Project Summary

One of PCORI’s goals is to improve the methods that researchers use for patient-centered outcomes research. PCORI funds methods projects like this one to better understand and advance the use of research methods that improve the strength and quality of comparative effectiveness research.

What is the project about?

To trust that their findings are accurate, researchers must know that their data are of high quality. To check the quality of their data, researchers look at factors such as:

  • Data structure
  • Whether data are missing
  • How well data work in a statistical model

But most methods for testing data quality do not consider clinical context, including how well study data reflect real patient traits and clinical conditions. In this project, the research team is developing an approach for judging how well data fit the needs of a research study.

How can this project help improve research methods?

Results may help researchers assess data quality to see how well their data reflect the clinical ideas they mean to study.

What is the research team doing?

The research team is working with patients, clinicians, and experts in health data and technology. Together, they are creating guidance to help researchers test and report on how well their data reflect clinical context. Next, the team is creating free tools, such as checklists and computer code, that other researchers can use to measure and report on data quality. Finally, the team is testing the approach and tools in three PCORnet® Clinical Research Networks. PCORnet is made up of networks of health systems and other partners who contribute data. These partners gather data from electronic health records, or EHRs, and transform the data into a common format.

Research methods at a glance

Design Element Description
  • Develop a semantic data quality assessment approach that accounts for the clinical meaning required for a given analysis.
  • Create tools to apply the approach when designing, testing, and reporting on data quality in studies.
  • With stakeholder input, develop semantic data quality assessment guidance based on a conceptual framework that integrates clinical context, existing data quality principles, statistical tools, and scope of application.
  • Develop reusable and adaptable tools to apply the semantic data quality assessment approach and produce data quality measurements that are comparable across studies and research networks.
  • Perform feasibility testing with defined use cases applied across multiple research networks.

Project Information

Charles Bailey, MD, PhD
Children's Hospital of Philadelphia
Semantic Data Quality Standards for Multi-Center Clinical Research Studies and Networks

Key Dates

July 2021
June 2026


State State The state where the project originates, or where the primary institution or organization is located. View Glossary
Last updated: October 20, 2023