Cloud vendor selection for the healthcare industry using a big data-driven decision model with probabilistic linguistic information

Abstract

The role of cloud services in the data-intensive industry is indispensable. Cision recently reported that the cloud market would grow to 55 billion USD, with an active contribution of the cloud to healthcare around 2025. Inspired by the report, cloud vendors expand their market and the quality of services to seek growth globally. The rapid growth of the cloud sector in the healthcare industry imposes a challenge: making a rational choice of a cloud vendor (CV) out of a diverse set of vendors. Typically, the healthcare industry 4.0 sees the issue as a large-scale group decision-making problem. Previous studies on a CV selection face certain challenges, such as (i) a lack of the ability to handle multiple users’ views, as well as experts’/users’ complex linguistic views; (ii) the confidence level associated with a view is not considered; (iii) the transformation of multiple users’ views into holistic data is lacking; and (iv) the systematic prioritization of CVs with minimum human intervention is a crucial task. Motivated by these challenges and circumventing them, a new big data-driven decision model is put forward in this paper. Initially, the data in the form of complex expressions are collected from multiple cloud users and are further transformed into a holistic decision matrix by adopting probabilistic linguistic information (PLI). PLI represents complex linguistic expressions along with the associated confidence levels. Later, a holistic decision matrix is formed with the missing values imputed by proposing an imputation algorithm. Furthermore, the criteria weights are determined by using a newly proposed mathematical model and partial information. Finally, the evaluation based on the distance from average solution (EDAS) approach is extended to PLI for the rational ranking of CVs. A real-time example of a CV selection for a healthcare center in India is exemplified so as to demonstrate the usefulness of the model, and the comparison reveals the merits and limitations of the model.

A Big Data Stream-Driven Risk Recognition Approach for Hospital Accounting Management Systems

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Dual Probabilistic Linguistic Full Consistency Additive Ratio Assessment Model for Medical Equipment Supplier Selection

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Selection of Suitable Cloud Vendors for Health Centre: A Personalized Decision Framework with Fermatean Fuzzy Set, LOPCOW, and CoCoSo

“…The usage of cloud computing by healthcare organizations is subtle in India compared to other countries like the USA and China. As of 2022, it was determined that only 400 out of 62,000 hospitals in India actively utilized cloud computing to collect, organize, and manage data (Krishankumar et al, 2022b). Although India has become well-advanced in using cloud services in the healthcare industry, there must be a gap in how the data are effectively organized, stored, and managed.…”

“…MCDM is a promising concept that can effectively consider multiple competing/conflicting criteria for rating different alternatives (CVs) based on pre-defined preference scales from a group of experts. Since then, researchers, including Sharma and Sehrawat (2020) and Krishankumar et al (2022b), have developed interesting MCDM solutions for selecting CVs in the healthcare industry. According to this research, it can be inferred that existing CV selection approaches (i) may inadequately handle uncertainty, (ii) may overlook expert hesitation and uncertainty, (iii) struggle with extreme value hesitancy in criteria, and (iv) may neglect the importance of compromise solutions in balancing conflicting criteria.…”