Clinical Practice Guidelines Formalization for Personalized Medicine

Clinical Practice Guidelines Formalization for Personalized Medicine

Nassim Douali (INSERM U872 Eq 20 & Medical School, Pierre and Marie Curie University, Paris, France) and Marie-Christine Jaulent (INSERM U872 Eq 20 & Medical School, Pierre and Marie Curie University, Paris, France)
Copyright: © 2013 |Pages: 8
DOI: 10.4018/jaec.2013070103
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Clinical guidelines are important means to improve quality of health care while limiting cost and supporting the medical staff. They are written as free text with tables and figures. Transforming them into a formal, computer-processable representation is a difficult task requiring both computer scientist skills and medical knowledge. In this paper the authors describe a CDSS designed to assist physicians for personalized care, and methodology for integration in the clinical workflow. A reasoning method for interacting heterogeneous knowledge and data is a necessity in the context of personalized medicine to achieve its potential and improve the quality, safety and efficiency of healthcare.
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2. Background

2.1. Genomic and Personalized Medicine Challenges

Despite the great promise of the clinical use of genomics, many obstacles lie in the way of integrating genomic and personalized medicine into routine clinical care. These challenges include the need for greater oversight and quality assurance of genetic testing (Phillips et al., 2001). There are limited availability of rigorous, prospectively collected evidence on the clinical value and cost effectiveness of genetic and genomic assays (Khoury, Gwinn, Yoon, Dowling, Moore, & Bradley, 2007; Khoury et al., 2007). That concern among pharmaceutical companies that the tailoring of pharmaceuticals to genetic sub-populations may segment the marketplace and reduce the number of patients for whom a given medication is indicated and prescribed (Ginsburg, Konstance, Allsbrook, & Schulman, 2005). More over the limited knowledge of, and comfort with, genetics and genomics among health-care professionals (Kawamoto, Lobach, Willard, & Ginsburg, n.d.).

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