into the insurance business. Their expertise plays on the collection of a very large number of data, and their processing (“Data Mining”). Based on the interview with the director of institutional relations at Google (but who obviously represents only one case), it seems that the preferred strategy is to study possible partnerships, rather than to diversify the activity. In other words, as in the case of self-driving cars, where development is envisaged with car manufacturers, the objective is to retain expertise in data processing, and to establish collaboration with operators in the sector.
However, there is already an active attitude in the United States towards “Data Mining” carried out by other actors. The Health Carolina Health system, for example, analyzes data from the tracing of the credit card of members of the system, in order to identify risky behaviors (number of “fast food” purchases). , cigarettes, alcohol and medication). This system could in the future be used to personalize insurance contracts (the more risky your behavior, the more you pay) with all the consequences that one can imagine in terms of privacy.
A second question concerns the dangers of knowing in depth the risks of developing a disease run by everyone. We can guess the dangers of a stigmatization of certain individuals carrying significant genetic risks revealed by predictive medicine. The objective is to consider forms of protection around this new knowledge as studied by various current academics.
Finally, we see the dreadful impact on the solidarity of insurance models where risks are shared. The revelation of these risks, and the differences observed between individuals, raise important ethical questions, in the context of maintaining solidarity insurance cover. The challenge for the coming years in this area seems to be to design insurance models based on personalized prevention of the insured, while guaranteeing values of solidarity in covering risks. This last observation clearly shows that the impact of ICT on health insurance coverage constitutes an issue of health democracy.
Change of scale and research tools
The fourth trend concerns the impact of digital technology on health research methods. Big data and new types of processing offer openings to multiple fields of research. As described in the report, these fields relate to precision medicine, clinical research, management research or epidemiology. There are techniques and tools for collecting and processing new data that are transverse to these various fields of application described. However, this eruption introduces an increasingly manifest debate on health research methods.
• Renewal of traditional analysis methods
Traditional statistical research methods have biases that recent methods are likely to mitigate, or at least reduce.
In particular, one of the main problems in the clinical evaluation of an individual is the systematic analogy made between the case he represents and the average person in his category. This type of reconciliation always involves a risk, as individual characteristics can sometimes present a large standard deviation from the average for individuals in the category. Through their processing capacities, current models have a possibility of more finely analyzing the uniqueness of each case. This is what personalized medicine embodies. But this is also found in other areas of application. There is in all the approaches this common will to categorize more and more finely until uniqueness.
Another difficulty in assessing a health risk is to sort out what is a genetic predisposition for a disease (like type 2 diabetes, for example), and environmental and lifestyle factors. There is a certain amount of risk written into the genome, as well as a variety of factors over which the patient can have some control – such as obesity, regular exercise, smoking, alcohol consumption . Current genetic tests also need to be discussed by a clinician capable of translating the result according to a given environment. But if this tangle requires some caution at present, p