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Top1. Introduction
The World Health Organization (WHO) provides a systemic view of health from the perspective of systems theory (Nutbeam, 1986). This introduces the distinct input, processing, and output system components that determine the health status of an individual. Health status corresponds to an open, causal, and time-varying system that responds to external stimuli, occurring events, and time-sensitive problems. The system is responsible for the decision-making procedures that lead to problem solutions, promoting the survival of the organism. Thus, the terms “decision-making” and “problem-solving” are interchangeably used in the relative literature (Turban et al., 2005).
Systems that support the health status within a ubiquitous computing environment (Sarivougioukas et al., 2015) produce, control, adapt, and improve their decisions using positive (forward) or negative feedback and buffering mechanisms. Such looping mechanisms require the assistance of sensors and actuators that implement the organizational decisions. The rationality of the applying constraints through the looping mechanisms bound the system’s operation in order to achieve the desired behavior (Simon, 1972).
The performance of an open system is measured according to its effectiveness, i.e., the degree of achievement of its objectives. Additionally, the achievement of the objective in the right way represents its efficiency (Turban et al., 2005). The quantified effectiveness and efficiency also determine a quantification of the quality provided by the employed system.
The decision-making that affects the system’s performance involves four main phases (Turban et al., 2005). First, the intelligence phase defines the problem upon which a decision must be made. Second, the model phase determines the modeling solutions that properly suit the problem at hand. Third, the choice phase is about the selection of a (sub-) optimal solution that satisfies the needs according to predefined criteria. Finally, the fourth phase is about the execution of the decisions taken in the previous steps. An additional fifth phase(Turban et al., 2005) refers to a monitoring mechanism that plays the role of controller, observing the efficiency and effectiveness of the decisions and applying combinations of the aforementioned looping mechanisms.
By the term UbiHealth (Sarivougioukas et al., 2018) it is employed for the description of the ubiquitous nature of healthcare services and support. In particular, Home UbiHealth (Sarivougioukas & Vagelatos, 2020) refers to the provision of healthcare services at home supporting the largest part of the population which is about the healthy people. Unfortunately, there is limited support from the medical professionals while people are at home and where a number of decisions must be made related to the health status. In a Home UbiHealth environment (Sarivougioukas et al., 2016) the supported people need to make decisions based upon very specialized medical knowledge. Thus, there must be designed systems to support people at home using specialized knowledge which can be provided through internet. The Home UbiHealth model supporting people at home requires a continuous flow of knowledge to support the decision making process at home.
The present work provides a formal description of a decision-support system model for Home UbiHealth applications using Denotational Mathematics (Wang, 2008). The system also supports knowledge flow. The design provides the means to either integrate or couple the system with other systems through properly designed interfaces. Also, it facilitates software engineering principles to address its inherent complexity.