Data-driven Clinical Decision Processes
Section edited by Enrico Capobianco
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 presents, and this too can be a beneficial aspect, potentially revealing more on how a disease manifests or 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 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.