Revolutionizing medicine in the 21<sup>st</sup> century through systems approaches

Leroy Hood, Rudi Balling, Charles Auffray

Biotechnology Journal · 2012 · 239 citations · 17 references

DOIFull text

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Concepts

TL;DR

Personalized medicine, or P4 medicine, represents a revolutionary shift toward predictive, preventive, personalized, and participatory care, driven by systems approaches and digital data that create vast, heterogeneous datasets posing a signal‑to‑noise challenge. The authors aim to demonstrate that systems approaches can transform medicine into proactive, cost‑reducing P4 care, envisioning each patient linked to a virtual data cloud that distills massive heterogeneous data into actionable health hypotheses. They propose a holistic systems approach that leverages digitalized, multi‑scale data to manage the signal‑to‑noise challenge inherent in P4 medicine.

Abstract

Abstract Personalized medicine is a term for a revolution in medicine that envisions the individual patient as the central focus of healthcare in the future. The term “personalized medicine”, however, fails to reflect the enormous dimensionality of this new medicine that will be predictive, preventive, personalized, and participatory‐a vision of medicine we have termed P4 medicine. This reflects a paradigm change in how medicine will be practiced that is revolutionary rather than evolutionary. P4 medicine arises from the confluence of a systems approach to medicine and from the digitalization of medicine that creates the large data sets necessary to deal with the complexities of disease. We predict that systems approaches will empower the transition from conventional reactive medical practice to a more proactive P4 medicine focused on wellness, and will reverse the escalating costs of drug development an will have enormous social and economic benefits. Our vision for P4 medicine in 10 years is that each patient will be associated with a virtual data cloud of billions of data points and that we will have the information technology for healthcare to reduce this enormous data dimensionality to simple hypotheses about health and/or disease for each individual. These data will be multi‐scale across all levels of biological organization and extremely heterogeneous in type – this enormous amount of data represents a striking signal‐to‐noise (S/N) challenge. The key to dealing with this S/N challenge is to take a “holistic systems approach” to disease as we will discuss in this article.

References

17