To provide effective healthcare services and encourage the exchange of data in a responsible and efficient manner, achieving interoperability in the meaning of the data is absolutely necessary. This is especially relevant now that the European Health Data Space Regulation is being discussed.
Our main focus is health data, and we are very advanced with medication data.
Dynaccurate’s CEO and co-founder Dermot Doyle points out certain challenges the health sector faces and explains how the company innovates in Luxembourg. He also highlights some of the benefits the Grand Duchy’s ecosystem offers, particularly for research partnerships.
How do you innovate in the healthtech space?
Dynaccurate harmonises health data at source. Currently, across pretty much all of the healthcare sector, our health data is recorded in slightly different ways. Generally the information is accurate, but using different words or terms. This creates several problems for patients, doctors and researchers.
For patients, their data from one system cannot be read or understood by another system.
For doctors, it is not possible to get a rapid summary of how the patient has been treated historically or at another facility.
These problems are all solved if the data is structured in a machine-readable way.
For researchers, it is not possible to review a mass of data across different systems or sites, because there is a lack of harmonisation.
The technical term that addresses these above issues is ‘semantic interoperability’, which just means preserving the meaning of words across different systems. These problems are all solved if the data is structured in a machine-readable way. This is what our technology does.
What type of data do you deal with?
Our main focus is health data, and we are very advanced with medication data. However, the semantic interoperability problem is pretty much pervasive in any area where you have highly specific information to manage.
We need to reform and improve our management of public health data.
We have described it for health, but it also exists across all of the life sciences sectors, law, defence, engineering etc. Our first client was actually NATO, which probably emphasises the importance of the matter. We have barely scratched the surface in dealing with semantic interoperability in machine systems.
How do you benefit from the data and innovation ecosystem in Luxembourg?
The innovation ecosystem is second to none in Luxembourg. For research partnerships in the academic sector, we have plenty of opportunities, thanks to our relationship with the Luxembourg Institute of Science and Technology (LIST), as well as other institutions. One benefit of Luxembourg is that it is pretty easy to network across institutions, and you can find the ‘go-to’ person quite fast in any research field. The interactions we have in the ecosystem are excellent, and we’re increasingly reaching out to local domain experts for bids, tenders and general collaboration. There are several opportunities to work with researchers, who help us stay up to date on new trends in our specific field of AI.
One benefit of Luxembourg is that it is pretty easy to network across institutions.
However, as is the case in other countries, there are some areas for development in the data ecosystem. ‘Real world data’ is one example. This refers to data held by public institutions such as hospitals, health agencies and ministries and directorates. Also, a lot of the medications data across the country will have to be linked and consolidated in the future. Our technology can do this pretty easily, incorporating multiple characteristics such as clinical codes, barcodes, reimbursement codes as well as legacy codes in other systems. We’ve already run these tests successfully using available medication lists.
Fixing real world issues
When these specific codes and identifiers are linked, one achieves absolute precision – complete ‘semantic interoperability’ so to speak – of regulated drugs in Luxembourg. If you have that, the related benefits for artificial intelligence are absolutely massive. AI absolutely loves structured data, and that’s what we deliver.
The semantic interoperability problem is pretty much pervasive in any area.
For example, you would be able to track the consumption of the drugs throughout the entire year, if you specify the quantities of medications held in the public health system on a regular basis. This would enable you to prepare ahead for global drug shortages, create stockpiles, and model for potential shortages.
The European Medicines Agency monitors medicine shortages in Europe. Amoxicillin is one common antibiotic affected by a global shortage and shows how much we need to reform and improve our management of public health data. If we do so, we would all benefit.
Photo credit: provided by Dynaccurate