“Augmented Decision Making – How to Implement Machine Learning Prediction into Research and Clinical Care”
Thursday, Jan. 16 at 1:00 pm
Clinical decision-making is dominated by hypothetical-deductive reasoning, individual judgment, and heuristics. These factors can lead to bias, error, and preventable harm. Traditional predictive analytics and clinical decision-support systems are intended to augment decision-making, but their clinical utility is compromised by time-consuming manual data management and suboptimal accuracy. These challenges can be overcome by automated artificial intelligence models fed by livestreaming electronic health record data with mobile device outputs. This approach would require data standardization, advances in model interpretability, careful implementation and monitoring, attention to ethical challenges involving algorithm bias and accountability for errors, and preservation of bedside assessment and human intuition in the decision-making process.
Azra Bihorac, MD MS FASN FCCM is R. Glenn Davis Professor of Medicine, Surgery and Anesthesiology and Director of Precision and Intelligence in Medicine Partnership (PrismaP), a multidisciplinary research group of experts in data science, AI, and clinical informatics at the University of Florida. Her research focus is on the development and implementation of intelligent clinical decision-making systems and technologies to optimize health care system delivery.