The value of data in healthcare is undeniable and realized when raw information is successfully converted into knowledge that changes clinical practice. To drive value improvements and ensure that the right patient receives the right care requires the right data in combination with the right data analytics. This session will cover various aspects and challenges of data science in hospitals and health systems that drive healthcare with better outcomes.
Biography
Dr. Kasarskis leads collaborative projects aimed at new therapeutics and diagnostics, and his research focuses on developing and applying technology to several areas including pathogen surveillance, pharmacogenomics, and the genetics of sleep. He has over a decade of experience managing research and technology development projects in software engineering, drug development, human and mouse genetics, and other biological research applications. Prior to Mount Sinai, he held positions at Pacific Biosciences, Sage Bionetworks, and Merck. Dr. Kasarskis holds a PhD in Molecular and Cellular Biology from UC Berkeley as well as a BS in Biology and a BA in Chemistry from the University of Kentucky.
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Finding The Molecules Behind Clinical Presentation
Clinical heterogeneity driven by environment and genetics is ubiquitous and drives work and cost in health care delivery. This heterogeneity is a proximal result of the state of molecular networks in patients. In the Mount Sinai Health System we are streamlining paths to describing and targeting the network state of unique patient subsets.
Biography
Christopher P. Boone has a career-long history as a dynamic, innovative thought leader and a public voice on the power of real-world data and health informatics and its ability to radically transform the U.S. health care delivery system from a healthcare quality and clinical research perspective. Boone currently serves as the Vice President and Global Medical Epidemiology and Big Data Analysis Lead at Pfizer, an adjunct professor at New York University’s Robert F. Wagner Graduate School of Public Service, an active board member of several influential organizations, and a co-founder of a few start-up companies. Most recently, he served as the Vice President of Real-World Data & Analytics at Pfizer. Boone has been recognized as a 2019 Top 100 Innovator in Data & Analytics, a 2018 Emerging Pharma Leader by Pharmaceutical Executive, and a 2017 Top 40 Under 40 in Minority Health Honoree by the National Minority Quality Forum.
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Big Data Analytics Matters In Big Pharma
As costs soar and patients wait, forward-looking pharmaceutical companies are exploring new approaches to developing drugs faster and with value in mind. Dr. Boone will explore the use of advanced analytical methods with real-world evidence for regulatory and clinical decision-making that create business value and impact.
Biography
Sharat Israni previously was Executive Director, Data Science, at Stanford Medicine. A long-serving Technology executive, Sharat’s teams pioneered the use of “Big Data.” He served as VP of Data at Yahoo! (1999-2008) and Intuit (2010-13), which pioneered “Big” Data Science to re-invent their products. He led Digital Media systems for broadcast/interactive TV at Silicon Graphics; and Data teams at IBM and HP. Sharat has been PI for NSF, NIH and RCUK workshops on Data Science topics in Biomedicine, and is a peer-reviewer of some scientific journals.
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A Next-Gen Research Computing Capability
With the advent of AI and full knowledge-based inquiry, biomedical and health researchers are seeing a chasm between their existing and needed computing capabilities. Bridging this chasm needs some paradigm shifts. We describe some key elements of UCSF’s next-gen capabilities, and illustrate their value in Imaging Sciences.
Biography
Sharmila Majumdar’s research work on imaging, particularly magnetic resonance and development of image processing and analysis tools, has been focused in the areas of osteoporosis, osteoarthritis, orthopedic imaging, and lower back pain. Her more recent focus has been on artificial intelligence applied to biomedical imaging. Her research is supported by grants from the NIH and corporate entities, and is diverse – ranging from technical development to clinical trials. She was selected as a fellow of the American Institute of Medical and Biological Engineers in 2004 and a fellow of the International Society of Magnetic Resonance in Medicine in 2008. In 2007, the UCSF Haile T. Debas Academy of Medical Educators at UCSF awarded her the “Excellence in Direct Teaching and/or Excellence in Mentoring and Advising Award”. She was awarded the ISMRM Gold medal in 2016. She has published extensively in highly regarded journals and serves as a reviewer and on the Editorial Board of multiple scientific journals.
Talk
A Next-Gen Research Computing Capability
With the advent of AI and full knowledge-based inquiry, biomedical and health researchers are seeing a chasm between their existing and needed computing capabilities. Bridging this chasm needs some paradigm shifts. We describe some key elements of UCSF’s next-gen capabilities, and illustrate their value in Imaging Sciences.
Biography
Wendy Nilsen’s work focuses on the intersection of technology and health. This includes a wide range of methods for data collection, advanced analytics and the creation of effective cyber-human systems. Her interests span the areas of sensing, analytics, cyber-physical systems, information systems, big data and robotics. More specifically, her efforts include: serving as cochair of the Health Information Technology Research and Development working group of the Networking and Information Technology Research and Development Program; the lead for the NSF/NIH Smart and Connected Health announcement; convening workshops to address methodology in technology in health research; serving on numerous federal technology initiatives; and, leading training institutes. Prior to joining NSF, Wendy was at the National Institutes of Health.
Biography
Jonathan Carlson leads a team of engineers and AI researchers at Microsoft Healthcare. The team focuses on modeling and mapping T-cells to their cognate antigens, with the aim of developing diagnostics and therapeutics based on measured and inferred immune repertoire specificity. As a researcher at Microsoft, he has published broadly in the areas of virology and immunology, largely through the lens of modeling within-host virus evolution. He is the recipient of the 2017 Bonnie Mathieson Young Investigator Award for outstanding research in HIV vaccines, and the 2009 University of Washington and US Council of Graduate Schools (finalist) Distinguished Dissertation Awards. Dr. Carlson received his A.B. in Biology and Computer Science from Dartmouth College in 2003, and his Ph.D. in computer science and engineering from the University of Washington in 2009.
Biography
Sonoo Thadaney Israni Co-chairs the National Academy of Medicine’s AI in Healthcare Working Group + co-shepherds their Technology across the Lifecourse Group. Co-hosted conferences at Stanford University: Human & Artificial Intelligence for Diagnostics; AI in Medicine: Inclusion & Equity; AI in Healthcare: The Hope, The Hype, The Promise, The Peril (launching NAM publication, she co-led). She serves on the AAMC Restorative Justice for Academic Medicine Committee, teaching curricula to address diversity in healthcare. 25+ years in Silicon Valley, now a Stanford intrapreneur for 10+ years - launching centers and programs: MSc. in Community Health and Prevention Research, Stanford WSDM (Women and Sex Differences in Medicine) Center, Diversity-First Gen Office, Restorative Justice Pilot etc. Teaches coursework in Leveraging Conflict for Constructive Change, Leadership Skills and Mediation. She co-chairs the Commission on Juvenile Justice Delinquency and Prevention (San Mateo County).
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Machine Learning in Healthcare: How It Can Serve Patients, fRamilies (Friends and Families) and The Clinical Team
Precision medicine requires granularity at the patient level and managing the unintended consequences of Healthcare AI means preventing another “Weapon of Math Destruction” and not further “Automating Inequality.” Prioritizing equity and inclusion in AI- healthcare is the proverbial ounce of prevention, rippling straight to the bottom line.