The PHEMS project is a joint EU project between European children's hospitals and technology partners. Our primary goal is to improve the use of health data between countries, particularly in the fields of children's health and rare diseases. PHEMS aims to tackle challenges in accessing relevant health data essential for international collaboration between scientists, clinicians, researchers, and the health industry—without compromising patient privacy.
The project will provide European children’s hospitals with a Pediatric Health Data Space (PHDS), consisting of technical components and governance frameworks. The objective is to facilitate access to health data, advance federated health data analysis and build services for the on-demand generation of shareable, synthetized, and anonymized datasets.
To achieve this, we leverage federated analytics, decentralized machine learning, synthetic and anonymized data, and a structured contractual and governance framework to enable long-term collaboration between data controllers (hospitals) and various use cases such as clinical and operational benchmarking, scientific research based on federated data analytics, federated machine learning for algorithm development and validation, and supporting clinical trials feasibility and execution.
The PHEMS project will validate its techniques through three clinical use cases. These are designed to demonstrate the benefits of the federated analytics network, validate the utility of algorithmically anonymized and synthetic data, and demonstrate the operability of the open ecosystem. Together, they serve as proof of principle, with the potential to scale across other clinical areas and hospital networks.
• Clinical Use Case 1: Cardiology Patients' Operation Benchmarking
Aiming to improve pediatric cardiology outcomes by creating a standardized, real-time system for data collection, monitoring, and quality measures, while promoting a culture of benchmarking across institutions.
• Clinical Use Case 2: Pediatric Intensive Care Unit (PICU) Sepsis
Developing and testing Machine Learning algorithm to predict sepsis across PICUs in four major European children’s hospitals by clustering patient trajectories based on outcomes.
• Clinical Use Case 3: Hematology; Hemophilia
Developing and testing a Machine Learning-based prediction algorithm to personalize treatment for pediatric hemophilia, aiming to reduce bleeding, improve quality of life, and lower costs.
Read more about the project:
www.PHEMS.eu
www.Linkedin.com/company/phems
www.Youtube.com/@PHEMS_EUProject
