TY - JOUR T1 - Using ontologies to improve semantic interoperability in health data JF - BMJ Health & Care Informatics SP - 309 LP - 315 DO - 10.14236/jhi.v22i2.159 VL - 22 IS - 2 AU - Harshana Liyanage AU - Paul Krause AU - Simon de Lusignan Y1 - 2015/04/01 UR - http://informatics.bmj.com/content/22/2/309.abstract N2 - The present–day health data ecosystem comprises a wide array of complex heterogeneous data sources. A wide range of clinical, health care, social and other clinically relevant information are stored in these data sources. These data exist either as structured data or as free-text. These data are generally individual personbased records, but social care data are generally case based and less formal data sources may be shared by groups. The structured data may be organised in a proprietary way or be coded using one-of-many coding, classification or terminologies that have often evolved in isolation and designed to meet the needs of the context that they have been developed. This has resulted in a wide range of semantic interoperability issues that make the integration of data held on these different systems changing. We present semantic interoperability challenges and describe a classification of these. We propose a four-step process and a toolkit for those wishing to work more ontologically, progressing from the identification and specification of concepts to validating a final ontology. The four steps are: (1) the identification and specification of data sources; (2) the conceptualisation of semantic meaning; (3) defining to what extent routine data can be used as a measure of the process or outcome of care required in a particular study or audit and (4) the formalisation and validation of the final ontology. The toolkit is an extension of a previous schema created to formalise the development of ontologies related to chronic disease management. The extensions are focused on facilitating rapid building of ontologies for time-critical research studies.What is already known on this topicOntologies are used in health care for (1) modelling the semantics of medical concepts and (2) to facilitate exchange of medical data between disparate systemsDiverse range of ontologies has been developed to semantically represent health care conceptsWhat this study addsA classification of semantic interoperability issues is presented in this studyAn extended toolkit that supports rapid building of ontologies related to chronic disease management is described ER -