Evaluating the sustainability of a smart technology application to mobile health care: the FGM–ACO–FWA approach

Abstract

a b s t r a c tSmart technologies present numerous opportunities for enhancing mobile health care. However, some concerns regarding the viability of smart technology applications must be addressed. This study investigated these concerns by reviewing the current practices of smart technology applications to mobile health care. As a result, five factors critical to the applicability of a smart technology to mobile health care are identified, and the fuzzy geometric mean-fuzzy analytic hierarchy process (FGM-FAHP) approach is proposed to assess the relative importance levels of the identified factors. The experimental results showed that the three most critical factors identified include: (a) the relaxation of the related medical laws; (b) unobtrusiveness; and (c) the precise need and situation of a user. Accordingly, approximately 44%, 26%, and 15% of the budget should be allocated to the realization of the three critical factors, respectively. In addition, the challenges involved and opportunities for enhancing the effectiveness of existing applications are discussed.

Assessing factors critical to smart technology applications to mobile health care − the fgm-fahp approach

“…They concluded from a questionnaire survey that perceived service availability (PSA) and personal innovativeness in information technology (PIIT) were the most critical factors. Other researchers such as Demirkan [22] , Chen & Chiu [23] , and Chen [17] have similarly encouraged using smart technology applications for mobile environments, all of which has been summarized in Table 2 .…”

“…• Two existing methods, FGM-fuzzy extent analysis (FEA) [57] and the FGM-alpha cut operations (ACO)-FWA method [17] , were also applied to the collected data for comparison. In the FGM-FEA method, experts' judgments were aggregated using FGM.…”

“…These weights provide valuable information to manage activities on the realization of the critical factors under limited resources [16] . In addition, a judgment modification mechanism is also incorporated in the FGM-FAHP approach, so that an expert can modify his/her judgment subjectively if that deviates considerably from the consensus, which was not considered in [17] . Further, experts can be of unequal authority levels, so that the decision-making process can be led by an authoritative professional, which is another novelty of this study when compared to earlier studies [17][18][19] .…”

A Fuzzy Collaborative Approach for Evaluating the Suitability of a Smart Health Practice

“…Chiu and Chen [5] assessed the sustainable effectiveness of the adjustment mechanism of a ubiquitous clinic recommendation system by modelling the improvement in the successful recommendation rate as a learning process. Chen [6] put forward a hybrid methodology combining fuzzy geometric mean 2 of 20 (FGM), alpha-cut operations (ACO), and fuzzy weighted mean (FWM) to evaluate the sustainability of a smart health practice, in which FGM, ACO, and FWM were for aggregation, prioritization, and assessment, respectively. Compared with earlier studies, the FGM-ACO-FWM method was more precise because of the application of the exact solution technique ACO.…”

“…Then, fuzzy intersection (FI) (or the minimum t-norm) is applied to aggregate the fuzzy weights derived by all decision makers. Obviously, the proposed methodology is a posterior-aggregation fuzzy analytic hierarchy process (FAHP) method, while the FGM-ACO-FWM method proposed by Chen [6] is an anterior-aggregation method. The FI results can be used to measure the prior consensus among decision makers.…”