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Clinical Decision Support System for Point of Care Use - Ontology-driven Design and Software Implementation

Journal: Methods of Information in Medicine
Subtitle: A journal stressing, for more than 50 years, the methodology and scientific fundamentals of organizing, representing and analyzing data, information and knowledge in biomedicine and health care
ISSN: 0026-1270
Topic:

Special Topic: BVM 2008 – German Conference on Medical Image Processing
Guest Editors: T. Tolxdorff, T. M. Deserno, H. Handels, H.-P. Meinzer

DOI: http://dx.doi.org/10.3414/ME0574
Issue: 2009 (Vol. 48): Issue 4 2009
Pages: 381-390

Clinical Decision Support System for Point of Care Use - Ontology-driven Design and Software Implementation

K. Farion (1), W. Michalowski (2), S. Wilk (2), D. O´Sullivan (2), S. Rubin (3), D. Weiss (4)

(1) Departments of Pediatrics and Emergency Medicine, University of Ottawa, Children’s Hospital of Eastern Ontario, Ottawa, Canada; (2) MET Research Group, Telfer School of Management, University of Ottawa, Ottawa, Canada; (3) Department of Surgery, University of Ottawa, Children’s Hospital of Eastern Ontario, Ottawa, Canada; (4) Institute of Computing Science, Poznan University of Technology, Poznan, Poland

Keywords

software design, Clinical decision support systems, Point of care systems, ontology-driven design

Summary

Objectives: The objective of this research was to design a clinical decision support system (CDSS) that supports heterogeneous clinical decision problems and runs on multiple computing platforms. Meeting this objective required a novel design to create an extendable and easy to maintain clinical CDSS for point of care support. The proposed solution was evaluated in a proof of concept implementation. Methods: Based on our earlier research with the design of a mobile CDSS for emergency triage we used ontology-driven design to represent essential components of a CDSS. Models of clinical decision problems were derived from the ontology and they were processed into executable applications during runtime. This allowed scaling applications’ functionality to the capabilities of computing platforms. A prototype of the system was implemented using the extended client-server architecture and Web services to distribute the functions of the system and to make it operational in limited connectivity conditions. Results: The proposed design provided a common framework that facilitated development of diversified clinical applications running seamlessly on a variety of computing platforms. It was prototyped for two clinical decision problems and settings (triage of acute pain in the emergency department and postoperative management of radical prostatectomy on the hospital ward) and implemented on two computing platforms – desktop and handheld computers. Conclusions: The requirement of the CDSS heterogeneity was satisfied with ontologydriven design. Processing of application models described with the help of ontological models allowed having a complex system running on multiple computing platforms with different capabilities. Finally, separation of models and runtime components contributed to improved extensibility and maintainability of the system.

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