the KRAS gene by using gene expression in patients, the response to a type of drug, etc. .). These approaches are the subject of numerous works under the expression of medicine not personalized, but precision.
However, there are limits. It is important to understand that the manipulation of a large amount of data, if it offers advantages, does not eliminate all the problems during their treatment. For example, the “noise” linked to random phenomena does not disappear with a large number of variables. There is a need to correct unwanted latent structures (preprocessing step such as background correction and normalization). Despite their large number, it is also possible that the data remains contextual. Studies are paradoxically carried out on small samples in very local situations. The challenge is to organize analyzes on a large number of patients, most often at the international level.
In addition, the manipulation of this large number of variables constitutes a major challenge: it is necessary to avoid over-learning, over-fitting (overfitting), and manage the rate of false discoveries (FDR, False Discovery Rate), especially in oncology. due to tumor heterogeneity. For example, a test can be analytically valid without clinical utility. Finally, despite its gigantic aspect, the data analysis, the hypotheses and the associated prediction models, are based solely on the so-called coding genome, in other words on only 1% of the total genome. It is easy to understand that potentially decisive information can be omitted from the results presented. The challenge is to sequence the entire genome of a tumor and compare it with the genome of a patient without cancer. There is great hope, the cost of this research decreasing as knowledge accumulates.
• New paradigms of medical practice from digital health
One consequence of the expansion of research fields is the potential influence on medical practices.
On the one hand, the new data processing capabilities introduce pure “data analysts” into medical practice, alongside doctors. By predictive models, these analysts can claim a definition of therapeutic strategies as relevant as that traditionally carried by doctors.
On the other hand, the involvement and initiatives that the patient can take to help with the collection and processing of data reinforces the co-constructed nature of knowledge (already mentioned previously with the trend towards patient autonomy), in a permanent, iterative, reciprocal and cooperative process between patients and the medical profession throughout the life course.
Finally, so-called precision medicine commits to a new classification of diseases, as we have seen in the case of cancer. This redistribution in the taxonomy of diseases will also have the probable concern of better personalizing the diseases, and consequently the appropriate therapies, as the movement of targeted therapies is already committing to.
Which health system in the future?
The first level of analysis that we have just closed consisted of identifying major trends based on already observable technological developments. Four main trends on the integration of the care offer, patient autonomy, the development of personalized prevention services by insurers, and the evolution of methods and fields of health research have been identified. A second level consists, on this basis, in studying the impact of ICT on the health system as a whole. In this respect, the evolution that seems to be taking shape is that of a substitution of a hospital-centered system, mainly positioned on curative time by a system giving more space to home care, and to preventive measures, upstream of the disease. All this points towards health paths managed at the different stages of life.
• A system organizing life courses
To start the exercise, it is necessary to take one by one the highlights from the previous parts, and deduce the effects on the health system.
The first of these facts concerns the possibilities offered by ICTs to experiment with more coordinated and personalized paths during illness. Remote moni