At the socio-economic level, MHMD contributes to rebalancing EU competitiveness in the biomedical sector with advanced privacy-enhancing technologies, while preserving European higher privacy and data security standards for individuals. This is mainly achieved thanks to both (1) its blockchain-based architecture for simplifying data sharing for hospitals, through advanced privacy-preserving and security tools; (2) its GDPR-compliant data sharing model designed for scalability at the industry and societal levels.
MHMD also supports patients in getting control over their personal health data, while protecting both the data subject and her personal data, thanks in particular to its smart-contract powered dynamic consent mechanism.
The combination of these innovations leads to the creation of a novel European ecosystem, involving a network of data sharing centres (e.g. hospitals, clinics, labs) interested in feeding the platform by providing access to their wealth of data thanks to MHMD privacy enhancing features, secure and trustable conditions, thus enabling the set up of a knowledge network, revolving around an “Information commons”, providing continuous data flow from individuals and healthcare providers to the research community, harnessing data for improving European competitiveness in the field of precision and personalised medicine. The ultimate impact sought by MHMD was to:
1) To facilitate access to sensitive health data while maintaining privacy and security in compliance-by-design with the GDPR.
2) To allow individuals to gain a new level of control over their data, using a dynamic consent interface and being generally more engaged in the management of their health data;
3) To unlock the value of large (big) biomedical data sets and thus foster innovation, including the development of medical artificial intelligence solutions.
MHMD managed to provide these impacts, supporting the creation of an advanced data-driven ecosystem for the biomedical sector, sustaining EU competitiveness while enabling the full implementation and enforcement of the newly-introduced GDPR, which was one of the principal value proposition of the project. The development of tools for secure and privacy-preserving data analysis (the “visiting mode”), including also federated machine learning, establishes a robust proof of concept for this new data access modality showing how it can support the biomedical innovation value chain, from biomedical research to AI development. Additionally, the project pioneered a novel methodology for creating fully synthetic datasets, managing to combine the richness and granularity of individual dataset with the complete anonymity generated through an algorithm, and not directly linked to any specific individual. The usage of this methodology at scale will significantly facilitate the creation of significant and high-quality datasets free from the GDPR-related privacy concerns and regulatory burdens associated with standard datasets, benefitting both the industry and the biomedical research.
