# Towards the Use of Big Data in Healthcare: A Literature Review
## 2 Background  
BD is a large collection of data from various healthcare sources that enables increasingly personalized treatments, evaluations of their effectiveness, and a reduction of clinical risk through innovative ways of managing and controlling processes [26; 27; 28]. The literature identifies the main features that characterize BD, also known as the 3 Vs [28; 29]:  
* Volume: amount of data generated every second;
* Variety: different types of data generated, accumulated, and used, even unstructured or semi-structured; and
* Velocity: referring to the generation of data (which is always increasing). To these first 3 Vs, another 3 Vs were later added [30; 31]:
* Veracity or uncertainty of data;
* Value: BD analytics technologies increase the value of data by transforming it into useful information; and
* Variability: data on the same topic can have differences related to their format or mode of collection, and this is often a limitation. Then a seventh additional characteristic was found:  
* Complexity: the larger the size of the dataset, the greater the complexity of the data to be managed. Although the importance of BD has emerged particularly in finance, banking, and insurance, one of the most promising and interesting areas in which it can effect significant change is healthcare, although its adoption has been slow [32; 33]. Global BD in the healthcare market was expected to grow at a CAGR (Compounded Average Growth Rate) of 20.69% between 2015 and 2021 [34]. BD is transforming the way healthcare is managed, enabling a revolution in knowledge management and data analytics [35]. The analysis of the large amount of data generated by a single patient related to diagnosis, treatment pathways, drugs, medical devices, digital images, and laboratory analysis results, to be meaningful, requires that these data be validated, processed, and integrated into processingsystems that allow for creating new value in the organization of health services [36]. The data, therefore, are stored in databases and are efficiently managed [9; 37], providing useful insights that otherwise would not have been possible and identifying better solutions in terms of health quality and timely decisions [23; 38]. The increasing influence of BD has prompted healthcare organizations to use AI and the skills needed to effectively exploit BDA [1; 24]. The quantity of data to manage, analyze, and archive is so large and complex that traditional methods of data processing are inadequate [39]. The potential acquisition and analysis of BD, in fact, requires the restructuring of the technological infrastructure and integration of traditional data analytical tools and techniques with computational technology that is able to enhance and extract information that will be useful for decision-making [2; 36; 40]. The most relevant sources from which to acquire BD in healthcare are medical recordings (e.g., electronic healthcare records, clinical decision support systems, biomedical data, etc.) [41] as well as external data sources (laboratories, pharmacies, patient-reported data, biometric and other data received directly from patients, etc.). Additional data sources are increasingly available, such us data derived from Internet use (social media) and smart applications [42; 43; 44; 45]. For the management and processing of these data, many healthcare sectors have adopted cloud computing. It is a solution for receiving and storing huge amounts of patient data and managing electronic medical records [46]. These heterogeneous data, when properly integrated with the most relevant health data, allow for the monitoring of patients' health status in various contexts (hospitals, nursing homes, private homes) [5; 16; 47]. This is an aspect of crucial relevance because the main errors that can lead to a misdiagnosis and case fatality occur due to improper monitoring and administration of therapeutic treatment, as well as to drug non-adherence [48].