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This article clarifies "big data" and "smart data" in healthcare. It explains how transforming big data into smart data can improve research and patient care.

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Area of Science:

  • Health Informatics
  • Data Science
  • Clinical Research

Background:

  • The terms "big data" and "smart data" are frequently used but poorly understood in scientific and lay literature.
  • Evidence of big data's impact on clinical care is limited, necessitating clearer definitions and applications.
  • Modern medical data, encompassing electronic health records and external sources, aligns with big data characteristics.

Purpose of the Study:

  • To define and elucidate the concept of big data in the context of modern medical information.
  • To define smart data and outline the necessary transformations from big data to smart data.
  • To examine the implications of transitioning from big data to smart data for research, retrospective analyses, and patient care.

Main Methods:

  • Literature review and conceptual analysis to define big data and smart data.
  • Exploration of how contemporary medical data fits the definition of big data.
  • Discussion of the process and impact of converting big data into smart data.

Main Results:

  • Big data in healthcare is characterized by volume, velocity, variety, and veracity, often found within and beyond electronic medical records.
  • Smart data is derived from big data through processes that enhance its usability, relevance, and actionability for clinical insights.
  • The transition from big data to smart data is crucial for advancing medical research, retrospective studies, and ultimately, patient care outcomes.

Conclusions:

  • Clear definitions of big data and smart data are essential for their effective application in healthcare.
  • Transforming big data into smart data enables more sophisticated predictive analytics and data-driven decision-making.
  • This transformation holds significant potential to revolutionize clinical research and improve patient outcomes.