The COVID-19 pandemic has dramatically transformed healthcare systems globally, therefore improving health information technology sector. From the moment that pandemic has broken out, the use of information and communication technologies (ICT) has become absolutely necessary for the continuation of healthcare services. Furthermore, data digitization has enabled the extraction of meaningful insights through big data analytics. E-Health, encompassing a wide range of ICTs used in healthcare, has become a critical component in addressing the challenges posed by the pandemic. The main goal of this paper is to examine the impact of COVID-19 on health information technology and explores the rapid growth of e-Health and big data-driven innovation in healthcare processes. Through the analysis of the major tools, techniques, and innovative processes that have emerged in response to the pandemic, this paper has the aim of highlight their potential to improve system efficiency and enhance citizen health. We explore the current state of e-Health and big data in healthcare and discuss the future implications of these technologies for the sector. Our analysis underscores the need for continued investments in health information technology and highlights the role of policymakers, healthcare providers, and researchers in fostering innovation and driving positive change in the sector.

In most decision-making scenarios, the decision relies upon a multiplicity of factors/criteria which need to be selected and prioritized counting on the knowledge of a group of experts, with the aim of reaching a global decision. Through judgment aggregation models we seek a rational mechanism to reconcile individual judgments into a collective prioritization of requirements. In this paper we present a fuzzy multi-criteria group decision-making model, and experiment its application in credit rating determination, applying Majority Judgment as a method to consolidate the evaluations of a bank's board of direction into a shared and rational decision on SME rating.

The coronavirus disease pandemic which broke out in 2019 heavily affected world wide health systems which found themselves unprepared for managing this wide-ranging event and were therefore forced to reorganize and review the entire system management. In this scenario, the use of information technology in health has played a fundamental role; in fact, e-Health, which indicates all the information and communication technologies (ICT) supporting the health system, has allowed not only the continuation of activities otherwise not executable, but has also left a strong legacy both to the health system in terms of improvements in efficiency and effectiveness and towards the health of the citizen. Covid-19 has pushed towards a rapid growth of e-Health by modifying the operation of the health system, deeply changing the vision of management processes, and favoring data digitization from which meaningful information and insights can be extracted. The aim of this article is to analyze the impact of Covid on the acceleration of the digital transformation of the healthcare system, the innovative processes, the tools used and the best adapted Big Data techniques.

Making decisions quickly and efficiently is essential in all areas of existence; when the decision concerns a fair number of voters and options to vote, it is sometimes appropriate to choose a voting system that represents the voting population well, without excluding the preferences of minorities. In this paper we present a voting system that aims to be an enhancement of Majority Judgment through unsupervised machine learning techniques, in particular the cluster and, in addition, a criterion for obtaining a multiwinner result has also been added. After exposing its functioning, a case study is presented to test its applicability, which leads to multiple fields of interest and it is not limited exclusively to purely political occasions.