Gigi Hirsch and R. John Glasspool join other innovation leaders to explore the challenges and potential of new technologies for patient care
San Mateo, Calif., May 9, 2018 – The second annual LIGHT Forum (Leaders in Global Healthcare and Technology) took place at Stanford University on May 8-9, 2018, convening a broad cross-section of 200 of the most influential decision-makers in healthcare – high-ranking executives, policymakers, and experts from leading medical research institutions – to discuss the current state of healthcare and how the industry is rapidly being reshaped by technologies like artificial intelligence (AI) and machine learning.
NEWDIGS Director Gigi Hirsch, MD, and R. John Glasspool, Special Advisor for the FoCUS Project, appeared on panels. Dr Hirsch spoke on the panel The Cutting Edge of Innovation – What’s State of the Art, What Does It Mean for Healthcare, and Mr Glasspool spoke on Patient Pathways in Cardiology: An Example of the Future.
Dr Hirsch’s panel explored the implications of information technologies, including natural language processing, artificial intelligence, and Big Data analysis, that were pioneered in other industries can be applied to challenges in healthcare R&D and patient care.
“It’s a really exciting time. A lot of the work we’re doing at MIT is centered around driving more value from the data, in the context of evidence generation,” said Dr Hirsch. “We just a project we’re very excited about called LEAPS…which is working with all the stakeholders across all the silos for one disease which we haven’t selected yet. At the core of this is an assumption that everyone one of the key stakeholders across the value chain—the biopharma companies, regulators, payers, providers, and patients—they all generate or could be generating data but none of them has all the data they need to in order to make the decisions the have to make. So [LEAPS] is about how we begin sharing data across the silos in a way that every player contributes and every player benefits.”
Mr Glasspool’s panel focused on untapped opportunities to use existing and new sources of data to improve healthcare quality and outcomes in cardiology. When asked if AI can help address cardiovascular disease in ways genetics can’t, Glasspool replied, “I classify disease pathways fourfold: Prevention, Personalization, Precision, and Phenotyping…I really believe that AI—small data and Big Data—will be able to tease out what are we dealing with in these particular disease areas. …In my ideal world we will get down to interception and prevention, … and instead of treating people randomly with even the best standard of care…we will be able to understand which patients are responding to what [treatments].”
The panels are available on the YouTube page for Roam Analystics, Forum co-sponsor, at https://www.youtube.com/channel/UCMNngCwOiaXN39OiNEfcQiA
Dr Hirsch’s panel: https://youtu.be/40xlESKs7AY
Mr Glasspool’s panel: https://youtu.be/BqAAOR7PhKM
LIGHT 2018: The Cutting Edge of Innovation: The State of the Art and What it Means for Healthcare
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LIGHT 2018: Patient Pathways in Cardiology: An Example of the Future
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The NEW Drug Development ParadIGmS (NEWDIGS) Initiative at MIT is an international “think and do tank” dedicated to delivering more value faster to patients, in ways that work for all stakeholders. NEWDIGS designs, evaluates and initiates advancements that are too complex and cross-cutting to be addressed by a single organization or market sector. Its members include global leaders from patient advocacy, payer organizations, biopharmaceutical companies, regulatory agencies, clinical care, academic research and investment firms. For more information, visit http://newdigs.mit.edu.
About the LIGHT Conference
The LIGHT Conference, a non-profit organization with a 501(c)(3) application pending with the Internal Revenue Service, was organized to develop content, convene thought leaders, and exchange best practices on the latest developments, challenges and opportunities shaping the healthcare industry through the rapid uptake of artificial intelligence and machine learning.