Towards the Use of Big Data in Healthcare: A Literature Review

Abstract

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.

Security risk models against attacks in smart grid using big data and artificial intelligence

“…Following population movements and trends is crucial for early diagnosis and personalized health care, made possible by the data’s diversity, volume, and velocity. All research in this field in 2021 has shown that big data management is crucial to raising the standard of healthcare and patient outcomes ( Dicuonzo et al, 2022 ). AI-based diagnosis-based techniques and algorithms might be used to find outbreaks before they spread.…”

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“…Another problem with big data is that it contains a huge amount of explicit and implicit knowledge that adds great value to healthcare. However, effective search for and use of this knowledge faces many challenges, such as developing healthcare knowledge management systems and possibly supporting them with ML algorithms to derive systematic knowledge of different data at a higher level to support diagnosis and treatment ( Phan et al, 2022 ; Dicuonzo et al, 2022 ).…”

Biobanking in the digital pathology era

“…In this scenario, AI techniques, and more specifically deep learning models, can bring several advantages. Therefore, looking back at the literature, studies underlined the impact of a computational approach to improve patient safety [52], healthcare quality [53], and reduce costs [54]. More in detail, biobanks can report significant improvement in integrating data derived from samples (e.g., -omics data) with medical records, or other types (e.g., pathological data) [48].…”