Precisely Practicing Medicine From 700 Trillion Points of Data


Wednesday, February 22, 2022 at 11am EST AI is driving the creation of a new system of precision medicine. Discover how using real world data leads to safer, more cost-effective therapeutic interventions for patients.

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Event overview:

There is an urgent need to take what we have learned in our new data-driven era of medicine and use it to create a new system of precision medicine, delivering the best, safest, most cost-effective preventative, or therapeutic intervention at the right time, for the right patients.

Dr. Atul Butte, a computer scientist and pediatrician, will highlight his center’s recent work on integrating electronic health records data across the entire University of California Health System. He will delve into how analytics on “real world data” can lead to evidence for drug efficacy, savings from better medication choices, and new methods to teach intelligence—real and artificial—to more precisely practice medicine, especially in the era of COVID-19.

3 Key take-aways

  1. Illustrate how artificial intelligence can be used to create a new system of precision medicine.
  2. Explain how integrating electronic health records data can help assess health care quality.
  3. Discuss ways analytics on real world data lead to drug efficacy evidence and better medication choices for patients.


Atul Butte
Distinguished Professor and Chief Data Scientist
University of California

Atul Butte, MD, PhD, is the Priscilla Chan and Mark Zuckerberg Distinguished Professor and inaugural director of the Bakar Computational Health Sciences Institute ( at the University of California, San Francisco (UCSF). Dr. Butte is also the chief data scientist for the entire University of California Health System, with 20 health professional schools, six medical centers, 12 hospitals, and over 1,000 care delivery sites.

Dr. Butte has been continually funded by NIH for 22 years, is an inventor on 24 patents, and has authored over 200 publications, with research repeatedly featured in the New York Times, Wall Street Journal, and Wired Magazine. Dr. Butte was elected into the National Academy of Medicine in 2015, and in 2013, he was recognized by the Obama administration as a White House Champion of Change in Open Science for promoting science through publicly available data.

Dr. Butte is also a founder of three investor-backed data-driven companies: Personalis (IPO, 2019), providing medical genome sequencing services; Carmenta (acquired by Progenity, 2015), discovering diagnostics for pregnancy complications; and NuMedii, finding new uses for drugs through open molecular data.

Dr. Butte trained in computer science at Brown University, worked as a software engineer at Apple and Microsoft, received his MD at Brown University, trained in pediatrics and pediatric endocrinology at Children's Hospital Boston, then received his PhD from Harvard Medical School and MIT.

Lucas Glass
Vice President, Analytics Center of Excellence

Lucas Glass is the vice president of the IQVIA Analytics Center of Excellence (ACOE). The ACOE is a team of over 200 data scientists, engineers, and product managers that research, develop, and operationalize machine learning and data science solutions within the R&D space.Glass has launched over a dozen machine learning offerings within R&D, such as site recommender systems, trial matching solutions, enrollment rate algorithms, drug target interactions, drug repurposing, and molecular optimization.Glass’ machine learning research, which is dedicated to R&D, has been accepted at AAAI, WWW, NIPS, ICML, JAMIA, KDD, and many others.

Glass started his career in pharmaceutical data science 15 years ago at Center (Galt) working on pharmacovigilance data mining algorithms. Since then, he has worked at the US Department of Justice in healthcare fraud, several small startups, and TTC, llc, which was acquired by IMS in 2012.

Glass holds a BA in Physics from Boston University, a MS in biostatistics from Drexel University, and is a PhD candidate at Temple University, where he is researching deep learning embedding techniques on large-scale healthcare data.

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