Integration of tools for binding archetypes to SNOMED CT.

BACKGROUND: The Archetype formalism and the associated Archetype Definition Language have been proposed as an ISO standard for specifying models of components of electronic healthcare records as a means of achieving interoperability between clinical systems. This paper presents an archetype editor with support for manual or semi-automatic creation of bindings between archetypes and terminology systems. METHODS: Lexical and semantic methods are applied in order to obtain automatic mapping suggestions. Information visualisation methods are also used to assist the user in exploration and selection of mappings. RESULTS: An integrated tool for archetype authoring, semi-automatic SNOMED CT terminology binding assistance and terminology visualization was created and released as open source. CONCLUSION: Finding the right terms to bind is a difficult task but the effort to achieve terminology bindings may be reduced with the help of the described approach. The methods and tools presented are general, but here only bindings between SNOMED CT and archetypes based on the openEHR reference model are presented in detail.

Semantic issues in integrating data from different models to achieve data interoperability.

Matching clinical data to codes in controlled terminologies is the first step towards achieving standardisation of data for safe and accurate data interoperability. The MoST automated system was used to generate a list of candidate SNOMED CT code mappings. The paper discusses the semantic issues which arose when generating lexical and semantic matches of terms from the archetype model to relevant SNOMED codes. It also discusses some of the solutions that were developed to address the issues. The aim of the paper is to highlight the need to be flexible when integrating data from two separate models. However, the paper also stresses that the context and semantics of the data in either model should be taken into consideration at all times to increase the chances of true positives and reduce the occurrence of false negatives.

Unambiguous data modeling to ensure higher accuracy term binding to clinical terminologies.

Work in the field of recording standard, coded data is important to reduce medical errors caused by misinterpretation and misrepresentation of data. The paper discusses the need to ensure that the source of the data i.e. the clinical data model is unambiguous to increase the quality and accuracy of the data mapping to terminology codes. The study chooses one especially ambiguous data model and remodels it to make clearer both the structure of the data, as well as its intended use and semantics. By ensuring an unambiguous model, results of the data mapping increased in accuracy from 64.7% to 80.55%. The clinical experts evaluating the models found it easier working with the revised model and agreed on the mappings 93.1% times as against 48.57% times previously. The aim of the study is to encourage good modeling practice to enable clinicians to record and code patient data unambiguously and accurately.