Knowledge Center

Accessing and standardizing raw clinical data across multiple hospitals presents a challenge in Oncology. However, it is crucial to use real-world data sources such as electronic health records (EHR) to leverage untapped information. We are building a federated data network to facilitate GDPR-compliant data exchange of large datasets, with hospitals as owners. This network, governed by a common data model (CDM), is aimed at fostering multicenter, observational, real-world evidence (RWE) studies in Oncology, with breast cancer, lung cancer, and immunotherapy as therapeutic areas of focus.

NLP algorithms allow rapid data extraction from pathology reports, thereby offering a time-efficient and cost-effective alternative to manual data processing. In turn, this approach enables rapid insight in current biomarker testing rates and prevalence of (actionable) mutations.

Information held in Electronic Health Records (EHRs) hold a significant opportunity to provide physicians and researchers with better and more insights to improve disease management and treatment.

The study aimed to analyze individuals with ATTR-CM in a real-world heart failure patient population using a federated OMOP-CDM database generated from data of electronic health records.

An example of the power of real-world data, technology and collaboration to transform patient care. The study aimed to shed light on patient eligibility for SGLT2 inhibitors as a therapy for heart failure patients, providing valuable information for clinicians and researchers.

The diagnostic delay for rare disease patients can go up to more than 5 years. But were you aware that technology could play a significant role in speeding up the diagnosis time of rare diseases?

A large multi-country Real-World Evidence study in heart failure shows that 1–2% of the contemporary adult population has heart failure. Through analyzing data from both national registries and Electronic Health Records, further insights into the burden of heart failure on patients and on our healthcare systems can be gained.

The underdiagnosis as well as frequently delayed diagnosis of cardiac aTTR amyloidosis (aTTR-CM), a progressive and fatal cardiomyopathy, asks for improved screening.

A clinically-validated NLP algorithm offers a valid and accurate tool to detect red flag symptoms in medical records across multiple disciplines, supporting better screening for patients with rare diseases.