GMV has been certified by the European Health Data and Evidence Network (EHDEN) after proof of its theoretical and practical capacity for standardizing health data to the OMOP Common Data Model (CDM).

EHDEN, an EU initiative, was born with the aim of developing a federated network of real-world health research, standardized to a common data model that more smartly manages and shares research methodologies to the benefit of all European citizens. One of EHDEN’s KPIs is geared towards an assessment of the growth of this market. The EHDEN community is made up by industry, universities, regulators, governments, European NGOs, etc.

There is a direct relationship between the amount of harmonized data shared under the same standard and the development of drugs and therapies to deal with diseases that have hitherto had no efficient treatment whatsoever. In the words of Inmaculada Pérez Garro, digital-health manager of GMV’s Secure e-Solutions sector «EHDEN’s certification of GMV vouches for our ongoing work in flagship projects like HARMONY, under which we have set up the big data platform for achieving the biggest possible data trawl of European patients with blood diseases. Health-data processing standards are essential for making sure that today’s huge volume of data can be properly mined and analyzed to the benefit of researchers and the patients themselves». This certification clears GMV for participation in European projects calling for this certification as an eligibility sine qua non.

EHDEN, driven by the Innovative Medicines Initiative (IMI), is bankrolled by the EU’s Horizon 2020 program.

On OMOP

The advent of the Electronic Medical Record (EMR) has opened up a universe of opportunities for reusing data and extracting clinical evidence from it. In this endeavor, however, it is essential for this data to be used and harmonized under a “common language”. If everyone is speaking the same language, then the much-vaunted «interoperability» will actually be achieved.

Enter OMOP. The OMOP Common Data Model allows for the systematic analysis of disparate observational databases. The concept lying behind this approach is to transform data contained within those databases into a common format (data model) as well as a common representation (terminologies, vocabularies, coding schemes), and then perform systematic analyses using a library of standard analytic routines that have been written based on the common format. This then allows companies like
GMV to extract priceless information that helps in the development of new drugs, therapies and treatments.