Focusing on developing participant-focused research methods, surfacing priorities, needs, hierarchies, and categories through the mental models of people directly affected by clinical and diagnostic research. Ian has a long history in Human-centered research and advocacy, diagnosed with a rare disease at 5 years old he has worked his whole career to bring together the shared experience of participants and the proven methods of product development.
Computational Social Choice Theory's Impact on the Collection and Understanding of Real-World Data
The fields of Computational Social Choice, UX Research, and product development have vastly outpaced the medical field in their ability to collect, categorize, and understand real-world data points to improve the lives of their "users." By implementing these lessons into the clinical landscape we can leap-frog years of systems growth and evolution without having to re-invent the wheel. For RWE to be effective, it must be representative of people's real lives, in their living rooms and around their dining tables, growing at the same pace as real life. How exactly can this be done? Case studies and an explanation of novel methodologies will pave the way.
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