MACHINE LEARNING & AI: A HIMSS EVENT
LAS VEGAS, NV - MARCH 5, 2018
Leonard D’Avolio, Ph.D. has spent the last 13 years in government, academia, philanthropy, and industry working to make the learning healthcare system a reality. He’s the co-founder of Cyft, an organization that uses data and AI to improve clinical care and operations. He is also an assistant professor at Brigham and Women’s Hospital and Harvard Medical School, an advisor to the Helmsley Charitable Trust Foundation and several healthcare startups, and a board member for Youth Development Organization. He helped improve childbirth across 70 clinics in India working with Atul Gawande at Ariadne Labs, created the infrastructure for the world’s largest genomic medicine cohort, and embedded the first clinical trial within an electronic medical record system for the Department of Veterans Affairs. His work has been funded by the National Cancer Institute, Department of Veterans Affairs, Department of Defense, Bill and Melinda Gates Foundation, National Library of Medicine, the Helmsley Charitable Trust Foundation.
After decades of lagging behind other industries in the use of data, healthcare is poised for its own data-driven transformation. Journalists describe a not-too-distant future where patients, phones, Fitbits, and physicians march hand-in-hand toward a healthier tomorrow.
But not everyone is jumping for joy.
Some healthcare organizations are so frustrated with the state of health IT that the American Medical Association’s CEO James Madara, MD, last year called digital health the “snake oil of the early 21st century.” Rather than improving care and boosting professional satisfaction, many digital tools, he wrote, don’t work that well, and actually impede care, confuse patients, and waste everyone’s time.
And then there’s machine learning. Or is it artificial intelligence? Or cognitive computing? Which is which or what is what?
In his closing keynote, Harvard Medical School professor Leonard D’Avolio, who previously led informatics at the VA, gets to the bottom of all this confusion and disgruntlement and examines what’s hype and what’s not. And how together, once a few very real barriers are eliminated, big data and machine learning will better serve doctors, patients and families, and contribute to improved healthcare.