Predicting What Comes Next: Hospital Readmission Risk Modeling using Claims Data
By Gilma Adunas-Rivas, Lead Data Analytics Specialist at TennCare
Hospital readmissions are costly and often associated with adverse outcomes for patients, making accurate risk prediction a priority in value-based care. This talk explores how machine learning models utilize administrative claims data to effectively forecast readmission risk. We’ll cover key steps in the modeling process, including data preparation and algorithm selection, while addressing real-world challenges like interpretability and integration into care workflows. Attendees will learn how predictive analytics can support early intervention and improve patient outcomes through data-driven decision-making.
Gilma Adunas-Rivas is a lead data analytics specialist in the Division of Quality Improvement at TennCare, where she applies quantitative methods to help improve the quality of healthcare for Tennesseans. She holds a master’s degree in Physics from Vanderbilt University and brings a research-focused background in biophysics and medical imaging to her work in public health. A longtime Nashville resident, Gilma enjoys spending time with family and friends, and finds joy in cooking and music.
As always free food and drinks. Come at 6PM to socialize and 6:30pm is the presentation.
Sponsored by Nashville Software School
