Multilingual Medical Data Models in ODM Format

Breil, Bernhard; Kenneweg, J.; Bruland, Philipp; Doods, D.; Trinczek, Benjamin; Dugas, Martin

doi:10.4338/aci-2012-03-ra-0011

Cited by 27 publications

(14 citation statements)

References 16 publications

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“…The general idea of rating and commenting information models has also been implemented by Breil et al [52] in a portal for Medical Data Models. A large number of record forms has been collected from various sources, and can also be imported from and exported to multiple formats.…”

Section: Discussionmentioning

confidence: 99%

Validating archetypes for the Multiple Sclerosis Functional Composite

Braun

Brandt

Schulz

et al. 2014

BMC Med Inform Decis Mak

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BackgroundNumerous information models for electronic health records, such as openEHR archetypes are available. The quality of such clinical models is important to guarantee standardised semantics and to facilitate their interoperability. However, validation aspects are not regarded sufficiently yet. The objective of this report is to investigate the feasibility of archetype development and its community-based validation process, presuming that this review process is a practical way to ensure high-quality information models amending the formal reference model definitions.MethodsA standard archetype development approach was applied on a case set of three clinical tests for multiple sclerosis assessment: After an analysis of the tests, the obtained data elements were organised and structured. The appropriate archetype class was selected and the data elements were implemented in an iterative refinement process. Clinical and information modelling experts validated the models in a structured review process.ResultsFour new archetypes were developed and publicly deployed in the openEHR Clinical Knowledge Manager, an online platform provided by the openEHR Foundation. Afterwards, these four archetypes were validated by domain experts in a team review. The review was a formalised process, organised in the Clinical Knowledge Manager. Both, development and review process turned out to be time-consuming tasks, mostly due to difficult selection processes between alternative modelling approaches. The archetype review was a straightforward team process with the goal to validate archetypes pragmatically.ConclusionsThe quality of medical information models is crucial to guarantee standardised semantic representation in order to improve interoperability. The validation process is a practical way to better harmonise models that diverge due to necessary flexibility left open by the underlying formal reference model definitions.This case study provides evidence that both community- and tool-enabled review processes, structured in the Clinical Knowledge Manager, ensure archetype quality. It offers a pragmatic but feasible way to reduce variation in the representation of clinical information models towards a more unified and interoperable model.

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Section: Discussionmentioning

confidence: 99%

Validating archetypes for the Multiple Sclerosis Functional Composite

Braun

Brandt

Schulz

et al. 2014

BMC Med Inform Decis Mak

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“…This validation inspects the association of ODM elements – for instance the group affiliation of items or item groups in forms – which is covered by OIDs within the element structure. Syntactic correctness was validated by uploading the results into the portal of medical data models [25], which is based on ODM. During the upload process each XML file is checked whether it complies with the ODM schema definition.…”

Section: Methodsmentioning

confidence: 99%

S2O – A software tool for integrating research data from general purpose statistic software into electronic data capture systems

Bruland

Dugas

2017

BMC Med Inform Decis Mak

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BackgroundData capture for clinical registries or pilot studies is often performed in spreadsheet-based applications like Microsoft Excel or IBM SPSS. Usually, data is transferred into statistic software, such as SAS, R or IBM SPSS Statistics, for analyses afterwards. Spreadsheet-based solutions suffer from several drawbacks: It is generally not possible to ensure a sufficient right and role management; it is not traced who has changed data when and why. Therefore, such systems are not able to comply with regulatory requirements for electronic data capture in clinical trials. In contrast, Electronic Data Capture (EDC) software enables a reliable, secure and auditable collection of data. In this regard, most EDC vendors support the CDISC ODM standard to define, communicate and archive clinical trial meta- and patient data. Advantages of EDC systems are support for multi-user and multicenter clinical trials as well as auditable data. Migration from spreadsheet based data collection to EDC systems is labor-intensive and time-consuming at present. Hence, the objectives of this research work are to develop a mapping model and implement a converter between the IBM SPSS and CDISC ODM standard and to evaluate this approach regarding syntactic and semantic correctness.ResultsA mapping model between IBM SPSS and CDISC ODM data structures was developed. SPSS variables and patient values can be mapped and converted into ODM. Statistical and display attributes from SPSS are not corresponding to any ODM elements; study related ODM elements are not available in SPSS. The S2O converting tool was implemented as command-line-tool using the SPSS internal Java plugin. Syntactic and semantic correctness was validated with different ODM tools and reverse transformation from ODM into SPSS format. Clinical data values were also successfully transformed into the ODM structure.ConclusionTransformation between the spreadsheet format IBM SPSS and the ODM standard for definition and exchange of trial data is feasible. S2O facilitates migration from Excel- or SPSS-based data collections towards reliable EDC systems. Thereby, advantages of EDC systems like reliable software architecture for secure and traceable data collection and particularly compliance with regulatory requirements are achievable.Electronic supplementary materialThe online version of this article (doi:10.1186/s12911-016-0402-4) contains supplementary material, which is available to authorized users.

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“…More than 80% of these items are coded with terminology codes [21]. The scope of the evaluation was a test with real medical forms that meet the following criteria and were manually selected out of the repository:…”

Section: Methodsmentioning

confidence: 99%

Automated UMLS-Based Comparison of Medical Forms

2013

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Medical forms are very heterogeneous: on a European scale there are thousands of data items in several hundred different systems. To enable data exchange for clinical care and research purposes there is a need to develop interoperable documentation systems with harmonized forms for data capture. A prerequisite in this harmonization process is comparison of forms. So far – to our knowledge – an automated method for comparison of medical forms is not available. A form contains a list of data items with corresponding medical concepts. An automatic comparison needs data types, item names and especially item with these unique concept codes from medical terminologies. The scope of the proposed method is a comparison of these items by comparing their concept codes (coded in UMLS). Each data item is represented by item name, concept code and value domain. Two items are called identical, if item name, concept code and value domain are the same. Two items are called matching, if only concept code and value domain are the same. Two items are called similar, if their concept codes are the same, but the value domains are different. Based on these definitions an open-source implementation for automated comparison of medical forms in ODM format with UMLS-based semantic annotations was developed. It is available as package compareODM from http://cran.r-project.org. To evaluate this method, it was applied to a set of 7 real medical forms with 285 data items from a large public ODM repository with forms for different medical purposes (research, quality management, routine care). Comparison results were visualized with grid images and dendrograms. Automated comparison of semantically annotated medical forms is feasible. Dendrograms allow a view on clustered similar forms. The approach is scalable for a large set of real medical forms.

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Multilingual Medical Data Models in ODM Format

Abstract: The portal provides a system-independent repository for multilingual data models in ODM format which can be used by physicians. It serves as a platform for discussion and enables the exchange of multilingual medical data models in a standardized way.

Cited by 27 publications

References 16 publications

Validating archetypes for the Multiple Sclerosis Functional Composite

Validating archetypes for the Multiple Sclerosis Functional Composite

S2O – A software tool for integrating research data from general purpose statistic software into electronic data capture systems

Automated UMLS-Based Comparison of Medical Forms

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