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

Year: 2025

Type: article

Abstract: Background: Data protection is often regarded as a major hurdle in clinical research, especially when handling sensitive patient information. However, by managing sensitive data within a secure environment, it becomes possible to mitigate these challenges and promote evidence generation through clinical data studies. This manuscript introduces a collaborative environment which facilitates the secure and ethical secondary use of clinical data for medical research. The environment combines a comprehensive health population database, integrating real-world data (RWD) from over 15 million patients, with a legal framework for patient privacy and data security and a secure processing environment (SPE) for ethically compliant clinical data research. Methods: The SPE of this collaborative environment is a computational infrastructure (IRWD), located within the healthcare corporative network, that enables large-scale studies essential for real-world evidence (RWE) generation. Within iRWD, diverse studies can be conducted, including treatment efficacy assessments, survival analyses, and the development of predictive models. These studies leverage RWD to perform robust analyses while maintaining compliance with stringent regulatory standards, such as the European Health Data Space (EHDS) and the General Data Protection Regulation (GDPR), which govern data security and patient privacy. Results: One of the key outcomes facilitated by this infrastructure is the systematic development of early endpoint predictors. These predictive algorithms can identify high-risk patients before symptoms emerge, enabling preventive interventions. The approach promotes a shift in healthcare from a reactive model to a preventive one, allowing for early, efficient, and cost-effective treatments that improve patient outcomes. Additionally, the model supports public-private partnerships, generating revenues that sustain this collaborative environment while reinforcing its capacity for continuous healthcare innovation. Conclusions: The integration of clinical big data within a legally compliant and secure framework provided by the SPE offers a sustainable and proactive model for healthcare improvement. The infrastructure supports the development of cost-effective predictive models that are particularly valuable for an aging population, ultimately transforming healthcare delivery into a proactive, data-driven system. By positioning the health system as a central player in knowledge generation and wealth creation, this collaborative initiative sets a new benchmark for data-driven healthcare models across Europe and beyond, highlighting its potential for global adaptation and impact.

Source: Frontiers in Public Health

DOI: 10.3389/fpubh.2025.1630351