IASIS · Project

Big Data Platform That Combines Medical Records and Genomics for Personalized Treatment

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Imagine a hospital where your doctor has to check your blood tests in one system, your genetic results in another, your X-rays in a third, and published medical research in a fourth — none of them talking to each other. IASIS built a platform that pulls all of these together into one place, then uses machine learning to spot patterns that humans would miss. It focused specifically on dementia and lung cancer, connecting genomic data with electronic health records and medical literature to help doctors figure out which treatment works best for which patient.

By the numbers

consortium partners

countries involved (DE, EL, ES, UK, US)

total deliverables produced

disease categories addressed (dementia and lung cancer)

EUR 4,337,475

EU research investment

The business problem

What needed solving

Hospitals and health systems sit on mountains of patient data — genomic profiles, electronic health records, clinical notes, medical images — but these live in separate systems that don't communicate. Doctors make treatment decisions without seeing the full picture, leading to one-size-fits-all care instead of personalized treatment. This is especially critical for complex conditions like dementia and lung cancer where combining genetic and clinical data could dramatically improve outcomes.

The solution

What was built

IASIS built a modular big data platform with 34 deliverables including: data harvesting services for genomic, clinical, and open data; semantic indexing modules for EHR and genomic data; clinical notes and image analysis tools; predictive modelling for treatment patterns; and a unified platform prototype. All core modules went through two development iterations (v1.0 and v2.0).

Audience

Who needs this

Large hospital networks needing to unify patient data across departmentsPharma companies stratifying patients for precision medicine clinical trialsHealth IT vendors adding clinical decision support to EHR platformsNational health services building population-level disease analyticsBiotech startups developing genomics-based diagnostic products

Business applications

Who can put this to work

Hospital Networks & Health Systems

enterprise

Target: Large hospital groups or national health services managing complex patient populations

If you are a hospital network struggling to connect patient records across departments — genomics, radiology, clinical notes — this project developed a platform that semantically indexes and integrates all these data sources into one knowledge base. It includes predictive modelling that identifies treatment patterns across 2 disease categories (dementia and lung cancer), helping clinicians make faster, evidence-based decisions.

Pharmaceutical & Life Sciences

enterprise

Target: Pharma companies running clinical trials or developing precision medicine therapies

If you are a pharma company trying to identify which patient subgroups respond best to your drug candidates — this project built genomic data analysis and predictive modelling modules that combine genomic profiles with electronic health records. The platform was tested across 12 consortium partners in 5 countries, offering cross-border data integration that could accelerate patient stratification for clinical trials.

Health IT & Medical Software

mid-size

Target: Health IT vendors building clinical decision support or data integration products

If you are a health IT company looking to add clinical intelligence to your existing EHR platform — IASIS developed reusable components including clinical notes and image analysis, semantic indexing for EHR data, and open data harvesting services. With 34 deliverables including final v2.0 modules, these components could be integrated into commercial products to offer predictive modelling on top of existing hospital data infrastructure.

Frequently asked

Quick answers

What would it cost to implement this kind of data integration platform?

The IASIS project received EUR 4,337,475 in EU funding across 12 partners over 3 years. A commercial implementation would depend on scope, but the platform components (data harvesting, semantic indexing, predictive modelling) were built as modular services. Licensing individual modules would be significantly cheaper than replicating the full research effort.

Can this scale to handle real hospital data volumes?

The platform was designed to handle heterogeneous big data from multiple sources — genomics, electronic health records, clinical images, and published literature. It includes dedicated harvesting services for each data type with both v1.0 and v2.0 iterations. Based on available project data, the system was demonstrated with 2 disease categories (dementia and lung cancer), but scaling to additional conditions would require further validation.

What about IP and licensing for the technology?

The project was coordinated by the National Center for Scientific Research Demokritos in Greece, with 12 partners across 5 countries. IP is likely distributed among consortium members under the Horizon 2020 grant agreement. Businesses interested in licensing specific modules should contact the coordinator or relevant technical partners directly.

Does this comply with medical data regulations like GDPR?

The project ran from 2017 to 2020, coinciding with GDPR enforcement. The objective emphasizes creating auditable and reliable information and building confidence in data sharing. Based on available project data, specific GDPR compliance measures are not detailed in the deliverable descriptions, so this should be verified with the consortium.

How long would it take to integrate this with our existing hospital systems?

The platform includes dedicated data harvesting and semantic indexing modules for EHR data, clinical notes, genomic data, and open data sources. Both v1.0 and v2.0 versions were delivered, suggesting iterative refinement. Integration timelines would depend on your existing infrastructure, but the modular architecture with separate harvesting, indexing, and analysis layers is designed for interoperability.

Was this tested with real patient data?

The project focused on 2 specific disease categories — dementia and lung cancer — and developed dedicated modules for EHR semantic indexing, genomic data analysis, and clinical notes and image analysis. The deliverables include an Initial Platform Prototype and final v2.0 versions of all major components, indicating testing with real-world data sources.

Consortium

Who built it

The IASIS consortium of 12 partners across 5 countries (Germany, Greece, Spain, UK, US) is heavily research-oriented, with 5 universities and 5 research organizations making up 83% of the team. Only 1 industrial partner and 1 SME were involved, giving just an 8% industry ratio — this means the technology was built primarily by academics with limited commercial input. The coordinator, Demokritos (Greece's national research center), is a strong technical lead but not a commercial entity. For a business considering adoption, this means the science is likely robust but commercialization, packaging, and enterprise-grade support would need additional work or partners.

NATIONAL CENTER FOR SCIENTIFIC RESEARCH "DEMOKRITOS"Coordinator · EL
ATHENS TECHNOLOGY CENTER ANONYMI VIOMICHANIKI EMPORIKI KAI TECHNIKI ETAIREIA EFARMOGON YPSILIS TECHNOLOGIASparticipant · EL
GRUPO ESPANOL DE INVESTIGACION EN CANCER DE PULMONparticipant · ES
THE UNIVERSITY OF MARYLAND FOUNDATIION INCparticipant · US
SERVICIO MADRILENO DE SALUDparticipant · ES
RHEINISCHE FRIEDRICH-WILHELMS-UNIVERSITAT BONNparticipant · DE
ST GEORGE'S HOSPITAL MEDICAL SCHOOLparticipant · UK
FUNDACION PARA LA INVESTIGACION BIOMEDICA DEL HOSPITAL UNIVERSITARIO PUERTA DE HIERRO-MAJADAHONDAthirdparty · ES
FUNDACIO CENTRE DE REGULACIO GENOMICAparticipant · ES
UNIVERSIDAD POLITECNICA DE MADRIDparticipant · ES
GOTTFRIED WILHELM LEIBNIZ UNIVERSITAET HANNOVERparticipant · DE

How to reach the team

National Center for Scientific Research Demokritos, Greece — contact through SciTransfer for a warm introduction to the research team

Order a report on this consortium See NATIONAL CENTER FOR SCIENTIFIC RESEARCH "DEMOKRITOS" profile →

Next steps

Talk to the team behind this work.

Want to explore how IASIS data integration technology could work for your health data challenge? SciTransfer can arrange a direct conversation with the research team and help assess fit for your use case.

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