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.

Keywords:

• study design
• real world evidence
• real world data
• visualization
• methods
• bias

Acknowledgments

We would like to recognize Judy Maro, Michael Nguyen, Joshua K Lin, and Jeremy Rassen for early discussions on this revision to the graphical framework for study design.

Disclosure

Authors were supported by funding from the NIH: NHLBI RO1HL141505 and NIA R01AG053302. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. Dr. Wang has no conflicts of interest to report. Dr. Schneeweiss is principal investigator of the FDA Sentinel Innovation Center funded by the FDA, co-principal investigator of an investigator-initiated grant to the Brigham and Women’s Hospital from UCB and Boehringer Ingelheim unrelated to the topic of this study. He is a consultant to Aetion Inc., a software manufacturer of which he owns equity. His interests were declared, reviewed, and approved by the Brigham and Women’s Hospital and Partners HealthCare System in accordance with their institutional compliance policies.