Automated UMLS-Based Comparison of Medical Forms

Abstract

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.

Current applications and future directions for the CDISC Operational Data Model standard: A methodological review

“…In this case, using semantically annotated ODM helped drive automated transformations while preserving the original semantics. The open-source compareODM tool in Dugas et al [81] compared semantically enriched ODM forms and was able to automatically derive the differences between two versions of a form including identical, matching, or similar data items [43]. ODM has been used to integrate clinical research data into the i2b2 (Informatics for Integrating Biology and the Bedside) data model including both ontology and fact data [26, 46].…”

“…Research exists on the automated analysis of CRFs [43, 81], but none have analyzed the CRF from a regulatory perspective. The benefits proposed in the 2007 FDA pilot [87] could be explored.…”

Clustering clinical models from local electronic health records based on semantic similarity

“…In a study by Dugas et al, no semantic similarity estimate is used, it is a simple set-based approach where the number of terms that the templates have in common is used as a metric. The metric is used in a hierarchical clustering approach using dendrograms [45]. In a study by Allones et al, SNOMED CT based semantic search of archetypes is developed.…”

“…The contribution of the present study compared to these earlier approaches is that intrinsic similarity estimation is introduced to the field of content analysis which makes semantic similarities quantifiable. This means that the clustering approaches such as the study by Dugas et al [45] can be expanded with similarity estimation information.…”

“…In the evaluation, we chose to include 15 templates, which is comparable to the related studies where the sample size is 4 [47], 7 [45], and 25 [46] respectively. We chose the relatively limited number of templates to make the analysis transparent, which in our opinion is important in this methodological oriented study.…”