on predictive medicine. With the conquest of the human genome, knowledge of the risk profile of developing certain diseases (in particular, cancer) becomes possible. On this observation, we can reasonably envisage preventive actions of a new type, knowing nevertheless that: at risk related to alcohol or tobacco consumption); (ii) the relationship between prediction and the onset of the disease is complex. In many cases, the genetic aspects mix with environmental factors. Furthermore, the state of knowledge is still partial. For example, we have known for the past fifteen years that the risk of developing Alzheimer’s disease is very high in people with the apoε gene. However, having this genotype is neither necessary nor sufficient to develop the disease. In other words, people with the apoε gene may never develop Alzheimer’s disease, and others without it, can. The reasons for the onset of the disease are still too unclear.
All these elements combined tend towards the development of individualized insurance cover, based on prevention measures which are itself personalized. Indeed,better knowledge of known risk factors, such as predictive factors revealed by better knowledge of the genetic heritage, should help to specify preventive actions according to the profile of each individual. The challenge that seems to be taking shape in the insurance sector is then that of developing insurance models based on personalized prevention programs in addition to curative care. It is a question of considering support services for the patient throughout his life, upstream of the illness, either directly with the insured, or via the employers, and the theme of health at work.
In this context, a first question aims to understand the role that members of the GAFA (Google-Amazon-Facebook-Apple) could play. These companies have the potential to collect data on the behavior of individuals (and therefore policyholders), and to process databases (in the context of predictive medicine) which can lead them to question their introduction 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 insura