BIG DATA & HEALTHCARE ANALYTICS: A HIMSS EVENT
BOSTON, MA - OCTOBER 23-24, 2017
Leonard D’Avolio, Ph.D. is an assistant professor at Harvard Medical School and Brigham and Women’s division of general internal medicine and primary care. He is the CEO and co-founder of Cyft, a company based on years of his research optimizing machine learning and natural language processing to improve healthcare. He is also an advisor to Ariadne Labs and the Helmsley Charitable Trust Foundation, board member for Youth Development Organization, a researcher, and writer.
He previously led informatics for the Department of Veterans Affairs’ precision medicine initiative (the Million Veteran Program) and the first clinical trial embedded within an electronic medical record system. He founded Ariadne Labs’ informatics team and led their partnership strategy as well as the creation of a mobile phone-based system that uses real time data feedback to improve neonatal care in Uttar Pradesh, India.
He has won awards for his efforts putting health data to work for veterans and is an invited speaker and writer on the topic of healthcare IT innovation by venues such as TEDMED, InformationWeek, and Scientific American. His work has been funded by several agencies and organizations including the Department of Defense, the Department of Veterans Affairs, the Agency for Healthcare Research & Quality, the National Cancer Institute, Helmsley Charitable Trust Foundation and the Bill and Melinda Gates Foundation.
The promise and potential for artificial intelligence in healthcare is very real. Yet we seem to be blowing it, says Harvard professor Len D’Avolio.
These technologies are important because they help us learn from our data – but this is something healthcare does not do very well. Medical errors, for example, are blamed for killing between 210,000 and 400,000 annually.
With that in mind, D’Avolio shares a curated list of the sometimes costly, usually embarrassing mistakes he’s made during a dozen years of trying to make these technologies work for healthcare.
He shares these lessons not out of frustration but with enthusiasm for what’s to come.
These technologies eventually will become an integral part of how we identify patients in need of attention, reduce waste, and recommend more appropriate pathways of care. The sooner we start to use them more effectively, the sooner we’ll improve clinical and financial outcomes.