Healthcare data

We discuss the potential of the abundant RWD for lung cancer presenting an invaluable resource when translated into RWE. While clinical trials are essential, they may not fully represent the real-world patient population. RWE complements clinical trial evidence by providing insights on treatment outcomes in broader, unselected patient groups, helping inform reimbursement decisions and ensuring access to innovative therapies.

In its latest report released in June 2023, the EMA highlights key opportunities and challenges to move towards an optimized usage of RWE across the spectrum of regulatory use cases. To push this transformative vision forward, the report presents a list of lessons learned and recommendations. Continue reading below.

A study published in Clinical Pharmacology &Therapeutics [1] looking at positive marketing authorization applications of new medicines by the European Medicines Agency (EMA) in 2018-2019, found that almost all evaluated medicines included RWE signatures in the discovery (98.2%) and lifecycle management (100%) phases. About half of the medicines had RWE signatures for the full development phase (48.6%) and for supporting regulatory decisions at the registration phase (46.8%), while over a third included RWE signatures for the early development (35.1%). Therapeutic areas such as oncology, hematology, and anti-infectives showed the highest use of RWE signatures in their full development phase.

Selecting patients for clinical studies can be a slow and tedious process. But it does not have to be. Emerging technologies are paving the way for faster and more efficient patient selection, and our Clinical REsearch Patient Selection (CREPS) project in collaboration with a renowned Belgian hospital is leading the change. In this blog post, we will dive into the world of real-world evidence (RWE) and explore how the use of big data and Natural Language Processing (NLP) can revolutionize the way we screen and select patients for clinical studies while always remaining 100% GDPR compliant. Let us explore how LynxCare’s technology can speed up and simplify your trials’ patient selection process.

A metanalysis published in the Heart journal identified 37 studies developing and applying NLP in various areas of cardiology. This systematic review found that the majority of NLP work in cardiology has been concentrated in a small number of clinical domains and NLP methods, with the most common applications being used for disease identification and classification purposes. Patient sample sizes ranged from 60 to 621,000. Data sources ranged from single centers to national healthcare system databases and registries, most of them taking place in the US.

LynxCare can help French hospitals to make the most of all their clinical data by building OMOP data warehouses (i.e. ‘EDS’) to improve patient care and research.

As we begin 2023’s Heart Failure Awareness Week, it is important to reflect on how we can better detect and treat this condition. This year's theme, "Detect the Undetected" highlights the need for early detection and intervention to improve patient outcomes.

Lung cancer claims millions of lives worldwide every year. And there’s no single cure. But what if we could gain deeper insights into the genetic makeup of a population of 30,000+ lung cancer patients? Thanks to our Natural Language Processing (NLP) algorithm and a team of dedicated researchers, we now have a clearer picture of the non-small cell lung cancer (NSCLC) patient population and the specific genetic mutations that drive tumor growth. In this blog, we’ll share our AI-driven findings from the Dutch nationwide registry of histo- and cyto-pathology (PALGA) registry. Read on to discover how AI data processing with 95.9% accuracy can propel the progress of cancer treatment. ‍

Muco is an association by and for people with cystic fibrosis and their families. Together with families, muco centers and healthcare providers, the association is committed to ensuring a better, longer and happier life for all muco patients.