Background In recent years, data-driven medicine has gained increasing importance in terms of diagnosis, treatment, and research due to the exponential growth of health care data. However, data protection regulations prohibit data centralisation for analysis purposes because of potential privacy risks like the accidental disclosure of data to third parties. Therefore, alternative data usage policies, which comply with present privacy guidelines, are of particular interest. Objective We aim to enable analyses on sensitive patient data by simultaneously complying with local data protection regulations using an approach called the Personal Health Train (PHT), which is a paradigm that utilises distributed analytics (DA) methods. The main principle of the PHT is that the analytical task is brought to the data provider and the data instances remain in their original location. Methods In this work, we present our implementation of the PHT paradigm, which preserves the sovereignty and autonomy of the data providers and operates with a limited number of communication channels. We further conduct a DA use case on data stored in three different and distributed data providers. Results We show that our infrastructure enables the training of data models based on distributed data sources. Conclusion Our work presents the capabilities of DA infrastructures in the health care sector, which lower the regulatory obstacles of sharing patient data. We further demonstrate its ability to fuel medical science by making distributed data sets available for scientists or health care practitioners.

In recent years, implementations enabling Distributed Analytics (DA) have gained considerable attention due to their ability to perform complex analysis tasks on decentralised data by bringing the analysis to the data. These concepts propose privacy-enhancing alternatives to data centralisation approaches, which have restricted applicability in case of sensitive data due to ethical, legal or social aspects. Nevertheless, the immanent problem of DA-enabling architectures is the black-box-alike behaviour of the highly distributed components originating from the lack of semantically enriched descriptions, particularly the absence of basic metadata for data sets or analysis tasks. To approach the mentioned problems, we propose a metadata schema for DA infrastructures, which provides a vocabulary to enrich the involved entities with descriptive semantics. We initially perform a requirement analysis with domain experts to reveal necessary metadata items, which represents the foundation of our schema. Afterwards, we transform the obtained domain expert knowledge into user stories and derive the most significant semantic content. In the final step, we enable machine-readability via RDF(S) and SHACL serialisations. We deploy our schema in a proof-of-concept monitoring dashboard to validate its contribution to the transparency of DA architectures. Additionally, we evaluate the schema's compliance with the FAIR principles. The evaluation shows that the schema succeeds in increasing transparency while being compliant with most of the FAIR principles. Because a common metadata model is critical for enhancing the compatibility between multiple DA infrastructures, our work lowers data access and analysis barriers. It represents an initial and infrastructure-independent foundation for the FAIRification of DA and the underlying scientific data management.

The constant upward movement of data-driven medicine as a valuable option to enhance daily clinical practice has brought new challenges for data analysts to get access to valuable but sensitive data due to privacy considerations. One solution for most of these challenges are Distributed Analytics (DA) infrastructures, which are technologies fostering collaborations between healthcare institutions by establishing a privacy-preserving network for data sharing. However, in order to participate in such a network, a lot of technical and administrative prerequisites have to be made, which could pose bottlenecks and new obstacles for non-technical personnel during their deployment. We have identified three major problems in the current state-of-the-art. Namely, the missing compliance with FAIR data principles, the automation of processes, and the installation. In this work, we present a seamless on-boarding workflow based on a DA reference architecture for data sharing institutions to address these problems. The on-boarding service manages all technical configurations and necessities to reduce the deployment time. Our aim is to use well-established and conventional technologies to gain acceptance through enhanced ease of use. We evaluate our development with six institutions across Germany by conducting a DA study with open-source breast cancer data, which represents the second contribution of this work. We find that our on-boarding solution lowers technical barriers and efficiently deploys all necessary components and is, therefore, indeed an enabler for collaborative data sharing.

The development of platforms for distributed analytics has been driven by a growing need to comply with various governance-related or legal constraints. Among these platforms, the so-called Personal Health Train (PHT) is one representative that has emerged over the recent years. However, in projects that require data from sites featuring different PHT infrastructures, institutions are facing challenges emerging from the combination of multiple PHT ecosystems, including data governance, regulatory compliance, or the modification of existing workflows. In these scenarios, the interoperability of the platforms is preferable. In this work, we introduce a conceptual framework for the technical interoperability of the PHT covering five essential requirements: Data integration, unified station identifiers, mutual metadata, aligned security protocols, and business logic. We evaluated our concept in a feasibility study that involves two distinct PHT infrastructures: PHT-meDIC and PADME. We analyzed data on leukodystrophy from patients in the University Hospitals of Tübingen and Leipzig, and patients with differential diagnoses at the University Hospital Aachen. The results of our study demonstrate the technical interoperability between these two PHT infrastructures, allowing researchers to perform analyses across the participating institutions. Our method is more space-efficient compared to the multi-homing strategy, and it shows only a minimal time overhead.

The growing interest in data-driven medicine, in conjunction with the formation of initiatives such as the European Health Data Space (EHDS) has demonstrated the need for methodologies that are capable of facilitating privacy-preserving data analysis. Distributed Analytics (DA) as an enabler for privacy-preserving analysis across multiple data sources has shown its potential to support data-intensive research. However, the application of DA creates new challenges stemming from its distributed nature, such as identifying single points of failure (SPOFs) in DA tasks before their actual execution. Failing to detect such SPOFs can, for example, result in improper termination of the DA code, necessitating additional efforts from multiple stakeholders to resolve the malfunctions. Moreover, these malfunctions disrupt the seamless conduct of DA and entail several crucial consequences, including technical obstacles to resolve the issues, potential delays in research outcomes, and increased costs. In this study, we address this challenge by introducing a concept based on a method called Smoke Testing, an initial and foundational test run to ensure the operability of the analysis code. We review existing DA platforms and systematically extract six specific Smoke Testing criteria for DA applications. With these criteria in mind, we create an interactive environment called Development Environment for AuTomated and Holistic Smoke Testing of Analysis-Runs (DEATHSTAR), which allows researchers to perform Smoke Tests on their DA experiments. We conduct a user-study with 29 participants to assess our environment and additionally apply it to three real use cases. The results of our evaluation validate its effectiveness, revealing that 96.6% of the analyses created and (Smoke) tested by participants using our approach successfully terminated without any errors. Thus, by incorporating Smoke Testing as a fundamental method, our approach helps identify potential malfunctions early in the development process, ensuring smoother data-driven research within the scope of DA. Through its flexibility and adaptability to diverse real use cases, our solution enables more robust and efficient development of DA experiments, which contributes to their reliability.