Opportunities and Challenges in the Use of Big Data in Healthcare: A Literature Review

Digitalisation and the use of technology are pushing the spread of new business models and improving the efficiency of processes. The demand for innovative and revolutionary applications is increasing, along with the use of big data (BD). The proliferation of large quantities of data is receiving considerable attention in all sectors due to the possibility of using these data in decision-making processes. In the healthcare sector, the role of BD is prominent, especially regarding patient diagnostics, fast epidemic recognition and patient management improvement. To ensure personalised care, the health system must transform individual medical services into electronic forms and favour complete and systemic automation based on the advanced technologies of Industry 4.0. This paper consists of a systematic literature review of the use of BD in the healthcare sector, focusing on the opportunities and challenges. To this end, we selected articles from the Scopus and Web of Science databases. Providing a deep understanding of the state of the art, this paper aims to reveal the implications of the use of BD and offer valuable insights to address future research and identify emerging issues. Keywords: big data, healthcare, digitalisation, internet of things, artificial intelligenc

Towards the Use of Big Data in Healthcare: a literature review

The interest in new and more advanced technological solutions is paving the way for the diffusion of innovative and revolutionary applications in healthcare organizations. The application of an artificial intelligence system to medical research has the potential to move toward highly advanced e-Health. This analysis aims to explore the main areas of application of big data in healthcare, as well as the restructuring of the technological infrastructure and the integration of traditional data analytical tools and techniques with an elaborate computational technology that is able to enhance and extract useful information for decision-making. We conducted a literature review using the Scopus database over the period 2010-2020. The article selection process involved five steps: the planning and identification of studies, the evaluation of articles, the extraction of results, the summary, and the dissemination of the audit results. We included 93 documents. Our results suggest that effective and patient-centered care cannot disregard the acquisition, management, and analysis of a huge volume and variety of health data. In this way, an immediate and more effective diagnosis could be possible while maximizing healthcare resources. Deriving the benefits associated with digitization and technological innovation, however, requires the restructuring of traditional operational and strategic processes, and the acquisition of new skills

An Analytic and Systemic View of the Digital Transformation of Healthcare

Industry 4.0 represents a digital revolution that is driven by technologies that blur the lines between the physical and digital worlds. Industry 4.0, the latest industrial revolution, is poised to have a profound impact on all aspects of society. In order to understand how the healthcare industry is being transformed by the convergence of the physical and digital realms, a systems perspective is taken in this study. Two research questions are addressed regarding the opportunities and interventions that can be provided by both analytical and systems conceptions of digital transformation. I use a systemic literature review approach to address the research questions. A sample of studies between 2000 and 2022 is analyzed. Existing studies mostly examine the effects of new digital technologies on healthcare providers. However, digital transformation also presents significant challenges, such as data privacy, ethical concerns related to AI-based automated decision-making, and equity issues related to e-health. Solutions to major challenges at both micro and macro levels can be derived from the existing theories and tools of systems thinking. For instance, systems thinking\u27s continuous learning and adaptation capabilities can be useful for healthcare organizations to develop the required digital capabilities. Furthermore, the interconnectedness of subsystems and stakeholders in systems thinking can be combined with digital twin technology to investigate the dynamic interactions among key stakeholders, leading to the development of new regulatory policies

Big Data Compliance for Innovative Clinical Models

In the healthcare sector, information is the most important aspect, and the human body in particular is the major source of data production: as a result, the new challenge for world healthcare is to take advantage of these huge amounts of data de-structured among themselves. In order to benefit from this advantage, technology offers a solution called Big Data Analysis that allows the management of large amounts of data of a different nature and coming from different sources of a “computerized” healthcare, as there are considerable changes made by the input of digital technology in all major health areas. Clinical intelligence consists of all the analytical methods made possible through the use of computer tools, in all the processes and disciplines of extraction and transformation of crude clinical data into significant insights, new purposes and knowledge that provide greater clinical efficacy and best health pronouncements about past performance, current operations and future events. It can therefore be stated that clinical intelligence, through patient data analysis, will become a standard operating procedure that will address all aspects of care delivery. The purpose of this paper is to present clinical intelligence approaches through Data Mining and Process Mining, showing the differences between these two methodologies applied to perform “real process” extraction to be compared with the procedures in the corporate compliance template (the so called “Model 231”) by “conformance checking”