Unlocking Machine Learning’s Potential to Transform Health Insurance: From Hype to Reality
San Diego, CA on June 21
Speaker: Professor John Guttag (Professor of Computer Science and Electrical Engineering at MIT)
Please RSVP for a delicious lunch and seminar sponsored by HEALTH[at]SCALE Technologies.
*** This event is by invitation only. Limited spaces are available. ***
We will confirm your seat based upon the timeliness of your response.
Date: June 21, 2018
Time: 12 Noon - 1:30 PM (Pacific Time)
Location: Hilton San Diego Bayfront (adjacent to San Diego Convention Center)
Meeting Room: Sapphire 410
1 Park Blvd, San Diego, CA 92101, USA
Executives and leaders from health insurance, managed care, and accountable care organizations.
Machine learning (ML) has generated significant attention as a key enabling technology with the potential to transform the management of health and wellness. The massive volumes of longitudinal data collected across healthcare encounters offer unprecedented opportunities to make healthcare delivery more proactive and personalized. In this talk, Professor John Guttag will describe how ML holds the promise of unlocking these opportunities and improving a broad range of healthcare transactions going forward. As an internationally acclaimed scholar and thought-leader for his pioneering work over the last two decades in building ML software systems for healthcare use cases, Professor Guttag will provide an overview of how ML can be leveraged to generate new knowledge and insights that drive improvements in the outcomes and economics of managed and accountable care. Professor Guttag will focus, in particular, on how healthcare-specialized ML offers new and differentiated capabilities relative to standard data analytics and conventional ML technologies that have found use in other domains; and discuss some of the major challenges specific to utilizing healthcare data for ML tasks. Professor Guttag will focus on use cases relevant to health insurance and ACOs: 1) steerage (predicting the right provider/facility for an individual patient), 2) interception (predicting a patient’s future care utilization based on a patient’s unique profile), and 3) personalized treatment (predicting a patient’s likely benefit from a particular treatment course).
Professor Guttag is the Dugald C. Jackson Professor of Computer Science and Electrical Engineering at MIT. He was previously Chair of Electrical Engineering and Computer Science at MIT. He currently leads the Data-Driven Inference Group at MIT—-one of the leading labs in advancing the state of the art in machine learning with a focus on health care applications. Professor Guttag is also the Chief Scientist and Co-Founder of HEALTH[at]SCALE Technologies.
About HEALTH[at]SCALE (www.healthatscale.com):
HEALTH[at]SCALE is on a mission to transform healthcare outcomes and economics by matching every patient to the right treatment by the right provider at the right time. We are deeply committed to the goal of achieving longer, healthier and happier lives, and continuously push the envelope on healthcare-specialized machine learning and artificial intelligence as a means for sustainable and affordable progress for patients, payers and providers. Our proprietary and patented machine intelligence technologies are designed by an award-winning team of faculty, engineers and clinicians; and uniquely target multiple opportunities along the care continuum to improve the management of complex populations across complex care networks.
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