Artikel
Challenges in Retrieving Patterns from Generic Data Structures in Clinical Systems – a Technical Case Report
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Autoren
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Richard Gebler
- Institut für Medizinische Informatik und Biometrie, Medizinische Fakultät Carl Gustav Carus der Technischen Universität Dresden, Dresden, Germany
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Ines Reinecke
- Datenintegrationszentrum, Zentrum für Medizinische Informatik, Universitätsklinikum Carl Gustav Carus, Dresden, Germany; Institut für Medizinische Informatik und Biometrie, Medizinische Fakultät Carl Gustav Carus der Technischen Universität Dresden, Dresden, Germany
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Martin Sedlmayr
- Institut für Medizinische Informatik und Biometrie, Medizinische Fakultät Carl Gustav Carus der Technischen Universität Dresden, Dresden, Germany
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Miriam Goldammer
- Institut für Medizinische Informatik und Biometrie, Medizinische Fakultät Carl Gustav Carus der Technischen Universität Dresden, Dresden, Germany
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Hung Manh Nguyen
- Institut für Medizinische Informatik und Biometrie, Medizinische Fakultät Carl Gustav Carus der Technischen Universität Dresden, Dresden, Germany
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Luise Donat
- Institut für Medizinische Informatik und Biometrie, Medizinische Fakultät Carl Gustav Carus der Technischen Universität Dresden, Dresden, Germany
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Jens Helbig
- Datenintegrationszentrum, Zentrum für Medizinische Informatik, Universitätsklinikum Carl Gustav Carus, Dresden, Germany
| Veröffentlicht: | 6. September 2024 |
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Gliederung
Text
The secondary use of data in clinical environments offers significant opportunities to enhance medical research and practices. This study addresses the challenges of extracting data from generic data structures, especially the Entity-Attribute-Value (EAV) model, to convert them into a more suitable format for analysis. The EAV model is widely used in clinical information systems due to its adaptability, but often complicates data retrieval for research purposes due to its vertical data structure and dynamic schema. Therefore, a methodological approach was developed to address the handling of these generic data structures, which involves five steps: 1) understanding the specific clinical processes to determine data collection points and involved roles; 2) analysing the data source to understand the data structure and metadata; 3) reversing a use-case-specific data structure to map the front-end data input to its storage format; 4) analysing the content to identify medical information and establish connections; and 5) managing schema changes to maintain data integrity. Applying this method to the hospital information system has shown that EAV-based data can be converted into a structured format, suitable for research. This conversion facilitated the reduction of data sparsity and improved the manageability of schema changes without affecting other classes of data. The approach provided a systematic method for dealing with complex data relationships and maintaining the integrity of the data.
The authors declare that they have no competing interests.
The authors declare that an ethics committee vote is not required.
