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

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.

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Publication Details

Journal
Frontiers in Public Health
Published
2025-08-08
DOI
https://doi.org/10.3389/fpubh.2025.1630351
Citations
2
Primary Topic
Machine Learning in Healthcare
Type
article
Field-Weighted Citation Impact
4.96

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article

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

Carlos Loucera, V. Oliva, Patricia Fernández del Valle, Joaquı́n Dopazo et al.
2 citations
Frontiers in Public Health
Machine Learning in Healthcare
4.96
article

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

Carlos Loucera, V. Oliva, Patricia Fernández del Valle, Joaquı́n Dopazo, Román Villegas, Dolores Muñoyerro-Muñiz, Ana María Sánchez Sánchez, Laura Alejos, Alberto Esteban-Medina, M. Belen Susin, Isidoro Gutierrez-Alvarez, Marta Reboredo
article en
2 citations

Abstract

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.

Frontiers in Public HealthVol. 13
Andalusian Health Service (ES), Fundación Progreso y Salud (ES), Servicio Aragonés de Salud (ES), Instituto de Biomedicina de Sevilla (ES)
European Commission, Ministerio de Ciencia e Innovación, Consejería de Salud y Familias, Junta de Andalucía, Instituto de Salud Carlos III
Industry, innovation and infrastructure
Openalex Percentile: Top 5%
Machine Learning in Healthcare
4.96
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