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| Content Provider | ACM Digital Library |
|---|---|
| Author | Puustjärvi, Leena Puustjärvi, Juha |
| Abstract | Existing health information systems gather and organize patients' health documents into hierarchical structures, and support a variety of ways for organizing the documents, e.g., grouping together the documents by episode, clinical specialty or time period. The ability to organize documents is of prime importance but it does not solve the problem of data centric queries. The problem here is that the efficient usage of patients' health documentation often is data centric, meaning that data should be extracted from various documents and then integrated according to specific criteria. Unfortunately the computation required by such retrieval is not provided by the query languages that are designed to address hierarchical document structures such as XML documents. In order to avoid this problem, we have developed an OWL-ontology for clinical documents by exploiting the Refined Message Information Models (RMIM), which specify the semantics of clinical documents. In this paper we describe how RMIMs can be transformed into OWL, how such OWL ontologies can be integrated, and how clinical documents can be stored in an archive that is organized according to the integrated ontology. We have restricted on Continuity of Care Documents (CCDs) as they are increasingly used for collecting together and storing health information that includes data gathered from different sources such as from health care providers, pharmacies, insures, the consumer, and third parties. |
| Starting Page | 342 |
| Ending Page | 347 |
| Page Count | 6 |
| File Format | |
| ISBN | 9781450313063 |
| DOI | 10.1145/2428736.2428799 |
| Language | English |
| Publisher | Association for Computing Machinery (ACM) |
| Publisher Date | 2012-12-03 |
| Publisher Place | New York |
| Access Restriction | Subscribed |
| Subject Keyword | Information integration Ontology Owl Clinical documents Health information systems Information retrieval |
| Content Type | Text |
| Resource Type | Article |
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