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Assessing and improving research readiness in PCORnet®.

Keith Marsolo^1,2, Laura Goettinger Qualls¹, Darcy Louzao¹

¹Duke Clinical Research Institute, Duke University School of Medicine, Durham, NC, USA.

Journal of Clinical and Translational Science

|December 25, 2025

Summary

This summary is machine-generated.

PCORnet® data curation improved research readiness by expanding data records and refining data checks between 2019 and 2024. These advancements enhance data quality and applicability for clinical research studies.

Keywords:

Data quality common data models data harmonization distributed research networks electronic health records

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Area of Science:

Health data science
Clinical data curation
Research data management

Background:

PCORnet® is a national data network crucial for real-world evidence generation.
Ensuring data quality and research readiness is vital for reliable clinical research.
Data curation processes are essential for standardizing and validating health data.

Purpose of the Study:

To assess and enhance the research readiness of data within PCORnet®.
To report on the PCORnet® data curation process from Cycle 7 (2019) to Cycle 16 (2024).
To detail improvements in data volume and the development of data quality checks.

Main Methods:

Extending the PCORnet® Common Data Model (CDM).
Developing and implementing a comprehensive suite of data quality checks.
Monitoring the performance of data checks across the network over time.

Main Results:

Significant growth in data volume, with diagnoses records increasing from ~3.7B to ~6.9B and lab results from ~7.7B to ~15.1B.
Implementation of numerous data checks, with examples showing stable (future dates), improved (RxNorm mapping), and variable (record persistence) performance.
Demonstrated progress in improving data quality and research readiness across the PCORnet® network.

Conclusions:

Transparent data curation fosters learning and informs study design decisions.
PCORnet® data quality improvements are transferable to other health data initiatives.
Ongoing efforts focus on modifiable metrics to further enhance data quality and research readiness.