Big data analytics has the capability to examine a diverse set of intricate data and generate valuable information that would otherwise be unobtainable. Due to this, big data analytics must be integrated into the health industry. The human capacity to process this data is limited, making effective decision support necessary. Thus, big data in the field of health is an important issue, not only because of its enormous volume but also because of its diversity and how quickly it can be managed. Further, its diverse and dynamic nature makes it challenging to extract valuable insights through the use of traditional analytical methods. Much of this available and particularly valuable data are in a semi-structured or unstructured form. The health sector has always generated a large amount of data due to the increased record-keeping needs in the context of patient care. Through this study, healthcare organizations and institutions considering the use of big data analytics technology, as well as those already using it, can gain a thorough and comprehensive understanding of the potential use, effective targeting, and expected impact. Finally, we propose a general strategy for medical organizations looking to adopt or leverage big data analytics. In addition, a particular focus is placed on the latest research work that addresses big data analysis in the health domain, as well as the technical and organizational challenges that have been discussed. We will explain the features of health data, its particularities, and the tools available to use it. In this paper, we will explore how big data can be applied to the field of digital health. Finally, there are significant advantages in saving resources and reallocating them to increase productivity and rationalization. Healthcare professionals have gained from the integration of big data in many ways, including new tools for decision support, improved clinical research methodologies, treatment efficacy, and personalized care. Medicine is constantly generating new imaging data, including data from basic research, clinical research, and epidemiology, from health administration and insurance organizations, public health services, and non-conventional data sources such as social media, Internet applications, etc.
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