FAIRness through automation: development of an automated medical data integration infrastructure for FAIR health data in a maximum care university hospital.

BACKGROUND: Secondary use of routine medical data is key to large-scale clinical and health services research. In a maximum care hospital, the volume of data generated exceeds the limits of big data on a daily basis. This so-called "real world data" are essential to complement knowledge and results from clinical trials. Furthermore, big data may help in establishing precision medicine. However, manual data extraction and annotation workflows to transfer routine data into research data would be complex and inefficient. Generally, best practices for managing research data focus on data output rather than the entire data journey from primary sources to analysis. To eventually make routinely collected data usable and available for research, many hurdles have to be overcome. In this work, we present the implementation of an automated framework for timely processing of clinical care data including free texts and genetic data (non-structured data) and centralized storage as Findable, Accessible, Interoperable, Reusable (FAIR) research data in a maximum care university hospital. METHODS: We identify data processing workflows necessary to operate a medical research data service unit in a maximum care hospital. We decompose structurally equal tasks into elementary sub-processes and propose a framework for general data processing. We base our processes on open-source software-components and, where necessary, custom-built generic tools. RESULTS: We demonstrate the application of our proposed framework in practice by describing its use in our Medical Data Integration Center (MeDIC). Our microservices-based and fully open-source data processing automation framework incorporates a complete recording of data management and manipulation activities. The prototype implementation also includes a metadata schema for data provenance and a process validation concept. All requirements of a MeDIC are orchestrated within the proposed framework: Data input from many heterogeneous sources, pseudonymization and harmonization, integration in a data warehouse and finally possibilities for extraction or aggregation of data for research purposes according to data protection requirements. CONCLUSION: Though the framework is not a panacea for bringing routine-based research data into compliance with FAIR principles, it provides a much-needed possibility to process data in a fully automated, traceable, and reproducible manner.

Management of Metadata Types in Basic Cardiological Research.

INTRODUCTION: Ensuring scientific reproducibility and compliance with documentation guidelines of funding bodies and journals is a topic of greatly increasing importance in biomedical research. Failure to comply, or unawareness of documentation standards can have adverse effects on the translation of research into patient treatments, as well as economic implications. In the context of the German Research Foundation-funded collaborative research center (CRC) 1002, an IT-infrastructure sub-project was designed. Its goal has been to establish standardized metadata documentation and information exchange benefitting the participating research groups with minimal additional documentation efforts. METHODS: Implementation of the self-developed menoci-based research data platform (RDP) was driven by close communication and collaboration with researchers as early adopters and experts. Requirements analysis and concept development involved in person observation of experimental procedures, interviews and collaboration with researchers and experts, as well as the investigation of available and applicable metadata standards and tools. The Drupal-based RDP features distinct modules for the different documented data and workflow types, and both the development and the types of collected metadata were continuously reviewed and evaluated with the early adopters. RESULTS: The menoci-based RDP allows for standardized documentation, sharing and cross-referencing of different data types, workflows, and scientific publications. Different modules have been implemented for specific data types and workflows, allowing for the enrichment of entries with specific metadata and linking to further relevant entries in different modules. DISCUSSION: Taking the workflows and datasets of the frequently involved experimental service projects as a starting point for (meta-)data types to overcome irreproducibility of research data, results in increased benefits for researchers with minimized efforts. While the menoci-based RDP with its data models and metadata schema was originally developed in a cardiological context, it has been implemented and extended to other consortia at GÃuttingen Campus and beyond in different life science research areas.

Digital teaching as an instrument for cross-location teaching networks in medical informatics: opportunities and challenges.

The increasingly digitized healthcare system requires new skills from all those involved. In order to impart these competencies, appropriate courses must be developed at educational institutions. In view of the rapid development of new aspects of digitization, this presents a challenge; suitable teaching formats must be developed successively. The establishment of cross-location teaching networks is one way to better meet training needs and to make the necessary spectrum of educational content available. As part of the Medical Informatics Initiative, the HiGHmed consortium is establishing such a teaching network, in the field of medical informatics, which covers many topics related to the digitization of the health care system. Various problem areas in the German education system were identified that hinder the development of the teaching network. These problem areas were prioritized firstly according to the urgency of the solution from the point of view of the HiGHmed consortium and secondly according to existing competencies in the participating societies. A workshop on the four most relevant topics was organized with experts from the German Society for Medical Informatics, Biometry and Epidemiology (GMDS), the Society for Medical Education (GMA) and the HiGHmed consortium. These are: recognition of exam results from teaching modules that are offered digitally and across locations, and their integration into existing curricula; recognition of digital, cross-location teaching in the teachers' teaching load; nationwide uniform competencies for teachers, in order to be able to conduct digital teaching effectively and with comparable quality; technical infrastructure to efficiently and securely communicate and manage the recognition of exam results between educational institutions. For all subject areas, existing preliminary work was identified on the basis of working questions, and short- and long-term needs for action were formulated. Finally, a need for the redesign of a technologically supported syntactic and semantic interoperability of learning performance recording was identified.

Cardiac Tissue Engineering as Use Case to Connect Biomedical Research Laboratories to an Emerging Global Data Infrastructure.

Methods for cardiac tissue engineering and application in experiments are core technologies developed at the Institute of Pharmacology and Toxicology in Göttingen. As is the case in many academic research laboratories data capture and documentation may be improved to latest methods of digital research. A comprehensive information system infrastructure is the foundation of further advances toward automation of lab processes. A data management system concept is proposed and prototypically deployed that enables traceability of assets within the lab and reproducibility of published assays and results. The prototype integrates existing electronic lab notebook, experiment result database, and a newly introduced research data management system by means of a custom developed portal and integration component. The architecture concept and developed integration tools explore connection of routine experimental work in a biomedical research lab to a universal infrastructure of data.