C. Decision Making and the Role of Operations Research The majority of researchers focusing on applications of fuzzy set theory in healthcare and medical problems used some existing decision-making processes or derived new ones. They found that the decisions of caregivers primarily aim to lower the health risk of patients while maximizing the health benefits and patients’ choice, thereby increasing the satisfaction of all parties. However, there involved numerous criteria, such as social, environmental, material, managerial, professional, and many more criteria, in the wider setting of medical and healthcare models [17]. Since the crisp decision-making methods under several qualitative and quantitative contradictory issues strived to avoid the complexities with tolerance to doubts and stakeholders’ favoritism, the fuzzy set theory was employed to represent the inherent impreciseness of data and thus to present an efficient, rational and explicit decision process [21].
Among recent studies, [16] presented a detailed survey by considering 142 articles published in the period 2000–2014. While they found the maximum number of publications focusing on applications of operations research in healthcare around the year 2008, they noted a surge in numbers post 2014. In the same edited volume of 2017, they presented a comprehensive survey in this area. They considered a longer period: 1966–2016 to study the advancements of this domain. By considering some relevant recent papers under each class of consideration, their analysis categorized the various approaches and methods applied in healthcare research. [13] provided a thorough analysis of the applications of decision-making and fuzzy set theory to solve health-related problems in a widely admired article. In the period from 1989 to 2018, their statistical findings ranked the year 2012 as first among the acclaimed papers. They also found that the various AHP and hybrid approach approaches were commonly used to rate different service quality applications in the healthcare industry.
Again, [34] shared the applications of operations research in healthcare supply chain management under ambiguity have been vividly demonstrated. By fuzzy set and probability theories, they represented the uncertainty in results, both, and thus could deliver the right medication to the right people at the right time and in good condition to combat the disease. Next, [17] posed an important question as to whether, by proper examination, hospitals could incorporate lean thought. First, various lean concepts and components implemented in healthcare institutions were defined. Next for healthcare organizations, a fuzzy-logic based lean implementation evaluation approach was deployed and then numerically studied. Although this study was validated in only one Indian hospital, it introduced some of the legislators’ futuristic and implementable action plans. The study [44] developed a model to measure the leanness of hospitals and then validated the model by discussing the corresponding initial version with select academic experts. This way, they determined two criteria for organizations, namely the ability to participate in the study, and the commitment to implement lean principles. Finally, a multi-attributes fuzzy logic-based ranking method was established to present the leanness index.
Recently, [18] performed the identification of enablers, criteria, and attributes of leanness to constitute the measures of assessment of hospitals under fuzzy environment. Their method could help to provide the measures to address the weaker attributes and thereby to further enable the enhancement of lean performance.
In a rather real-life-oriented study [19], Pythagorean fuzzy data were considered, in which different evaluation data were provided in the form of Pythagorean fuzzy decision matrices regarding the feasible alternatives. The entries were taken from the views of experts and were described by fuzzy numbers from Pythagoras Table 2.2. In order to solve the resulting MCDM problems under uncertainty, they also broadened the application of the classical TOPSIS system. The most appropriate location and priority setting for buying the best healthcare technology could be decided by this process.
In another fresh-taste study [20], the emphasis was on a muchdiscussed issue of workplace hazards, including protection and effectiveness of health workers against public abuse. To define and prioritize control measures of aggression, their innovative approach used fuzzy AHP and Fuzzy Additive Ratio Assessment. They described the solution as the best advice for controlling violence against health workers by increasing the number of security personnel and training staff.
Below, Part C of Table 2.1 presents some very recent related articles published in highly acclaimed journals. This way, above deliberations, find ample scopes of research on applications of fuzzy set theory on the health-care and medicine problems.
2.7 Conclusion
The IoT is a great blended domain for many fields such as mHealth application’s development. The mHealth application’s development is very trendy topic among the research community due to its direct involvement with the human’s life. These applications mostly focus on static patients but do not focus on the remote patient’s monitoring. The remote patient’s monitoring is getting fame due to fewer innovations and work is done in this domain. In this chapter we investigated different health issues. Additionally, the fuzzy logics work with a focus on their major components of the applications to develop for health monitoring is discussed. There is a strong need to address these all mentioned issues sot enhance the health sector both in eHealth and mHealth Environments.
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