ArticleJournal of translational medicine2020
Designing and piloting a generic research architecture and workflows to unlock German primary care data for secondary use.
Article in Journal of translational medicine, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 1 of them a synthesis that pooled it.
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Who cites it
12 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Functional Requirements for Medical Data Integration into Knowledge Management Environments: Requirements Elicitation Approach Based on Systematic Literature Analysis.Journal of medical Internet research · 2023Pooled it
- Lessons learned from implementing FAIRification workflows in diabetes research in Germany.PLOS digital health · 2026Article
- [Anonymization of general practitioners' electronic medical records in two research datasets].Gesundheitswesen (Bundesverband der Arzte des Offentlichen Gesundheitsdienstes (Germany)) · 2025Article
- The Journey to a FAIR CORE DATA SET for Diabetes Research in Germany.Scientific data · 2024Article
- Development of a Trusted Third Party at a Large University Hospital: Design and Implementation Study.JMIR medical informatics · 2024Article
- Article
- eHealth implementationFrontiers in digital health · 2024Article
- [BeoNet-Halle-development of a multifunctional database for the automated extraction of healthcare data from general practitioner and specialist practices].Bundesgesundheitsblatt, Gesundheitsforschung, Gesundheitsschutz · 2023Review
- Data integration between clinical research and patient care: A framework for context-depending data sharing and in silico predictions.PLOS digital health · 2023Article
- Identifying primary care datasets and perspectives on their secondary use: a survey of Australian data users and custodians.BMC medical informatics and decision making · 2022Article
- A theoretical model of health management using data-driven decision-making: the future of precision medicine and health.Journal of translational medicine · 2021Article
- Intelligent technology-driven diabetes prevention and control: From informatization management to artificial intelligence.Science progressReview
Corrections and comments
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Authors and funding
12 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundMedical data from family doctors are of great importance to health care researchers but seem to be locked in German practices and, thus, are underused in research. The RADAR project (Routine Anonymized Data for Advanced Health Services Research) aims at designing, implementing and piloting a generic research architecture, technical software solutions as well as procedures and workflows to unlock data from family doctor's practices. A long-term medical data repository for research taking legal requirements into account is established. Thereby, RADAR helps closing the gap between the European countries and to contribute data from primary care in Germany.
methodsThe RADAR project comprises three phases: (1) analysis phase, (2) design phase, and (3) pilot. First, interdisciplinary workshops were held to list prerequisites and requirements. Second, an architecture diagram with building blocks and functions, and an ordered list of process steps (workflow) for data capture and storage were designed. Third, technical components and workflows were piloted. The pilot was extended by a data integration workflow using patient-reported outcomes (paper-based questionnaires).
resultsThe analysis phase resulted in listing 17 essential prerequisites and guiding requirements for data management compliant with the General Data Protection Regulation (GDPR). Based on this list existing approaches to fulfil the RADAR tasks were evaluated-for example, re-using BDT interface for data exchange and Trusted Third Party-approach for consent management and record linkage. Consented data sets of 100 patients were successfully exported, separated into person-identifying and medical data, pseudonymised and saved. Record linkage and data integration workflows for patient-reported outcomes in the RADAR research database were successfully piloted for 63 responders.
conclusionThe RADAR project successfully developed a generic architecture together with a technical framework of tools, interfaces, and workflows for a complete infrastructure for practicable and secure processing of patient data from family doctors. All technical components and workflows can be reused for further research projects. Additionally, a Trusted Third Party-approach can be used as core element to implement data privacy protection in such heterogeneous family doctor's settings. Optimisations identified comprise a fully-electronic consent recording using tablet computers, which is part of the project's extension phase.
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