Dr. Shaun Grannis, MD, MS, FFAFP, FACMI, is Director of the Regenstrief Center for Biomedical Informatics and Associate Professor of Family Medicine at the Indiana University School of Medicine. He co-leads the Informatics pillar for Indiana University’s Precision Health Initiative and collaborates closely with national and international health stakeholders to advance technical infrastructure and data-sharing capabilities. His research focuses on developing, testing, and implementing novel patient matching approaches and other data integration, NLP, and machine learning strategies to improve discovery, decision support, and health outcomes in a variety of contexts.
8 – Real-World Examples Leveraging NLP, Big Data, and Data Science to Improve Population Health and Individual Care Outcomes (AI, technical)
With the unrelenting exponential growth in the volume of health and health-related electronic data, the potential to rapidly and accurately monitor, predict, intervene, and ultimately improve human health and care processes has never been more promising. However, real-world examples leveraging these technologies in an operational context are nascent, and care must be taken to realize the benefits of these resources and methods. Dr. Grannis will share outcomes and lessons learned from recent initiatives supporting precision and population health in the context of one of the country’s largest and longest-tenured health information exchanges. Real world examples will include leveraging Natural Language Processing (NLP) and machine learning to identify patients at risk for high resource utilization, early identification of cancer cases, managing patients in need of social determinants of health wrap-around services, and automated notifiable disease case identification.