Our Technology

Precision. Speed. Scale.
LynxCare’s proprietary clinical NLP uses healthcare-trained pipelines to process unstructured clinical text and extract, contextualize, and normalize medical entities (e.g. diagnoses, symptoms, procedures, medications, outcomes), complementing structured data from EHRs. Outputs are mapped to standard terminologies and integrated into the OMOP Common Data Model. The pipeline includes quality controls and validation steps, enabling reproducible, high-quality real-world data suitable for large-scale analytics and regulatory-grade research.
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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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Defined by Accuracy, Interoperability, Trust

Medical-grade Clinical NLP
Extracts clinically meaningful concepts from narrative EHR text. Transforms them into structured, analysis-ready data.

Proprietary small language model, trained on medical data.
Designed for precision, traceability, and validation. Not generative output.
Complements existing structured EHR data.

OMOP-CDM Interoperability
All outputs are mapped to OMOP Common Data Model. Once data is in OMOP, research scales, across hospitals, countries, and studies.

Cross-institution analytics.
Participation in international research networks (e.g. OHDSI/EHDEN).
Reuse across studies and partners.

Federated-by-design
All analytics and model execution happen where the data resides. Hospitals retain control. Researchers gain scale.

No raw data transfers.
GDPR-compliant by default.
Enables cross-country studies without central data pooling.

Quality & Trust – No black box
High-stakes research requires quality assurance, explainability and auditability. 5 QA checks from intake to insight.

Transparent NLP pipeline (no opaque generative outputs)
Continuous auto re-training & QA
Designed to support regulatory-grade RWE

Enriched Insights

Unlocking the full value of hospital data goes beyond structured fields. Much of the patient journey, from side effects to outcomes, lives in unstructured clinical notes. With clinical NLP, LynxCare enriches datasets to unlock deeper, more accurate insights.

Clinical NLP Features

Small, Purpose-Built

Focus on clinical relevance rather than just scale

Auto-retrained

Model performance continuously improves

Validation-Driven

Clinician-validation supporting clinical accuracy

No Black Box

100% traceability, audit-ready outputs aligned with EHDS

Flexible Deployment

In the cloud, on-premise, or in hybrid setups; and scalable

Research-Grade

Harmonised datasets with integraded quality controls

Evaluating the performance of our cNLP pipeline against two open-source alternatives

Biomedical LLMs Pretrained on Non-EHR Data Underperform in Multilingual Real-World Settings
Published in CORIA-TALN-RJCRI-RECITAL 2025

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From Raw Notes to OMOP-CDM

Our powerful yet efficient language models automatically standardize raw clinical notes into the OMOP CDM format, enriching existing structured data and making it instantly ready for research and validation.

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Data Quality at the Core

LynxCare’s Sentinel is a robust, OMOP Common Data Model-based system designed to continuously measure, benchmark, and improve EHR-derived datasets for regulatory compliance, collaborative studies, and translational research.

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FAQs

Find answers to your questions about our data solutions and services.

What does LynxCare's federated approach mean?

A federated approach means data remains at the source hospital under their control. Analysis queries are sent to multiple hospitals, each processes the query locally on their data, and only aggregated results (not patient-level data) are shared back, ensuring privacy and compliance.

How does LynxCare's technology work?

We deploy a secure local gateway at hospitals that extracts data from Electronic Health Records and other hospital information systems. Our AI and clinical NLP then processes both structured and unstructured data (including narrative clinical notes) and harmonizes it into OMOP Common Data Model format. Request a demo for more info.

How does LynxCare assure data quality?

5 quality insurance checks from intake to insight (completeness, accuracy, clinical validation, medical review, benchmark match). Read more about Sentinel in our Knowledge Center and on our Blog.