Multi-scale modeling and simulation of complex systems: Opportunities and challenges. Issue 2 (2nd April 2016)

• Record Type: Journal Article
• Authors: Ravitz, Alan; Mazzuchi, Thomas A.; Sarkani, Shahram
• Is Part Of: IIE transactions on healthcare systems engineering. Volume 6:Issue 2(2016)
• Page Start: 79
• Page End: 95
• Publication Date: 2016-04-02
• Subjects: agent-based simulation -- discrete event simulation -- systems dynamics -- multi-scale modeling -- stroke care; Biomedical engineering -- Periodicals; Medical informatics -- Periodicals; Medical care -- Periodicals; Biomedical engineering; Medical care; Medical informatics; Periodicals; 610.28
• DOI: 10.1080/19488300.2016.1156201
• Languages: English
• ISSNs: 1948-8300

Abstract

ABSTRACT: Healthcare, like other industries, large corporations, and institutions, is a complex system composed of many diverse interacting components. Frequently, to improve performance of a system, to move into new markets, or to expand capability or capacity, healthcare decision makers face opportunities or mandates to implement innovations (new technology, processes, and services). Successful implementation of these innovations involves seamless integration with the policy, economic, social, and technological dynamics associated with the complex system. These dynamics are frequently difficult for decision makers to observe and understand. Consequently, they take on risk from lack of insight into how best to implement the innovation and how their system-of-interest will ultimately perform. This research defines a modeling and simulation framework that provides decision makers with prospective insight into the likely performance to expect once an innovation is implemented in a complex system. We describe the need for such a framework when modeling complex systems, and we discuss suitable simulation paradigms and the challenges related to implementing these simulations. We focus on a specific example in the healthcare field to demonstrate the framework's application and utility in understanding how an innovation, once fielded, will actually affect the larger complex system to which it belongs.