A Framework for Visualizing Study Designs and Data Observability in Electronic Health Record Data

Abstract

Background: There is growing interest in using evidence generated from clinical practice data to support regulatory, coverage and other healthcare decision-making. A graphical framework for depicting longitudinal study designs to mitigate this barrier was introduced and has found wide acceptance. We sought to enhance the framework to contain information that helps readers assess the appropriateness of the source data in which the study design was applied. Methods: For the enhanced graphical framework, we added a simple visualization of data type and observability to capture differences between electronic health record (EHR) and other registry data that may have limited data continuity and insurance claims data that have enrollment files. Results: We illustrate the revised graphical framework with 2 example studies conducted using different data sources, including administrative claims only, EHR only, linked claims and EHR, as well as specialty community based EHRs with and without external linkages. Conclusion: The enhanced visualization framework is important because evaluation of study validity needs to consider the triad of study question, design, and data together. Any given data source or study design may be appropriate for some questions but not others.