Real-world Evidence in Health Data Sciences

This lecture discusses the importance of real-world evidence in health data sciences, focusing on observational research and data quality assessment. The instructor presents examples of major bleeding risk comparisons among atrial fibrillation patients and the challenges of reliability and reproducibility in analysis. The seminar also covers the OHDSI program, emphasizing the use of large-scale analytics to extract value from health data through open-source solutions. Various concepts such as negative controls, empirical p-values, and data standardization are explored to ensure the reliability of observational research.