Geisinger advances to final phase of national AI Health Outcomes Challenge
Geisinger, Medial EarlySign partnership one of seven entries chosen for final round
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.
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“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.”
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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.
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