LLMs as Bridges: Connecting Healthcare Data, Policy, and Operations in the AI Era with Yubin Park
Date: Feb 26th 2025 (10:30 am - 1:00 pm)
- 10:30 AM - 12:00 PM (Talk & Q&A)
- 12:00 PM - 1:00 PM (Lunch Reception)
Large Language Models (LLMs) are emerging as powerful tools for bridging traditional gaps between healthcare data science, policy implementation, and operational excellence. Drawing from real-world applications, including CMS policy analysis and Medicare Advantage research, this presentation demonstrates how LLMs are transforming healthcare analytics and decision-making processes.
Through practical case studies, we'll explore three key areas: automated analysis of large healthcare datasets, real-time policy impact assessment, and cross-domain knowledge synthesis. The discussion will highlight both successes and limitations encountered when deploying LLMs in healthcare settings, providing actionable insights for organizations looking to enhance their analytical capabilities.
This presentation will benefit healthcare administrators, data scientists, policy researchers, and clinicians interested in leveraging AI to improve healthcare delivery while maintaining rigorous scientific standards. Particular emphasis will be placed on practical implementation strategies and future directions for AI-augmented healthcare research.
Who is Yubin Park? Yubin Park, Ph.D., specializes in integrating healthcare data, policy, and operations through innovative AI solutions. As the founder and CEO of mimilabs, he applies large language models to bridge gaps in healthcare analytics. Formerly Chief Data and Analytics Officer at Astrana Health (Nasdaq: ASTH), Dr. Park developed strategies to unite diverse healthcare data for improved outcomes. With a Ph.D. in Machine Learning from UT Austin, successful exits of two healthcare AI startups, and a position as Adjunct Professor at Emory University, he combines academic insight with industry experience. A LinkedIn Top Voice, Dr. Park focuses on using AI to synthesize insights across the healthcare ecosystem, driving innovation and efficiency in the field. His work has been instrumental in developing AI-driven platforms for risk adjustment, quality measurement, and virtual care, demonstrating the practical applications of machine learning in healthcare. Dr. Park's approach to connecting siloed datasets with real-world insights has the potential to transform how healthcare organizations leverage data for decision-making and patient care.
Organizers: UMN Data Science Initiative and Business Advancement Center for Health (BACH) at Carlson School of Management
*RSVP Deadline: RSVP deadline of February 19th for in-person attendance and February 25th for virtual attendance or when event reaches capacity. Those who RSVP will receive a calendar invitation.
