About LynxCare

Provides a federated data platform for healthcare organizations, enabling the integration and harmonization of structured and unstructured clinical data using NLP and OMOP CDM. This system supports real-world evidence generation and multicenter studies in oncology and cardiology, improving data quality and accelerating research while ensuring compliance with privacy regulations.

```xml <problem> Healthcare organizations face challenges in integrating and harmonizing diverse clinical data types, including structured and unstructured data, which are often stored in disparate systems. This lack of interoperability hinders real-world evidence (RWE) generation and multicenter studies, limiting the ability to derive meaningful insights from comprehensive patient data. </problem> <solution> The company offers a federated data platform designed for healthcare and life science organizations that integrates and harmonizes structured and unstructured clinical data. The platform leverages NLP to enrich data and maps it to the OMOP common data model (CDM), facilitating real-world evidence generation and multicenter studies. By ensuring data quality and compliance with privacy regulations, the platform accelerates research and enables deeper insights into oncology, cardiology, and other therapeutic areas. The platform can be deployed on any cloud or on-premise via Kubernetes clusters. </solution> <features> - Federated architecture enabling data integration across multiple healthcare organizations while maintaining data privacy. - NLP pipeline trained on over 50 million patient records for extracting and structuring information from unstructured clinical text. - Mapping to the OMOP common data model (CDM) to standardize data representation and facilitate interoperability. - Quality control steps throughout the data value chain, from source to insight, ensuring data accuracy and reliability. - Support for multicenter real-world evidence (RWE) studies in oncology (immuno-oncology, breast cancer, lung cancer, multiple myeloma, CLL), cardiology (heart failure, ATTR-CM), and mental health. - Compliance with international privacy regulations in both the EU and the USA. </features> <target_audience> The primary customers are healthcare organizations and life science companies seeking to leverage real-world data for research, clinical decision support, and improving patient outcomes. </target_audience> ```

What does LynxCare do?

Where is LynxCare located?

LynxCare is based in Leuven, Belgium.

When was LynxCare founded?

LynxCare was founded in 2015.

How much funding has LynxCare raised?

LynxCare has raised 26670000.

Location

Leuven, Belgium

Founded

2015

Funding

26670000

Employees

40 employees

Major Investors

MTIP AG

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LynxCare

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Executive Summary

lynx.care 5K+

Crunchbase

Founded 2015 – Leuven, Belgium

Funding

Estimated Funding

$20M+

Major Investors

MTIP AG

Team (40+)

No team information available.

Company Description

Problem

Healthcare organizations face challenges in integrating and harmonizing diverse clinical data types, including structured and unstructured data, which are often stored in disparate systems. This lack of interoperability hinders real-world evidence (RWE) generation and multicenter studies, limiting the ability to derive meaningful insights from comprehensive patient data.

Solution

The company offers a federated data platform designed for healthcare and life science organizations that integrates and harmonizes structured and unstructured clinical data. The platform leverages NLP to enrich data and maps it to the OMOP common data model (CDM), facilitating real-world evidence generation and multicenter studies. By ensuring data quality and compliance with privacy regulations, the platform accelerates research and enables deeper insights into oncology, cardiology, and other therapeutic areas. The platform can be deployed on any cloud or on-premise via Kubernetes clusters.

Features

Federated architecture enabling data integration across multiple healthcare organizations while maintaining data privacy.

NLP pipeline trained on over 50 million patient records for extracting and structuring information from unstructured clinical text.

Mapping to the OMOP common data model (CDM) to standardize data representation and facilitate interoperability.

Quality control steps throughout the data value chain, from source to insight, ensuring data accuracy and reliability.

Support for multicenter real-world evidence (RWE) studies in oncology (immuno-oncology, breast cancer, lung cancer, multiple myeloma, CLL), cardiology (heart failure, ATTR-CM), and mental health.

Compliance with international privacy regulations in both the EU and the USA.

Target Audience

The primary customers are healthcare organizations and life science companies seeking to leverage real-world data for research, clinical decision support, and improving patient outcomes.