Using Ontologies in Drug Prescription: The SemMed Approach

Using Ontologies in Drug Prescription: The SemMed Approach

Alejandro Rodríguez-González (Universidad Carlos III de Madrid, Spain), Ángel García-Crespo (Universidad Carlos III de Madrid, Spain), Ricardo Colomo-Palacios (Østfold University College, Norway), Juan Miguel Gómez-Berbís (Universidad Carlos III de Madrid, Spain) and Enrique Jiménez-Domingo (Universidad Carlos III de Madrid, Spain)
Copyright: © 2011 |Pages: 15
DOI: 10.4018/ijkbo.2011100101
OnDemand PDF Download:
$30.00
List Price: $37.50

Abstract

Medical prescription has been touted as following an accurate approach to addressing particular health problems. However, the importance of the process might demand considering a formal knowledge-driven procedure to ensure its correctness which can be achieved through Medical Decision Support Systems (MDSS). Semantic Technologies have emerged as a potential silver bullet to become the backbone of those particular Information Systems since it provides seamless integration and an underlying logical formalism. This paper sheds light into using ontologies for drug prescription through the SemMed model, architecture and proof-of-concept implementation, being able to face challenges in these areas and solve day-to-day problems of health professionals in terms of drug prescription
Article Preview

Introduction

Medication prescription plays a central role in healthcare. Nevertheless, medication is not always prescribed effectively (Martens et al., 2007). Apart from higher costs, a number of different risks concerning patient health have been raised. Medication-oriented errors are usually the result of failures during the medication process (Eslami, de Keizer, & Abu-Hanna, 2008). Errors can occur in any step of this process: taking history, ordering, pharmacy management, administration management or surveillance (Kilbridge & Classen, 2001). Prescription errors are often associated with poor health information (Bates et al., 2001). In order to reduce the risks caused by human factors, alert functions are set up in the prescription systems to remind doctors to check the related information (Lai et al., 2007). Therefore, taking into account that Information Technology enables us to do things in a better way (Fazlollahtabar, 2008), computer-based reminders proved to be effective in influencing doctor behavior in medication management (Bennett & Glasziou, 2003). On the other hand, the inability of the average physician to memorize the ever increasing number of drugs, treatment regiments and side effects can be also a source of prescription problems (Dean et al., 2002). In this scenario, computer-based Decision Support Systems (DSS) provide advice to care professionals based on guidelines can solve some of the problems related to drug prescription, among others (e.g., Goud, Hasman, & Peek, 2008; Garg et al., 2005). The development of Decision Support Systems (DSS, for short) is increasingly important in primary care for prescribing, performance measures, cost control and quality of care (Ruland & Bakken, 2002). Thus, DSS systems related to medicine are gaining importance in the literature (e.g., German, Leibowitz, & Shahar, 2009; Jerbi & Kamoun, 2009; Vich, Gomez, & Carnero, 2009; Zhuang et al., 2009), to cite just the most recent and relevant ones.

Semantic Technologies have been pointed out as the future of Web (Benjamins et al., 2008) and a new way to support knowledge (Vossen et al., 2007; Fensel & Musen, 2001) in a wide range of domains (Lytras & García, 2008), including medicine. Semantic Technologies, based on ontologies (Fensel, 2002), provide a common framework that enables for data integration, sharing and reuse from multiple sources.

Complete Article List

Search this Journal:
Reset
Open Access Articles: Forthcoming
Volume 8: 4 Issues (2018): 1 Released, 3 Forthcoming
Volume 7: 4 Issues (2017)
Volume 6: 4 Issues (2016)
Volume 5: 4 Issues (2015)
Volume 4: 4 Issues (2014)
Volume 3: 4 Issues (2013)
Volume 2: 4 Issues (2012)
Volume 1: 4 Issues (2011)
View Complete Journal Contents Listing