Can We Apply Principles From Social Networking To Healthcare Informatics For Intelligent Data Analytics?

Abstract

Extracting the principles associated with complexity theory and swarm intelligence has offered practical solutions for routing and scheduling. Reality mining and its link with social networking relationships may yield pragmatic ideas applicable to many fields including business services and healthcare analytics. In healthcare, the focus is on the patient and physiological systems generate patient data. Since human physiology is highly integrated and always strives to maintain biologically relevant equilibrium, it follows, naturally, that physiological data and variables are likely to be co-integrated. Because physiological systems always strive to maintain homeostasis, it follows, that the focus of physiology is to attain equilibrium where various interacting components possess information about the functional status of other components. The physiological state, may, therefore, be viewed as a healthcare service system which is amenable to data analytics using the principles of Nash Equilibrium. In sharp contrast, the healthcare industry, like most businesses, suffers from chronic information asymmetry of data and information about its supply chain network. Information asymmetry in the complex and inter-related healthcare network may offer grounds to explore potential benefits to the industry if information asymmetry could be reduced through appropriate acquisition of data including real-time data. Availability of high volume data may improve forecasting in healthcare sectors related both to service and the business. But, while the innovation for service may draw inspiration from information symmetry, the techniques to reduce volatility in the healthcare business must address information asymmetry. In both cases, potential use of advanced econometric tools (generalized autoregressive conditional heteroskedascity or GARCH) may be applicable. The network in healthcare relates both to physiological circuitry and the business relationships. Both networks may be evaluated in the context of the structures of social networking to elucidate if such principles may add value to healthcare analytics.