The Centers for Medicare and amp; Medicaid (CMS) Artificial Intelligence Health Outcomes Challenge selected Geisinger as one of the seven finalists.
Geisinger joined hands with Medial EarlySign to predict unplanned hospital admissions, healthcare-associated complications, and readmissions occurring soon after hospital discharge using machine learning (ML) and artificial intelligence (AI). EarlySign is a leading machine learning-based solutions that help in the early prevention and detection of high-burden diseases. Collaboration between these two firms will lead to the evolution of a model that predicts the risk of such outcomes through 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., RN, Executive Vice President and Chief Innovation Officer, Geisinger and Founding Director, Steele Institute for Health Innovation, Geisinger.
“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 was announced in 2019 by the CMS Center for Medicare and Medicaid Innovation. More than 300 organizations offer AI strategies to forecast patient health outcomes for potential use. Submissions reflected several outcomes, including unplanned admissions due to heart failure, pneumonia, chronic obstructive pulmonary disease and other high-risk conditions, as well as adverse events such as infections acquired in hospitals, sepsis, and respiratory failure.
CMS judged each application on the basis of the model’s success and how well innovators visually showed how clinicians could use their model predictions to enhance patient care and results. The visual presentations were analyzed and assessed by clinicians from the American Academy of Family Physicians, a CMS partner in the AI Challenge. A CMS senior management panel reviewed the evaluations and chose the seven finalists.
The finalists will further improve their predictive models in the final stage of the competition while discussing implicit algorithmic biases that impact healthcare disparities., CMS will name the grand prize winner and runner-up by the end of April 2021.