Presentation Type: Industry Partner Sessions

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Presentation Time: 08:00 AM - 09:15 AM

Abstract Keywords: Privacy and Security, Data Modernization, Real-World Evidence Generation, Machine Learning, Education and Training

Healthcare is racing to harness AI, yet access to high-quality, privacy-preserving data remains the bottleneck for students, trainees, and investigators. In this Industry Partner session, Randi Foraker, PhD, MA, FAHA, FAMIA, FACMI (Professor & Chair, Biomedical Informatics, Biostatistics & Medical Epidemiology, University of Missouri School of Medicine) will lead a pragmatic discussion on using synthetic data to accelerate coursework, capstones, and IRB-ready protocols; stand up hands-on labs and hackathons without PHI; and rapidly iterate on ML/LLM pipelines while maintaining fidelity to real-world populations. You’ll see how synthetic data enables fast exploration and collaboration without exposing patient identities, so learners and researchers can move from question to insight in days, not months.

We’ll also highlight examples from academic programs and research cores that put synthetic data directly in learners’ hands, removing data-access friction, shortening time-to-analysis, and providing a safe “sandbox” for AI development and validation before requesting original data. Expect concrete guidance you can take home to launch student exercises, teach model development responsibly, and “de-risk” multi-site collaborations by sharing high-utility synthetic cohorts that mirror source populations.

Speaker:
Randi Foraker, PhD, MA, FAHA, FAMIA, FACMI
University of Missouri School of Medicine

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