Central theme across ROADMAP (Alzheimer's RWE), OPTIMA (real-world data for cancer treatment), and their broader portfolio of patient outcomes research.
FUNDACIO INSTITUT UNIVERSITARI PERA LA RECERCA A L'ATENCIO PRIMARIA DE SALUT JORDI GOL I GURINA
Barcelona-based primary care research institute providing real-world patient evidence, disease screening, and health economics expertise to European health consortia.
Their core work
IDIAP Jordi Gol is Barcelona's primary care research institute, specializing in population-level health studies conducted through the lens of real-world clinical practice. They contribute epidemiological data, patient cohorts, and primary care expertise to large European research consortia tackling chronic diseases — from Alzheimer's to obesity to liver disease. Their strength lies in bridging the gap between clinical research and everyday patient care, providing real-world evidence from primary healthcare settings that large clinical trials often miss.
What they specialise in
LiverScreen focuses on population-based liver fibrosis screening and risk factor stratification; SOPHIA addresses obesity phenotype stratification for treatment optimization.
ROADMAP explicitly includes health economics of Alzheimer's care; LiverScreen involves cost-effectiveness analysis of screening methodologies.
OPTIMA applies artificial intelligence and big data analysis to optimize cancer treatment; SOPHIA uses federated databases for obesity research.
BEAMER develops a comprehensive behavioural theoretical framework for improving patient adherence to treatment across healthcare settings.
How they've shifted over time
Their early H2020 work (2016–2018) centred on Alzheimer's disease, health economics, and data sourcing for real-world patient outcomes — a classic observational research profile. From 2020 onward, their focus expanded into population-based screening (liver disease, obesity), treatment algorithms, federated databases, and artificial intelligence applications. The shift signals a move from descriptive real-world evidence toward predictive and prescriptive analytics in chronic disease management.
Moving toward AI-assisted clinical decision support and federated data approaches for multi-disease population health — expect future work at the intersection of primary care data and machine learning.
How they like to work
IDIAP Jordi Gol consistently joins as a participant or third-party contributor rather than leading consortia — they have zero coordinator roles across all five projects. They operate in large consortia (140 unique partners across 21 countries), which is typical for an organization that provides specialized primary care data and clinical site access to major European health studies. This makes them a reliable, low-friction partner who contributes domain expertise without requiring project management overhead.
Connected to 140 distinct partners across 21 countries, indicating deep integration into the European health research ecosystem. Their network is broad and pan-European rather than concentrated in any single regional cluster.
What sets them apart
What sets IDIAP Jordi Gol apart is their access to primary care patient data at population scale in Catalonia — one of Europe's most digitized primary healthcare systems. While many research institutes focus on hospital or laboratory data, they bring the primary care perspective: how diseases manifest, progress, and are managed in everyday clinical settings. For consortium builders, they offer something difficult to replicate — a direct pipeline from real-world primary care records to research-grade evidence.
Highlights from their portfolio
- ROADMAPTheir largest funded project (EUR 212,250), tackling real-world outcomes across the Alzheimer's disease spectrum — a disease area attracting massive pharma and public health investment.
- OPTIMAApplies AI and big data to optimize solid tumour treatment across Europe, representing their most forward-looking work at the intersection of oncology and artificial intelligence.
- SOPHIAStratifies obesity phenotypes using federated databases and patient voice data — signals their entry into precision medicine for metabolic disease.