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A Framework for Detecting Interactions Between Co-Incident Clinical Processes

A Framework for Detecting Interactions Between Co-Incident Clinical Processes

Kerry Hinge, Aditya Ghose, Andrew Miller
Copyright: © 2010 |Volume: 1 |Issue: 2 |Pages: 12
ISSN: 1947-315X|EISSN: 1947-3168|EISBN13: 9781609604363|DOI: 10.4018/jehmc.2010040103
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MLA

Hinge, Kerry, et al. "A Framework for Detecting Interactions Between Co-Incident Clinical Processes." IJEHMC vol.1, no.2 2010: pp.24-35. http://doi.org/10.4018/jehmc.2010040103

APA

Hinge, K., Ghose, A., & Miller, A. (2010). A Framework for Detecting Interactions Between Co-Incident Clinical Processes. International Journal of E-Health and Medical Communications (IJEHMC), 1(2), 24-35. http://doi.org/10.4018/jehmc.2010040103

Chicago

Hinge, Kerry, Aditya Ghose, and Andrew Miller. "A Framework for Detecting Interactions Between Co-Incident Clinical Processes," International Journal of E-Health and Medical Communications (IJEHMC) 1, no.2: 24-35. http://doi.org/10.4018/jehmc.2010040103

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Abstract

The detection of treatment conflicts between multiple treatment protocols that are co-incident is a difficult and open problem that is particularly exacerbated regarding the treatment of multiple medical conditions co-occurring in aged patients. For example, a clinical protocol for prostate cancer treatment requires the administration of androgen-suppressing medication, which may negatively interact with another, co-incident protocol if the same patient were being treated for renal disease via haemodialysis, where androgen-enhancers are frequently administered. These treatment conflicts are subtle and difficult to detect using automated means. Traditional approaches to clinical decision support would require significant clinical knowledge. In this paper, the authors present an alternative approach that relies on encoding treatment protocols via process models (in BPMN) and annotating these models with semantic effect descriptions, which automatically detects conflicts. This paper describes an implemented tool (ProcessSEER) used for semantic effect annotation of a set of 12 cancer trial protocols and depicts the machinery required to detect treatment conflicts. The authors also argue whether the semantic effect annotations of treatment protocols can be leveraged for other tasks.

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