ArticleBMC medical informatics and decision making2023
FAIRness through automation: development of an automated medical data integration infrastructure for FAIR health data in a maximum care university hospital.
Article in BMC medical informatics and decision making, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Safeguarding patient privacy in eHealth systems: Bridging theory and practice through a scoping review and healthcare survey.PLOS digital health · 2026Article
- Leveraging Machine Learning and Robotic Process Automation to Identify and Convert Unstructured Colonoscopy Results Into Actionable Data: Proof-of-Concept Study.JMIR medical informatics · 2025Article
- I-ETL: an interoperability-aware health (meta)data pipeline to enable federated analyses.BMC medical informatics and decision making · 2025Article
- Enhancing Clinical Data Infrastructure for AI Research: Comparative Evaluation of Data Management Architectures.Journal of medical Internet research · 2025Article
- Proposal for Using AI to Assess Clinical Data Integrity and Generate Metadata: Algorithm Development and Validation.JMIR medical informatics · 2025Article
- Challenges and applications in generative AI for clinical tabular data in physiology.Pflugers Archiv : European journal of physiology · 2025Review
- Integrating Clinical Data and Medical Imaging in Lung Cancer: Feasibility Study Using the Observational Medical Outcomes Partnership Common Data Model Extension.JMIR medical informatics · 2024Article
- Research collaboration data platform ensuring general data protection.Scientific reports · 2024Article
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundSecondary 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.
methodsWe 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.
resultsWe 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.
conclusionThough 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.
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