article · Big Data and Cognitive Computing
Medicine continuously produces vast quantities of diverse data, ranging from basic research, imaging, and clinical studies to health administration, insurance records, and online applications. Incorporating big data analytics provides healthcare professionals with valuable capabilities, including decision-support systems, refined clinical research methods, enhanced treatment efficacy, and personalised patient care. Applying these technologies also helps institutions save resources and reallocate them to raise overall productivity. This work examines how big data fits into digital health by outlining the distinct traits of healthcare data, current analytical tools, and prominent technical and organisational challenges. To assist medical institutions in navigating these complexities, a general strategy for adopting and leveraging big data analytics is presented. This framework helps both prospective and existing users understand how to target analytics tools effectively and realise their expected operational impact.
Modern healthcare systems generate overwhelming volumes of complex information, from diagnostic images to administrative files. Clarifying how big data tools and strategies function helps health services make sense of this information. It offers a pathway towards better clinical decisions, tailored patient treatments, and more efficient allocation of public health resources.
The work proposes a strategic adoption roadmap aimed at healthcare organisations and medical institutions planning to implement or scale data analytics. Potential applications include clinical decision support and administrative resource management. As a descriptive overview and strategic model rather than a validated software product, the approach is at an early conceptual stage of adoption.
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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. 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. Finally, there are significant advantages in saving resources and reallocating them to increase productivity and rationalization. In this paper, we will explore how big data can be applied to the field of digital health. We will explain the features of health data, its particularities, and the tools available to use it. 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. Finally, we propose a general strategy for medical organizations looking to adopt or leverage big data analytics. 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.
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DOI: 10.3390/bdcc7020064
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