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Updated: Jul 30, 2025

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Published on: May 27, 2022
Data integration between clinical research and patient care: A framework for context-depending data sharing and in
Katja Hoffmann1,2, Anne Pelz1, Elena Karg1
1Institute for Medical Informatics and Biometry, Faculty of Medicine Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany.
This study introduces a novel software framework enabling seamless bidirectional data flow between clinical care and research. It supports both patient treatment decisions and research initiatives while ensuring data protection and quality.
Area of Science:
- Biomedical Informatics
- Clinical Research Informatics
- Health Data Management
Background:
- Bridging the gap between clinical practice and research is hindered by data transfer challenges.
- Existing solutions lack a unified approach for bidirectional data flow between treatment and research settings.
- Routine clinical data is often inaccessible for research, and research findings are slow to integrate into patient care.
Purpose of the Study:
- To present a generic, web-based software framework for integrated health data management and computational analytics.
- To facilitate a bidirectional transfer of data between clinical treatment contexts and research settings.
- To support both patient-specific healthcare decisions and research endeavors while ensuring data protection and quality.
Main Methods:
- Developed a web-based software framework based on a generic data management concept.
- Integrated data analysis, visualization, computer simulation, and model prediction functionalities.
- Implemented a regulation-compliant pseudonymization service with audit trail functionality.
- Created tailored clinical and research views within the front-end application.
Main Results:
- Demonstrated a feasible integrated generation and backward propagation of data analysis results and model predictions.
- Enabled patient-specific data visualization, analysis, and outcome prediction in the clinical view.
- Facilitated exploration of pseudonymized data in the research view.
- Showcased successful application through two use-cases in haematology/oncology.
Conclusions:
- The developed framework effectively integrates clinical data and computational analytics for improved healthcare and research.
- It enables seamless integration into clinical information systems or electronic health records.
- The solution addresses the need for efficient and secure bidirectional data exchange in biomedical research and practice.
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