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Living in the Era of Big Data, Algorithms, and Inefficient Health Care: Stories from the Frontlines of Health Data Science

Shaun Grannis,  MD MS FAAFP FACMI

Friday, April 6 at 12:30 p.m. in IT 252

Abstract

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 has never been more promising. However, care must be taken to uncover and understand the capabilities and limitations of these resources. Electronic data is rife with biases and other data quality issues; machine learning and other classes of algorithms, when improperly applied, can produce spurious findings; and ultimately, humans who consume the fruits of these rapidly emerging amalgamations of “data plus algorithms” may be limited in their capacity to apply these findings. Dr. Grannis will share outcomes and lessons learned from recent projects leveraging “data plus algorithm” using unparalleled data from one of the country’s largest and longest-tenured health information exchanges. He will also highlight important future directions in the health data science field.

About Shaun Grannis

Dr. Shaun Grannis, MD MS FAAFP FACMI, is the Clem McDonald Scholar for Biomedical Informatics, Director of the Regenstrief Center for Biomedical Informatics, and Associate Professor of Family Medicine at the Indiana University School of Medicine. After studying Aerospace Engineering at MIT, Dr. Grannis pursued a career in medicine and medical informatics, joining the Regenstrief Institute in 2001. He co-leads the Informatics pillar for Indiana University’s Precision Health Initiative and collaborates closely with national and international partners to advance technical infrastructure and data-sharing capabilities. His research focuses on improving discovery and decision support in a variety of contexts by developing, testing, and implementing innovative approaches for data integration, patient matching, predictive modeling and other novel data science use-cases.