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Ethical and secure evidence generation from regionwide clinical data through a collaborative environment for advancing predictive care

Bibliographic Data

ID22072597
AuthorsDolores Muñoyerro-Muñiz (Andalusian Health Service), Román Villegas (0000-0003-0845-0172, Andalusian Health Service), V Oliva (0000-0002-1444-3465, Fundación Progreso y Salud), Alberto Esteban-Medina (0000-0003-2265-0410, Fundación Progreso y Salud), Patricia Fernández del Valle (0000-0003-4718-1446, Fundación Progreso y Salud), Ana María Sánchez Sánchez (0000-0002-6591-954X, Fundación Progreso y Salud), Ana Sánchez (0009-0006-2555-6373), M Belen Susin (Fundación Progreso y Salud), Isidoro Gutierrez-Alvarez (Fundación Progreso y Salud), Marta Reboredo (Fundación Progreso y Salud), Laura Alejos (0009-0004-3564-9544, Andalusian Health Service), Carlos Loucera (0000-0001-9598-6965, Fundación Progreso y Salud), Joaquín Dopazo, Joaquı́n Dopazo (0000-0003-3318-120X, Fundación Progreso y Salud, corresponding author)
Year2025
Volume13
Pages1630351-1630351
Publication date2025-08-08
Open AccessYes
TypeARTICLE
VenueFrontiers in Public Health (JOURNAL)
Journal identifiersISSN: 2296-2565 • E-ISSN: 2296-2565
PublisherFrontiers Media SA (PUBLISHER • CH)
DOI10.3389/fpubh.2025.1630351
PMID40860551
OpenAlexW4413255752
LanguageEN
References cited41

Ensuring data protection is a major challenge in clinical research involving sensitive patient information. However, secure processing environments (SPEs) enable the ethical and compliant secondary use of real-world data (RWD) for evidence generation. This study presents a collaborative infrastructure integrating a comprehensive Health Population Database (BPS) with a legal and computational framework to facilitate secure, large-scale clinical studies. The Andalusian Platform for Medical Evidence Generation is an SPE embedded within the Andalusian healthcare network, leveraging RWD from over 15 million patients from the BPS. It supports diverse studies, including treatment efficacy, survival analyses, and predictive modeling, while ensuring alignment with the General Data Protection Regulation (GDPR) and proactively designed to meet forthcoming European Health Data Space (EHDS) requirements. Data are processed within a secure ecosystem, preventing unauthorized access and enabling legally compliant research collaborations. By combining clinical RWD with a robust ethical and legal framework, we present a scalable model for secure, data-driven region-level healthcare innovation. The platform supports cost-effective predictive models, particularly relevant for aging populations, and establishes a blueprint for regional and international adaptation. This approach strengthens the role of healthcare systems in both knowledge generation and sustainable economic growth, ensuring that patient data is leveraged for scientific and societal benefit

Blueprint · Data science · Database · Environmental health · Health care · Knowledge management · Population · Scalability · Artificial Intelligence in Healthcare and Education · Computer Science · Health, Environment, Cognitive Aging · Machine Learning in Healthcare · Medicine

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