Geisinger Runner-up in National AI Health Outcomes Challenge
Geisinger, Medial Earlysign Partnership Uses Artificial Intelligence to Improve Health Outcomes
Geisinger has been named runner-up out of more than 300 entries in the Centers for Medicare & Medicaid Services (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) to predict unplanned hospital admissions, readmissions occurring soon after hospital discharge, healthcare-associated complications, and mortality. The two entities collaborated to develop models that predict the risk of these outcomes using Medicare administrative claims data and created novel visualizations to explain the results in a clinician-friendly manner, a key component of AI implementation.
“We are honored to be recognized as a national leader in using artificial intelligence to improve health outcomes,” said David Vawdrey, Geisinger’s chief data informatics officer. “The opportunity to participate in the CMS competition has significantly broadened our capabilities to design and implement predictive models, which will ultimately help prevent unnecessary hospitalizations and complications and reduce healthcare costs.”
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Geisinger and EarlySign’s shared vision of innovation and their collective focus on patient-centered care garnered recognition by CMS for “consistent strong performance across all competition elements while generating the best prediction accuracy results.” Their ability to successfully communicate predictions to clinicians, known as AI explainability, was a key factor in their selection as runner-up.
“This achievement demonstrates the synergistic relationship Geisinger and EarlySign have in the journey to provide better care for patients,” said Ori Geva, co-founder and chief executive officer of Medial EarlySign. “This recognition is another validation that successful clinical AI solutions require deep understanding of clinical workflow, and expertise in clinical machine learning and clinical data.”
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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. 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.
Geisinger was chosen as one of seven finalists in November 2020. To select the winner and runner-up, CMS conducted a rigorous evaluation process, supported by a team of AI scientists. Clinicians from the American Academy of Family Physicians, a CMS partner in the AI Challenge, reviewed and scored the models’ explainability. Submissions were reviewed and winners selected by a panel of CMS senior leadership.