Infections caused by resistant microorganisms often fail to respond to the standard treatment, resulting in prolonged hospital stays, higher health care expenditures, and a greater risk of death.1 Antimicrobials Resistant Organism (AMRO) or Superbugs are invisible, and can survive on surfaces for up to three days. That means that they can be transferred when one infected person simply touches another, or when the patient touches something on which the pathogen resides like a stethoscope or a TV remote control.2 Antibiotic resistance varies according to geographic locations and is directly proportional to the use and misuse of antibiotics. Active surveillance systems are in place across all European member states and Norway and are to be considered one of the main contributing factors to the reduction AMRO infections over the years. On the other hand the effort done up to now is not enough to eradicate AMRO/superbugs infections and ANTI-SUPERBUGS PCP will challenge the industry to develop solutions that in contact with resistant microorganisms can detect their presence and give real-time feedback to the user and at the same time share the information with the healthcare provider electronic record systems linking the infection with the place of the detection. The project will be developped with the support of Sara Bedin, who will provide her know-how in Procurement in Innovation.

In recent years, data-driven medicine has gained increasing importance in terms of diagnosis, treatment, and research due to the exponential growth of healthcare data. The linkage of cross-border health data from various sources, including genomics, and analysis via innovative approaches based on artificial intelligence (AI) will enable a better understanding of risk factors, causes, and the development of optimal treatment in different disease areas. Nevertheless, the reuse of patient data is often limited to datasets available at a single medical centre. The main reasons why health data is not shared across institutional borders rely on ethical, legal, and privacy aspects and rules. Therefore, in order to (1) enable health data sharing across national borders, (2) fully comply with present GDPR privacy guidelines / regulations and (3) innovate by pushing research beyond the state of the art, BETTER proposes a robust decentralised privacy-preserving infrastructure which will empower researchers, innovators and healthcare professionals to exploit the full potential of larger sets of multi-source health data via tailored made AI tools useful to compare, integrate, and analyse in a secure, cost-effective fashion; with the very final aim of supporting the improvement of citizen’s health outcomes. In detail, this interdisciplinary project proposes the co-creation of 3 clinical use cases involving 7 medical centres located in the EU and beyond, where sensitive patient data, including genomics, are made available and analysed in a GDPR-compliant mechanism via a Distributed Analytics (DA) paradigm called the Personal Health Train (PHT). The main principle of the PHT is that the analytical task is brought to the data provider (medical centre) and the data instances remain in their original location. In this project, two mature implementations of the PHT (PADME and Vantage6) already validated in real-world scenarios will be fused together to build the BETTER platform.