Human health is not just complicated. It is complex, meaning that it cannot be simplified. There are many parameters that are related one another and you need to consider them all. And it goes beyond personal parameter since ambient parameter play a role as well (in the ambient you should also consider food, drink, habits, climate...).
In previous posts I tackled the evolution in getting data, physiological data, as one of the pillar of future health care. The second pillar is about making sense of those data, exploiting the complexity to create an emerging view of the health status and predict its evolution under a variety of conditions.
This is supported by data analytics and it is the realm of ICT, Information Communications Technology.
There are many studies and initiatives aiming at making sense of the increasing amount of data becoming available, both at personal level and at community level.
One interesting initiative is the 100k project by the P4 Medicine Institute.
P4 stands for Predictive, Preventive, Personalised, Participatory medicine, what medicine should become. A good fit with the EIT Digital focus on leveraging ICT for Health and Well Being. Indeed, according to Lee Hood, president of the Institute for System Biology -see clip-, medicine has focused on curing the sick, which makes sense, but has not paid sufficient attention to the healthy to prevent them becoming sick. It is a bit like the attitude you have for your care. Would you run periodic check to avoid it breaking down or you rather wait for it to break down to fix it?
Yes I know the saying: "if it ain't broke don't fix it" but in health care you can say that we are all sick, provided a sufficient number of tests are made. Hence, we can get fixed!
The 100k project aims at getting some 250 physiological parameters from 100,000 healthy individuals and following them with repeated samples over 25 years with the goal of spotting tiny variations that can foretell the start of pathological conditions. Just by analysing these 250 parameters researchers have been able to spot anomalies and provide guidelines to the person to fix the potential problem, as an example the detection of an excess of mercury resulted in the advise to cut on tuna sushi and shift to salmon sushi resulting in a significant decrease of mercury.
The project is interesting as a trend. It has some similarity with the human genome project. Like that project it looks very ambitious, it is restricted to a small subset of the population but aims at deriving knowledge that will be applied to a large audience. Also, as technology progress and its cost decreases, we can imagine that every single human being in the future will be part of that "project". Each of our grand-child will likely have their genome sequenced, and each of them will have sensors continuously monitoring many physiological and ambient parameters. These will be fed into a data analytic system that will result in the emergence of a health state as part of the P4 health care paradigm.
Data analytics is a crucial component for future health care and to be effective need to take into account a variety of parameters. One of the critics voiced to data based health care is that data may lead to a distorted representation of the reality and of the problem. Seeing an increase in weight of a person over a few day may be the result of "eating salty stuff" leading to water retention, or it can be a red flag for a problem. Unless you have all the figures at your fingertip you may derive a wrong answer. Also, some doctors claim that preventive exams may in the end be detrimental, like it is the case of detecting first signs of cellular degeneration in the prostate that is pushing patients to surgery for its removal (with potential side effects) whilst the majority of them would not have experienced a malignancy.
Also, several players in the field are saying that by moving to EMR, Electronic Medical Records, and applying data analytics on them would provide a wealth of knowledge with no need to have sensors continuously monitoring a person physiological data.
As in any paradigm shift there are supporters and opposers. My feeling is that personalised medicine based on data analytics will be a reality by 2030 and will start by the end of this decade in niches. I'll discuss these probable niches in the next post.