Predictive Models, Bias, Health Equity, and COVID-19
January 19 @ 1:00 pm - 2:00 pm CST
Michael Pencina, PhD, Vice Dean for Data Science and IT & Professor of Biostatistics and Bioinformatics, Duke University School of Medicine. Modern health systems stand to derive substantial benefit from employing algorithm-based clinical decision supposed (ABCDS) tools. However, adoption has been slow and recent high profile failures have raised concerns about bias and inequity, lack of transparency and trust, as well as poor evaluation standards. Dr. Pencina discusses an objective framework for evaluation of ABCDS tools aimed at overcoming these barriers.
- January 19
1:00 pm - 2:00 pm
- Event Categories:
- 2021, ACO/Pop Health/Behavioral Health/Social Determinants, Care Management/Disease management, Chief Medical Officer/Chief of Staff, Clinical Informatics/CDS/Patient Safety, Data and Analytics/Business and Clinical Intelligence, Emerging Technologies/Innovation/Transformation/AI, Interest Groups, IT Leadership, Nursing Leadership, Quality Management/Performance Improvement, Strategic Planning, Teleconference