Abstract:Metadata management is an essential condition to follow the FAIR principles. Therefore, metadata management was one asset of an accompanying project within a funding scheme for registries in health services research. The metadata of the funded projects were acquired, combined in a database compatible with the metamodel of ISO/IEC 11179 “Information technology – Metadata registries” third edition (ISO/IEC 11179-3), and analyzed in order to support the development and the operation of the registries. In the seco… Show more
“…In the initial phase of registry development, the accompanying project recommended a structure for a catalog of data elements that registries could use to define their metadata [ 18 ]. This approach was based on the work of Leiner and Haux who introduced a documentation protocol with a documentation scheme [ 19 ].…”
Section: Methodsmentioning
confidence: 99%
“… Fig. 1 Mapping of the catalog of data elements (right) to the metamodel of ISO/IEC 11179 (left) adopted from [ 7 ] …”
Section: Methodsmentioning
confidence: 99%
“…Within the first conceptual phase of registries in health services research funded by the German Federal Ministry of Education and Research (BMBF), nearly 4,000 data elements were defined in total by 15 projects with a mean of 260 ± 195 elements (range 48 to 756) per registry [ 7 ]. Based on the identification of commonly used elements, the idea of formulating recommendations for shared data elements could only be carried out for seven data elements: sex, date of birth, number of procedures, reason for admission, pseudonym, highest school degree, and highest professional qualification.…”
Background
The selection of data elements is a decisive task within the development of a health registry. Having the right metadata is crucial for answering the particular research questions. Furthermore, the set of data elements determines the registries’ readiness of interoperability and data reusability to a major extent. Six health registries shared and published their metadata within a German funding initiative. As one step in the direction of a common set of data elements, a selection of those metadata was evaluated with regard to their appropriateness for a broader usage.
Methods
Each registry was asked to contribute a 10%-selection of their data elements to an evaluation sample. The survey was set up with the online survey tool „LimeSurvey Cloud”. The registries and an accompanying project participated in the survey with one vote for each project. The data elements were offered in content groups along with the question of whether the data element is appropriate for health registries on a broader scale. The question could be answered using a Likert scale with five options. Furthermore, “no answer” was allowed. The level of agreement was assessed using weighted Cohen’s kappa and Kendall’s coefficient of concordance.
Results
The evaluation sample consisted of 269 data elements. With a grade of “perhaps recommendable” or higher in the mean, 169 data elements were selected. These data elements belong preferably to groups’ demography, education/occupation, medication, and nutrition. Half of the registries lost significance compared with their percentage of data elements in the evaluation sample, one remained stable. The level of concordance was adequate.
Conclusions
The survey revealed a set of 169 data elements recommended for health registries. When developing a registry, this set could be valuable help in selecting the metadata appropriate to answer the registry’s research questions. However, due to the high specificity of research questions, data elements beyond this set will be needed to cover the whole range of interests of a register. A broader discussion and subsequent surveys are needed to establish a common set of data elements on an international scale.
“…In the initial phase of registry development, the accompanying project recommended a structure for a catalog of data elements that registries could use to define their metadata [ 18 ]. This approach was based on the work of Leiner and Haux who introduced a documentation protocol with a documentation scheme [ 19 ].…”
Section: Methodsmentioning
confidence: 99%
“… Fig. 1 Mapping of the catalog of data elements (right) to the metamodel of ISO/IEC 11179 (left) adopted from [ 7 ] …”
Section: Methodsmentioning
confidence: 99%
“…Within the first conceptual phase of registries in health services research funded by the German Federal Ministry of Education and Research (BMBF), nearly 4,000 data elements were defined in total by 15 projects with a mean of 260 ± 195 elements (range 48 to 756) per registry [ 7 ]. Based on the identification of commonly used elements, the idea of formulating recommendations for shared data elements could only be carried out for seven data elements: sex, date of birth, number of procedures, reason for admission, pseudonym, highest school degree, and highest professional qualification.…”
Background
The selection of data elements is a decisive task within the development of a health registry. Having the right metadata is crucial for answering the particular research questions. Furthermore, the set of data elements determines the registries’ readiness of interoperability and data reusability to a major extent. Six health registries shared and published their metadata within a German funding initiative. As one step in the direction of a common set of data elements, a selection of those metadata was evaluated with regard to their appropriateness for a broader usage.
Methods
Each registry was asked to contribute a 10%-selection of their data elements to an evaluation sample. The survey was set up with the online survey tool „LimeSurvey Cloud”. The registries and an accompanying project participated in the survey with one vote for each project. The data elements were offered in content groups along with the question of whether the data element is appropriate for health registries on a broader scale. The question could be answered using a Likert scale with five options. Furthermore, “no answer” was allowed. The level of agreement was assessed using weighted Cohen’s kappa and Kendall’s coefficient of concordance.
Results
The evaluation sample consisted of 269 data elements. With a grade of “perhaps recommendable” or higher in the mean, 169 data elements were selected. These data elements belong preferably to groups’ demography, education/occupation, medication, and nutrition. Half of the registries lost significance compared with their percentage of data elements in the evaluation sample, one remained stable. The level of concordance was adequate.
Conclusions
The survey revealed a set of 169 data elements recommended for health registries. When developing a registry, this set could be valuable help in selecting the metadata appropriate to answer the registry’s research questions. However, due to the high specificity of research questions, data elements beyond this set will be needed to cover the whole range of interests of a register. A broader discussion and subsequent surveys are needed to establish a common set of data elements on an international scale.
“…A data element comprises two parts in ISO/IEC 11179 metamodel: Data Element Concept and Value Domain. A Data Element Concept joins an Object class (like a person) with its Property (like sex) [ 47 ]. The Value Domain is the set of permissible values for one or more data elements.…”
Section: Data Elements Derived From Hcdm and Their Descriptionmentioning
Background
Health data from different specialties or domains generallly have diverse formats and meanings, which can cause semantic communication barriers when these data are exchanged among heterogeneous systems. As such, this study is intended to develop a national health concept data model (HCDM) and develop a corresponding system to facilitate healthcare data standardization and centralized metadata management.
Methods
Based on 55 data sets (4640 data items) from 7 health business domains in China, a bottom-up approach was employed to build the structure and metadata for HCDM by referencing HL7 RIM. According to ISO/IEC 11179, a top-down approach was used to develop and standardize the data elements.
Results
HCDM adopted three-level architecture of class, attribute and data type, and consisted of 6 classes and 15 sub-classes. Each class had a set of descriptive attributes and every attribute was assigned a data type. 100 initial data elements (DEs) were extracted from HCDM and 144 general DEs were derived from corresponding initial DEs. Domain DEs were transformed by specializing general DEs using 12 controlled vocabularies which developed from HL7 vocabularies and actual health demands. A model-based system was successfully established to evaluate and manage the NHDD.
Conclusions
HCDM provided a unified metadata reference for multi-source data standardization and management. This approach of defining health data elements was a feasible solution in healthcare information standardization to enable healthcare interoperability in China.
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