Distributed Analytics on Sensitive Medical Data: The Personal Health Train

TLDR

Recent advances generate vast healthcare data, but its sensitive nature limits sharing, hindering analytics that could improve treatment, diagnosis, and patient empowerment. The paper introduces the Personal Health Train, a distributed analytics framework that lets data owners retain control while enabling reuse of their data. The PHT keeps data in place and sends analytical tasks to data sources, providing a flexible, FAIR‑compliant distributed platform. The PHT facilitates responsible use of sensitive data by adopting international principles and regulations.

Abstract

In recent years, as newer technologies have evolved around the healthcare ecosystem, more and more data have been generated. Advanced analytics could power the data collected from numerous sources, both from healthcare institutions, or generated by individuals themselves via apps and devices, and lead to innovations in treatment and diagnosis of diseases; improve the care given to the patient; and empower citizens to participate in the decision-making process regarding their own health and well-being. However, the sensitive nature of the health data prohibits healthcare organizations from sharing the data. The Personal Health Train (PHT) is a novel approach, aiming to establish a distributed data analytics infrastructure enabling the (re)use of distributed healthcare data, while data owners stay in control of their own data. The main principle of the PHT is that data remain in their original location, and analytical tasks visit data sources and execute the tasks. The PHT provides a distributed, flexible approach to use data in a network of participants, incorporating the FAIR principles. It facilitates the responsible use of sensitive and/or personal data by adopting international principles and regulations. This paper presents the concepts and main components of the PHT and demonstrates how it complies with FAIR principles.