Healthcare technology assessment plays a key role in shaping healthcare policy decisions with respect to granting access to new healthcare programs. Analytics are critical for informing these decisions. Julia’s performance offers significant advantages especially for complex health economic models. This talk will highlight opportunities for bridging fields and forming a Julia community of data scientists for the advancement of healthcare technology assessment analytics.
Few in the healthcare technology assessment data science community use Julia for their analytics. I have found the many qualities of the language (e.g., thoughtful design, performance, and open-source) could transform how data science is conducted in my field. I would like to find others in the Julia community who would have an interest in building a network and community of data scientists that contribute to advancing our healthcare technologies assessment analytics. These types of analytics include foundational epidemiology, healthcare database studies, transmission dynamic differential equation infectious disease models, cost off illness studies, meta-analyses, budget impact models, and cost-effectiveness models.
Erik Dasbach joined Merck 20+ years ago and his work experiences have included mathematical modeling, clinical trial design and analytics, population health and epidemiologic studies, patient reported outcome measure development and analytics, real-world database analytics, and platform development for sharing data, models, and analytics.
Prior to joining Merck, Erik worked in a variety of scientific and technology roles in the healthcare industry including the Centers for Disease Control and Prevention, the National Cardiovascular Network, the hospital industry, business development, and software development.
Erik received his Ph.D. from the University of Wisconsin-Madison in Industrial & Systems Engineering with a specialty in decision sciences and health technology assessment.