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Implementing precision medicine requires a secure, scalable infrastructure for medical data storage and retrieval. This study identifies key data characteristics, challenges, and solutions for effective precision medicine data management.

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Area of Science:

  • Medical Informatics
  • Bioinformatics
  • Data Science

Background:

  • Precision Medicine (PM) relies on robust infrastructure for medical data handling.
  • Challenges in PM data storage and retrieval are critical for implementation.
  • A secure and scalable infrastructure is essential for data integration and storage.

Purpose of the Study:

  • To characterize PM data, including volume, variety, and velocity (3Vs).
  • To identify challenges in storing and retrieving PM data.
  • To recognize solutions for appropriate data infrastructure in PM.

Main Methods:

  • Systematic literature review from 2015-2023.
  • Searches conducted on Web of Science, Scopus, PubMed, Embase, and Google Scholar.
  • 16 articles selected and evaluated based on eligibility criteria.

Main Results:

  • 1,961 studies identified; 16 met inclusion criteria.
  • PM data characteristics analyzed based on the 3Vs of big data.
  • Key areas include data quality, storage infrastructure, cloud computing, and security/privacy.

Conclusions:

  • A suitable PM infrastructure must integrate and store heterogeneous data.
  • Leveraging big data management from other industries can facilitate PM implementation.
  • Aligning data characteristics can prevent duplication and enhance PM initiatives.