Dr. Shaun Grannis, MD MS 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.
Living in the Era of Big Data, Algorithms, and Inefficient Health Care: Stories from the Frontlines of Health Data Science
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.