Big data in healthcare: Conceptual network structure, key challenges and opportunities

Big data is a concept that deals with large or complex data sets by using data analysis tools (e.g., data mining, machine learning) to analyze information extracted from several sources systematically. Big data has attracted wide attention from academia, for example, in supporting patients and health professionals by improving the accuracy of decision-making, diagnosis and disease prediction. This research aimed to perform a Bibliometric Performance and Network Analysis (BPNA) supported by a Scoping Review (SR) to depict the strategic themes, thematic evolution structure, main challenges and opportunities related to the concept of big data applied in the healthcare sector. With this goal in mind, 4857 documents from the Web of Science covering the period between 2009 to June 2020 were analyzed with the support of SciMAT software. The bibliometric performance showed the number of publications and citations over time, scientific productivity and the geographic distribution of publications and research fields. The strategic diagram yielded 20 clusters and their relative importance in terms of centrality and density. The thematic evolution structure presented the most important themes and how it changes over time. Lastly, we presented the main challenges and future opportunities of big data in healthcare.

Automated summary

This study used bibliometric performance and network analysis to examine the strategic themes, thematic evolution structure, main challenges, and opportunities of big data in the healthcare sector. The analysis revealed the significant role of big data in enhancing decision-making, diagnosis, and disease prediction accuracy.