Using an AI-Based Solution to Aid in Early Detection of High-Burden Disease
Lesson learned, realities and preliminary results with a real-world use case from Geisinger Health System and Medial EarlySign

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Scouring big medical data, machine learning-based algorithms can find subtle signs of high-risk trajectories for chronic and high-burden diseases and downstream complications. This creates early opportunities for clinicians and health care teams to intervene with personalized and preventative care.

Join us as Geisinger and Medial EarlySign discuss their joint efforts to deploy an artificial intelligence (AI) based solution in an active clinical environment to provide the most effective care for challenging health issues. They will review the processes and lessons learned related to deployment of a machine learning solution designed to get more value out of their data assets and enhance risk stratification, clinician decision-making, patient activation and resource allocation.

What You Will Learn:

• The relationship of clinical data, model performance characteristics and validation to early detection of high burden disease with the aid of a machine learning based solution.
• How effective internal communication across a complex organization — will lead to a more efficient implementation of an AI-based solution and faster realization of results.
• How to effectively work with a solution provider to assure alignment around having the right technology, service and support, and desired outcomes.

Speakers:

Keith Boell, D.O.
Associate Chief Quality Officer and Vice Chairman, Medical Institute
Geisinger Medical Center
Danville, PA

Rebecca Stametz, D.Ed, MPH
Associate Vice President
Geisinger Medical Center
Danville, Penn.

Jeremy Orr, MD, MPH
Cheif Executive Officer
Medial EarlySign
Boston, Mass.

Patrick Wells
Chief Customer Success Officer
Medial EarlySign
Boston, Mass.

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