nce 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, predictive models have, by their learning ability (the famous “deep learning”), the possibility of improving the distinction between these two types of factors in the future .
In addition to these debates on traditional methods of analysis, it is worth emphasizing the role that the patient may have to play in the actual collection of data. As its name suggests, “Big data” is developing massive methods of collecting patient data. In this movement, connected tools, such as portals, and in general access to the Internet, have paved the way for data collection carried out by the patients themselves. It is one of the indirect effects of digital health to favor data collection channels coming directly from the patient. This is what we call the “data-driven patient”. The benefits are potentially significant in the area of clinical research. Traditional clinical trials are based on standardized data collection protocols managed by clinical research assistants. Direct transmission by patients can represent a very significant source of savings in the course of the trial. Consequently, many initiatives seek to develop methods based on data collected by patients after obtaining their consent. Through these movements emerges an evolution of research methods towards personalization. It challenges traditional approaches. It is still difficult to understand the real meaning of this questioning: will randomized controlled trials on samples – the “gold standard” of clinical research – see their place reduced? Or will they (more likely) be supplemented by other methods managing greater variability in data?
Generally speaking, the next few years should bring about an evolution in methods and also in the fields of health research, particularly in the field of clinical research.
Today, much of this new cancer data is already publicly available in the Atlas of Genomic Cancer (TGCA). It is therefore already possible to use this data in order to develop predictive models for given situations (for example the prediction of the mutation of 