Our Approach to Data Quality
A framework-driven path to research-ready real-world data.
Real-world evidence is only as credible as the data beneath it.
A retrospective cohort, an external control arm, a safety signal assessment: each one inherits the strengths and weaknesses of the records it was built from. That’s why data quality isn’t a back-office hygiene function. It determines whether data can become evidence.
At TriNetX, that responsibility operates at network scale: more than 250 healthcare organizations, 18,000+ provider sites, and over 20 countries. This eBook is our account of how we practice data quality, built on a shared research-community standard rather than a proprietary one, and open to scrutiny.
Inside, you’ll find:
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Why data quality determines evidence quality, and how we move from characterization to quality to fitness for purpose
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How we adopted the Harmonized Data Quality Assessment Framework (Conformance, Completeness, Plausibility) as the backbone of our program
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How data is sourced, preserved, harmonized, and monitored, with every transformation documented and a path back to the source record
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How continuous monitoring, human triage, and a monthly executive forum turn alerts into action
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Why “percent complete” isn’t one question, and how we break Completeness into a multi-resolution view
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How we extend the same accountability to facts extracted from clinical notes, and what the go-live review and peer benchmarking look like in practice
From source to study — quality you can verify.
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