Ontological Approach to Holistic Healthcare Systems Simulation

Introduction

Modeling and Simulation (M&S) related to Healthcare Systems (HSs) has been paid a lot of attention over these last decades. Different Simulation techniques have been used to study HS including Discrete Event Simulation (Gunal & Pidd, 2010), optimization techniques, goal programming (Topaloglu, 2006), and data envelopment analysis or mixed methods (Ahmed & Alkhamis 2009). Merely integrating different simulation variables in the same model to address the multi-aspect challenges that healthcare is facing and allowing a truly holistic systems view is only half the story (Brailsford et al., 2010). Instead, unit specific studies that deal with specific problems have been predominant. Such unit specifics include outpatient clinics, A&E (accident and emergency) departments, and inpatient facilities that are addressed by Ahmed and Alkhamis (2009), and Topaloglu (2006). Common issues that are being addressed include, scheduling and patient flow, sizing and planning of beds, rooms, and staff. However, the authors argue that when studying such complex systems, it is not sufficient to focus on the diverse components or aspects separately. This leaves a big gap of understanding the overall system by decision makers and managers to make a consistent design and analysis of healthcare systems. Using unit specific and facility specific models for the analysis and design of healthcare systems can be misleading (Gunal & Pidd, 2010). Hence, healthcare modelers are confronted with the challenging task of conducting efficient “design and analysis” of HSs due to problems such as diverse sources of knowledge, and lack of agreed upon concepts that make their study more difficult. Therefore, an ontological description of HSs simulation domain is required to alleviate problems such as conceptual design, architecture, verification, validation and interoperation of healthcare processes within the healthcare domain (Turnitsa & Tolk, 2006). The application of ontology to modelling and simulation is still in its infancy and a limited number of investigations have been carried out within the research community (Grolinger et al., 2012).

Prior to this study, ontology-based studies have been proposed to address modelling challenges in large-scale and complex systems, e.g., ontology for discrete event system modeling (DESO) (Guizzardi & Wagner 2010), web-based ontology for discrete event modeling (DeMO)(Miller et al., 2004), ontology to deal with modelling problems such as composability and interoperability (Tolk & Turnitsa 2007), fundamental types of modelling errors - system description errors, and model translation errors- (McGinnis et al., 2011), simulation conceptual model (Balci et al., 2011), and model reuse (Durak et al., 2011). However, the authors argue that most of the research works related to ontology driven simulation focus mainly on ontology mapping from referential ontologies to simulation ontologies resulting in considerable loss of information. The objective of this study is to propose a new ontology assisted modeling for HSs simulation that can be directly implemented in an ontology language such that both humans and machines can process while targeting a representation of HSs as an integrated whole. The major components of HS simulation that are most often studied in isolation are formally captured by various classes of the ontology - refined into subclasses with their definitional characteristics - to address modelling challenges such as common representation of HS models from different simulation platforms, querying simulation models, and browsing.