EHR Data Processing and Analytics for Research and Real-World Evidence Discovery from A to Z

This short course will present details of electronic health record (EHR) data extraction, cleaning, processing and analytics for scientific discoveries and evidence generation. The use of EHR data is becoming more prevalent for research and deriving real-world evidence for clinical decision or policy-making. However, analysis of EHR data has many unique features due to how the EHR data are collected and processed. This short course covers many important topics related to using EHR data for research and scientific discoveries that include data extraction, cleaning, processing, making inference, and predictions based on many years of practical experience of instructors and their collaborators. Statistical and machine learning approaches will also be presented for EHR data extraction, cleaning, analysis and prediction. The real-world examples from multiple EHR projects will be used to demonstrate the basic concepts and methodologies. Additionally, since the research project of EHR Big Data often requires a large multidisciplinary team, the approaches for project management are necessary and will be also covered in this course. This short course targets at researchers and practitioners from academia, industry and government agencies who have background and basic training (ideally MS degree) in statistics, biostatistics, or data science.