Session Chair Profile

Ph.D., Professor of Health Economics and Founding Director, UCSF Center for Translational and Policy Research on Personalized Medicine (TRANSPERS), Department of Clinical Pharmacy, UCSF

Biography
Kathryn A. Phillips PhD is Professor of Health Economics and Health Services Research, Department of Clinical Pharmacy, University of California San Francisco. She founded and leads the UCSF Center for Translational and Policy Research on Precision Medicine (TRANSPERS), which focuses on developing objective evidence on how to implement precision medicine into health care so that it is effective, efficient, and equitable. Kathryn has published over 150 peer-reviewed articles in major journals including JAMA, New England Journal of Medicine, Science, and Health Affairs. She has had continuous funding from NIH as a Principal Investigator for over 25 years and was recently awarded a 5-year, $5M NIH grant to examine payer coverage and economic value for emerging genomic technologies (e.g., cell-free DNA tests and tests based on polygenic risk scores). Kathryn serves on the editorial boards for Health Affairs, Value in Health, JAMA Internal Medicine, Genetics in Medicine; is a member of the National Academy of Medicine Roundtable on Genomics and Precision Health; and has served on the governing Board of Directors for GenomeCanada and as an advisor to the FDA, CDC, and the President’s Council of Advisors on Science and Technology. She has also served as an advisor to many diagnostics, sequencing, and pharmaceutical companies as well as venture capitalists. Kathryn is Chair of the Global Economics and Evaluation of Clinical Sequencing Working Group, and a member of an evidence review committee for the Institute for Clinical and Economic Review (ICER). Her work has been quoted by the Washington Post, Wall Street Journal, New York Times, CNBC, Reuters, Newsweek, and other major news outlets.


 Session Abstract – PMWC 2022 Silicon Valley


Track Chair:
Keith Yamamoto, UCSF

Patient-centric data – Real-World Evidence (RWE) and Real-World Data (RWD) - is becoming instrumental in the drug development process and for health care decisions in general. This data is not only informative for the process from discovery to new indications, clinical trial design, and drug development, it also can be of value to monitor post-marketing drug safety and for decision support in clinical practice. As the data becomes a decision driver, science companies and medical organizations are increasingly focused on leveraging RWD and RWE to not only better understand the patient populations using their drugs and the respective outcomes, but also to accelerate clinical decision support. This session will focus on the various aspects of integrating RWE and RWD to support drug development and clinical decision support

Sessions:

  • Realizing the Promise of Precision Medicine Using Real-world Evidence
    Session Chair: Atul Butte, UCSF
    - James Wall, Stanford
    - Nancy A. Dreyer, IQVIA
    - Mark Laabs, RCRF
    - John Concato, FDA
    - Rhonda Cooper-DeHoff, University of Florida
  • Opportunities and Challenges in Using Real World Data (RWD)
    Session Chair: Vivek Rudrapatna, UCSF
    - Kathy Giacomini, UCSF
  • How Are Patient Data Revolutionizing Precision Medicine?
    Session Chair: Clara Lajonchere, UCLA
    - Sharon Terry, Genetic Alliance
    - Farid Vij ,Ciitizen
    - Latha Palaniappan, Stanford
  • Regulatory Requirements and Challenges for Using RWE
    Session Chair: Sheila Walcoff, Goldbug Strategies
  • Government Partnerships: California Initiative to Advance Precision Medicine
    Session Chair: Julianne McCall, California Initiative to Advance Precision Medicine
    - Pablo Tamayo, UCSD
    - George M. Slavich, UCLA
  • Leveraging RWE to Create Value
    Session Chair: Kathryn A. Phillips, UCSF
    - Phil Febbo, Illumina
    - Suzanne Belinson, Tempus
    - Patricia Deverka, UCSF
    - Stacey Dacosta Byfield, OptumLabs
  • The Past, Present, and Future of RWE
    Session Chair: Keith Yamamoto, UCSF
    - Matthew Porteus, Stanford