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.

The CLEF corpus: semantic annotation of clinical text.

The Clinical E-Science Framework (CLEF) project is building a framework for the capture, integration and presentation of clinical information: for clinical research, evidence-based health care and genotype-meets-phenotype informatics. A significant portion of the information required by such a framework originates as text, even in EHR-savvy organizations. CLEF uses Information Extraction (IE) to make this unstructured information available. An important part of IE is the identification of semantic entities and relationships. Typical approaches require human annotated documents to provide both evaluation standards and material for system development. CLEF has a corpus of clinical narratives, histopathology reports and imaging reports from 20 thousand patients. We describe the selection of a subset of this corpus for manual annotation of clinical entities and relationships. We describe an annotation methodology and report encouraging initial results of inter-annotator agreement. Comparisons are made between different text sub-genres, and between annotators with different skills.