Project
The TI4PEC Project
Intended ResultsThis project will be instrumental to enable clinical research based on routinely collected clinical data from Electronic Health Records (EHR) in Pediatric Emergency Care (PEC) at a global scale. Unlocking the full potential of EHR data from Emergency Departments (EDs) by overcoming existing challenges around data interoperability and sharing will significantly impact the development/refinement of preventive and treatment strategies to for the vulnerable population of acutely ill and injured children across countries and continents.
The Business CaseTI4PEC is a collaborative effort among three key partners: Tune Insight, DataRiver, and University of Padua, each contributing unique expertise to revolutionize healthcare. In the ever-evolving landscape of healthcare and data-driven technologies, our project presents a compelling business case that not only promises significant revenue generation but also addresses critical industry challenges.
Development GoalsThis project contributes significantly to several United Nations SDGs, particularly SDG 3 (Good Health and Well-being) by enabling secure, collaborative medical research that advances precision medicine. It aligns with SDG 9 (Industry, Innovation, and Infrastructure) by fostering innovation through a scalable, privacy-preserving data-sharing platform. By promoting inclusivity in healthcare research, it addresses SDG 10 (Reducing Inequality), while ensuring responsible data management supports SDG 11 (Sustainable Cities and Communities). Furthermore, its focus on partnerships and ethical practices aligns with SDGs 17 (Partnerships for the Goals) and 16 (Peace, Justice, and Strong Institutions).
Our PlanOur plan is to deploy and scale the solution focused on Pediatric Emergency Care and the Electronic Health Record in the context of the Pediatric Emergency Research Network (PERN) starting with selected Italian and Swiss partners and gradually growing to the entire network. While creating value and generating initial revenue, it will also be used as a showcase deployment paving the way for the next steps.
Our Technical ApproachOur approach centers on Federated Analytics (FA), where institutions collaborate on joint analyses without transferring patient-level data. To enhance privacy, we integrate Privacy-Enhancing Technologies (PETs) like Multiparty Homomorphic Encryption (MHE), allowing encrypted computations on distributed data, and differential privacy to add noise to results. This ensures that sensitive information remains protected while enabling secure and scalable collaboration across multiple institutions.
Ethics
TI4Health is at the forefront of advancing healthcare research through federated analytics.
By enabling researchers to access and train machine learning models on distributed datasets from different geographic zones, TI4Health inherently promotes fairness and reduces bias in AI models.
This unique advantage stems from its ability to draw upon diverse and inclusive data sources, ultimately leading to more equitable and robust AI solutions.