# Towards the Use of Big Data in Healthcare: A Literature Review
## 5 Discussion and Conclusions
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Refs.** & **Author** & **Title** & **Cited** \\ \hline
[23] & [PERSON]; [PERSON] (2017) & Exploring the path to big data analytics success in healthcare. & 340 \\ \hline
[124] & [PERSON] (2014) & Big data investment, skills, and firm value. & 173 \\ \hline
[120] & [PERSON] [PERSON]; [PERSON]; [PERSON]; [PERSON] (2018) & Human resources for Big Data professions: A systematic classification of job roles and required skill sets. & 118 \\ \hline
[123] & [PERSON]; [PERSON] (2015) & Business Analytics Curriculum for Undergraduate Majors. & 92 \\ \hline
[62] & [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] (2019) & Diagnosis of [PERSON]’s disease using modified grey wolf optimization. & 90 \\ \hline
[72] & [PERSON]; [PERSON]; [PERSON]; [PERSON] (2019) & Leveraging Big Data Analytics to Improve Quality of Care in Healthcare Organizations: A Configurational Perspective. & 75 \\ \hline
[118] & [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] (2021) & Value that matters: intellectual capital and big data to assess performance in healthcare. An empirical analysis on the European context. & 13 \\ \hline
[119] & [PERSON]; [PERSON] (2022) & Translating the Machine: Skills that Human Clinicians Must Develop in the Era of Artificial Intelligence. & 3 \\ \hline
[117] & [PERSON]; [PERSON](2022) & The impact of artificial intelligence on skills at work in Denmark. & 3 \\ \hline
[115] & [PERSON]; [PERSON]; [PERSON] (2019) & Disease prediction by using deep learning based on patient treatment history. & 1 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Top ten articles per citations on theme 3: Big Data and analytical skills. ment plans aimed at channeling the flow of BD into healthcare. Indeed, the collection and use of health-related data have been indispensable tools in the effort to counter and contain the pandemic [17]. Moreover, the evolution of technologies and the competitive environment require the development of new skills in the field of data analysis. In the Fourth Industrial Revolution, people continue to be the most strategic and important component in the business, and it is becoming increasingly strategic to be able to acquire analytical skills to analyze and transform consolidated data from existing fragmented data sources into valuable information for business decision-makers. In this way, it is possible to gain a competitive advantage through timely and more informed decisions based on adequate knowledge of descriptive analytics and predictive analytics, analytical techniques that are ideal for analyzing a large proportion of text-based health documents and other unstructured clinical data [135]. In conclusion, personalization of care, reduction of hospitalization, and effectiveness and cost containment of services and waiting lists are benefits unquestionably linked to digitalization and technological innovation but that require a review of the systems of traceability and control with a revolution of traditional ICT systems. This is the challenge that healthcare must overcome. In fact, over the years, analyzing these data and sharing the results with managers and healthcare operators has made it possible to improve the level of knowledge of the system, the sustainability of the healthcare system, its accountability and transparency, and the quality and equity of care. Our work has theoretical and practical implications. From the theoretical perspective, the paper, by proposing a literature review with a strong focus on managerial aspects, extends the literature by enriching a growing field of research. From the practical perspective, the paper reveals the need to develop new skills and redesign operational and strategic processes to consciously use heterogeneous data in future scenarios. This paper presents several limitations. First, we used a defined set of keywords and only one database.