Data-driven clinical decision processes: It's time

Research output: Contribution to journalReview articlepeer-review

4 Scopus citations

Abstract

Changes and transformations enabled by Big Data have direct effects on Translational Medicine. At one end, superior precision is expected from a more data-intensive and individualized medicine, thus accelerating scientific discovery and innovation (in diagnosis, therapy, disease management etc.). At the other end, the scientific method needs to adapt to the increased diversity that data present, and this can be beneficial because potentially revealing greater details of how a disease manifests and progresses. Patient-focused health data provides augmented complexity too, far beyond the simple need of testing hypotheses or validating models. Clinical decision support systems (CDSS) will increasingly deal with such complexity by developing efficient high-performance algorithms and creating a next generation of inferential tools for clinical use. Additionally, new protocols for sharing digital information and effectively integrating patients data will need to be CDSS-embedded features in view of suitable data harmonization aimed at improved diagnosis, therapy assessment and prevention.

Original languageEnglish (US)
Article number44
JournalJournal of translational medicine
Volume17
Issue number1
DOIs
StatePublished - Feb 12 2019

Keywords

  • Big Data
  • Clinical decision support systems
  • Translational Medicine

ASJC Scopus subject areas

  • Biochemistry, Genetics and Molecular Biology(all)

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