Data Quality

Quality You Can Measure, Trust You Can Prove.
LynxCare's Sentinel is a robust, OMOP CDM-based framework designed to continuously measure, benchmark, and improve the quality of EHR-derived datasets — transforming hospital data into high-value research assets and delivering trusted, regulatory-grade evidence for life sciences, aligned with the European Health Data Space.
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Our Team & Mission

Founded in 2015, LynxCare has grown from an ambitious Belgian start-up into a fast-growing European scale-up with over 30 dedicated professionals. We accelerate clinical research and real-world evidence generation at leading health systems across Europe.

What began as a shared vision to unlock the full potential of healthcare data has evolved into a clear mission: to foster research and innovation by enabling hospitals and life sciences organizations to leverage high-quality clinical insights, preparing them for the future of data-driven healthcare. Through our work, we actively contribute to building the European Health Data Space (EHDS) and other European initiatives that promote secure, interoperable, and ethical use of health data (EHDEN, OHDSI).

Our multidisciplinary team — combining expertise in data science, healthcare, informatics, and artificial intelligence — works side by side with hospitals, researchers, and life sciences partners to transform how clinical data is accessed and leveraged to improve patient care and advance medical research.  

Teambuilding October
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Five Levels of
Data Quality Assurance

Built on the OMOP Common Data Model, Sentinel runs five sequential layers of automated quality checks across every dataset in LynxCare's network. Each level builds on the previous, creating a rigorous, end-to-end quality guarantee. Sentinel enables consistent, benchmarkable quality assessment across hospitals, countries, and studies. It is designed to meet the regulatory and interoperability standards of today's European health data ecosystem.

Completeness

Ensuring all expected data fields and patient records are present, with no unacceptable gaps in core clinical variables.

Accuracy

Verifying that data values are internally consistent, plausible, and free from technical extraction or mapping errors.

Validation

Assessing whether extracted variables align with clinical logic — flagging implausible combinations or out-of-range values for expert review.

Medical Review

Human-in-the-loop validation by clinical experts who assess edge cases, ambiguous records, and NLP-extracted variables with real-world scrutiny.

Benchmark

Comparing dataset characteristics against known epidemiological benchmarks to detect systematic biases or site-specific anomalies.

Data Quality Checks in 98% Less Time

With Sentinel

1 HOUR

Per cohort

Systematic and automated

Manually

6 DAYS

Per cohort

Manual Review

Sentinel’s automated pipeline removes manual validation bottlenecks, running systematic checks across millions of data points per hospital. It detects missing values, implausible measurements, and mis-extracted clinical data, letting human experts focus only on flagged issues. The result: full, reproducible validation in just an hour per cohort — enabling data quality checks at scale.

In Action

A walkthrough of how LynxCare's quality framework continuously monitors and improves EHR-derived data across our hospital network.
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Sentinel at Scientific Conferences

Our research on automated data quality has been presented and validated at leading international conferences in health informatics, real-world evidence, and clinical data science.