CANCELED: Clinical Evidence Generation Using Electronic Health Records Data

The widespread adoption of electronic health records (EHR) as a means of documenting medical care has created a vast resource for the study of health conditions, interventions, and outcomes in the general population. Using EHR data for research facilitates the efficient creation of large research databases, execution of pragmatic clinical trials, and study of rare diseases. Despite these advantages, there are many challenges for research conducted using EHR data. To make valid inference, statisticians must be aware of data generation, capture, integration, and availability issues and utilize appropriate study designs and statistical analysis methods to account for these issues. This short course will introduce participants to the basic structure of EHR data and analytic approaches to working with these data through a combination of lectures and hands-on exercises in R. The first part of the course will cover issues related to the structure and quality of EHR data, including different data types, opportunities and challenges, and methods for extracting variables of interest. In the second part of the course, we will discuss statistical methods to mitigate data quality issues arising in EHR, including confounding, error in EHR-derived covariates and outcomes, and data integration across multiple clinical practices. Participants will explore synthetic EHR-derived data sets to gain familiarity with the structure of EHR data and statistical tools for analyzing EHR data. R code will be provided for implementation of the presented methods, and hands-on exercises will be used to compare results of alternative approaches. This short course is of interest to researchers without prior experience working with EHR data as well as more experienced individuals interested in learning practical solutions to some common analytic challenges. The overarching objective of this course is to provide participants with an introduction to the structure and content of EHR data as well as a set of practical tools to investigate and analyze this rich data resource.