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. Combined with human-in-the-loop clinical oversight, it transforms hospital data into high-value research assets — 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

LynxCare runs five sequential layers of quality checks across every dataset in its network, covering both structured sources and NLP outputs. Each layer builds on the previous, creating a rigorous, end-to-end quality guarantee — configurable at the site level, scalable across the network, and validated by human experts where it matters most. Built to meet the regulatory and interoperability standards of today's and tomorrow's European health data ecosystem.

Conformance

Automated checks verifying that data aligns with the expected structure, data types, and OMOP CDM rules — ensuring it has been captured, formatted, and stored correctly.

Completeness

Verifying that information expected for a patient, encounter, or variable is actually present and sufficiently populated in the dataset.

Plausibility

Assessing whether recorded values are logical and clinically credible — either against expected ranges from the literature, or through internal consistency across variables.

Medical Review

Validation by clinical experts who assess edge cases, ambiguous records, and NLP-extracted variables, flagged by Sentinel, with real-world scrutiny.

Benchmark

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

Scalable Data Validation

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 thousands 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.

Sentinel 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.