# Towards the Use of Big Data in Healthcare: A Literature Review
## 1 Introduction  
The adoption of Fourth Industrial Revolution technologies, particularly artificial intelligence (AI) and big data (BD), has been a major challenge for all industries [1]. The increasing technological progress has initiated a digital transformation process in many sectors, including healthcare [2], which is already moving toward Healthcare 4.0 due to the impact of smart technologies [3, 4] such as the Internet of Things (IoT) paradigm [5], cloud and fog computing [5], and big data analytics (BDA) [6].  
Healthcare institutions often face many challenges, ranging from epidemics to determining the most suitable therapies for treating diseases. If an AI technology system is applied to medical research, owing to the development, validation, and deployment of various machine learning algorithms for industrial applications with sustainable performance [7], it has the potential to diagnose, find vaccines, and personalize healthcare services, moving toward highly advanced e-Health [8].  
Patient-centered care cannot ignore the continuous expansion of data in terms of its volume, variety, and velocity, propelling it toward a new technological paradigm, now widely called BD [9, 10]. The analysis of the enormous volume, heterogeneity, and velocity of the information provided by BD allows for the extraction of the greatest value from collected data and successfully solving and analyzing the relationships between different variables that describe a patient's vital functions and that can affect their health [11]. These data stimulate healthcare organizations to invest heavily in data analysis to facilitate decision-making [12, 13]. Integrating data on an individual's unique characteristics, clinical phenotypes, and biological information obtained from diagnostic imaging to laboratorytests and medical records enables precision medicine to operate under predictive and preventive conditions [14]. Having abundant data is crucial, especially in critical care environments, to be able to rapidly identify diagnoses and specific treatments for particular or rare pathological cases [15]. The improvement of critical stages of diagnosis and the personalization of therapeutic treatments for various diseases are spreading rapidly due to the emerging technological development of BD and the use of social media and IoT that allow for collecting various kinds of data generated by a huge number of devices. In particular, these are biomedical sensors and intelligent devices that, during the diagnosis and monitoring of a patient, collect data related to their health and make them accessible through interconnected and integrated systems, facilitating the transmission of information [5; 16].  
Currently, the health emergency situation caused by the spread of the COVID-19 disease is increasing the need to develop a BD information system for epidemic- and rapid problem-oriented BD acquisition and integration [17; 18]. Previous studies examining BD in healthcare predominantly focused on informatics [16; 19; 20] and medical aspects [21; 22]. Few studies have analyzed this topic from a managerial point of view [23; 24]. Therefore, this paper contributes to this stream of literature by exploring which tangible and intangible elements are needed to draw the maximum benefits from BD.  
Scopus has been used to select the most relevant studies on the role of BD in healthcare to generate knowledge even from the most remote contributions to the literature [25]. The study shows exponential growth in publications, especially since 2020, highlighting the growing criticality and urgency of healthcare and Industry 4.0 integration. Given the increasing interest in BD in healthcare management, a literature review is useful to understand the challenges and opportunities of BD's use [22] for future applications in healthcare.