Abstract
The adoption of Electronic Healthcare Records (EHRs) holds the key for the success of next generation intelligent healthcare systems to improve the quality of healthcare and patient safety by facilitating the exchange of critical patient’s episodic information among different stakeholders. The primary and secondary care healthcare systems store the episodic information for future reuse and for auditing purposes. The conventional healthcare information management systems for primary and secondary care are expected to be able to communicate and exchange complex medical knowledge (often expressed in numerous languages in different parts of the world) in an efficient and unequivocal way. For the purpose of this research, we present a novel technique to transform conventional patients’ data into OWL-based Electronic Healthcare Records (EHRs) which addresses the issues of interoperability, flexibility, and scalability through the utilization of ontology inspired framework. Using ontologies is a cost effective and pragmatic solution to implementing a shift from simple patient interviewing systems to more intelligent systems in the primary and secondary care. The Patient Semantic Profile specifically developed for generating EHRs has been validated using a sample of real patients’ data acquired from the Raigmore Hospital’s RACPC (Rapid Access Chest Pain Clinic).
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References
Turley, M., et al.: Use Of Electronic Health Records Can Improve The Health Care Industry’s Environmental Footprint. Health Affairs 30, 938 (2011)
Bouamrane, M.-M., Rector, A., Hurrell, M.: Using Ontologies for an Intelligent Patient Modelling, Adaptation and Management System. In: Meersman, R., Tari, Z. (eds.) OTM 2008. LNCS, vol. 5332, pp. 1458–1470. Springer, Heidelberg (2008)
Spackman, K.A., Reynoso, G.: Examining SNOMED from the perspective of formal ontological principles: Some preliminary analysis and observations. In: Proc. KR-MED 2004, Whistler, Canada, pp. 81–87 (2004)
Farooq, K., et al.: Ontology-driven cardiovascular decision support system. In: 2011 5th International Conference on Pervasive Computing Technologies for Healthcare (PervasiveHealth), pp. 283–286 (2011)
J.T. Case, et al.: Use of SNOMED in HL7 Messaging, pp. 1–22 (2008)
Cornet, R., De Keizer, N.: Forty years of SNOMED: a literature review. BMC Medical Informatics and Decision Making 8, S2 (2008)
Bouamrane, M.-M., Rector, A.L., Hurrell, M.: Ontology-Driven Adaptive Medical Information Collection System. In: An, A., Matwin, S., Raś, Z.W., Ślęzak, D. (eds.) ISMIS 2008. LNCS (LNAI), vol. 4994, pp. 574–584. Springer, Heidelberg (2008)
Abidi, S.R., et al.: Ontology-based Modeling of Clinical Practice Guidelines: A Clinical Decision Support System for Breast Cancer Follow-up Interventions at Primary Care Settings Computerization of BC Follow-up CPG Development of Breast Cancer Ontology The BC ontology model. Computer
Cambria, E., et al.: Bridging the Gap between Structured and Unstructured Health-Care Data through Semantics and Sentics. Science, 1–14 (2010)
Cambria, E., Hussain, A.: Sentic Computing: Techniques, Tools, and Applications. In: SpringerBriefs in Cognitive Computation. Springer, Heidelberg (2012)
Cambria, E., et al.: Sentic PROMs: Application of sentic computing to the development of a novel unified framework for measuring health-care quality. Expert Systems with Applications
Abidi, S.: Ontology-based knowledge modeling to provide decision support for comorbid diseases. Knowledge Representation for Health-Care, 27–39 (2011)
Horridge, M., et al.: A Practical Guide To Building OWL Ontologies Using The Protégé-OWL Plugin and CO-ODE Tools Edition 1.0, The University Of Manchester, vol. 27 (August 2004)
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Farooq, K., Hussain, A., Leslie, S., Eckl, C., MacRae, C., Slack, W. (2012). Semantically Inspired Electronic Healthcare Records. In: Zhang, H., Hussain, A., Liu, D., Wang, Z. (eds) Advances in Brain Inspired Cognitive Systems. BICS 2012. Lecture Notes in Computer Science(), vol 7366. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31561-9_5
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DOI: https://doi.org/10.1007/978-3-642-31561-9_5
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