers, and the Geisinger Commonwealth School of Medicine. With nearly 24,000 employees and more than 1,600 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.
About Medial EarlySign™
Medial EarlySign helps healthcare systems keep patients healthier longer. EarlySign accelerates the successful use of AI and Machine Learning solutions within clinical workflows with insights-driven from real-world data allowing early detection and prevention of high-burden complications and conditions. With a focus on better outcomes and reduced costs, EarlySign partners with clients to deploy one or more of its pre-built, outcome-focused solutions (AlgoMarkers™) for augmenting early detection of cancers, indicating chronic diseases progression and deterioration, stratifying risks for unplanned admissions and adverse events and complications from infectious diseases. EarlySign also partners to co-develop custom predictive models using its AI Framework and rich clinical data expertise. The company’s machine learning results have been supported by peer-reviewed research published by internationally recognized health organizations and hospitals. Founded in 2009, Medial EarlySign is headquartered in Israel and the US. For more information, please visit us at earlysign.com. Follow Medial EarlySign on LinkedIn and Twitter.