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An Ontology-Driven Approach to Clinical Evidence Modelling Implementing Clinical Prediction Rules
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Author(s): Derek Corrigan (Royal College of Surgeons in Ireland, Ireland), Lucy Hederman (Trinity College Dublin, Ireland), Haseeb Khan (Trinity College Dublin, Ireland), Adel Taweel (Kings College London, UK), Olga Kostopoulou (Kings College London, UK)and Brendan Delaney (Kings College London, UK)
Copyright: 2013
Pages: 28
Source title:
E-Health Technologies and Improving Patient Safety: Exploring Organizational Factors
Source Author(s)/Editor(s): Anastasius Moumtzoglou (Hellenic Society for Quality & Safety in Healthcare and P. & A. Kyriakou Children's Hospital, Greece)and Anastasia N. Kastania (Athens University of Economics and Business, Greece)
DOI: 10.4018/978-1-4666-2657-7.ch016
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Abstract
Diagnostic error is a major threat to patient safety in the context of the primary care setting. Evidence-based medicine has been advocated as one part of a solution. The ability to effectively apply evidence-based medicine implies the use of information systems by providing efficient access to the latest peer-reviewed evidence-based information sources. A fundamental challenge in applying information technology to a diagnostic clinical domain is how to formally represent known clinical knowledge as part of an underlying evidence repository. Clinical prediction rules (CPRs) can provide the basis for a formal representation of knowledge. The TRANSFoRm project defines the architectural components required to deliver a solution by providing an ontology driven clinical evidence service to support provision of diagnostic tools, designed to be maintained and updated from electronic sources of research data, to assist primary care clinicians during the patient consultation through delivery of up to date evidence based diagnostic rules.
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