Big Data in Medicine – Evolution and Revolution

We spend so much time thinking about consumers that’s it’s refreshing to find applications of Big Data and advanced analytics that are not linked to selling something.  Last week I wrote about Predictive Analytics role in student success in colleges.  This week a friend pointed me to this very interesting article on The Role of Big Data in Medicine.  This article was published by McKinsey&Company and features interviews with Dr. Eric Schadt, the founding director of the Icahn Institute for Genomics and Multiscale Biology at New York’s Mount Sinai Health System.  Here are a few of the highlights.

Evolution or revolution?

The role of big data in medicine is one where we can build better health profiles and better predictive models around individual patients so that we can better diagnose and treat disease.

One of the main limitations with medicine today and in the pharmaceutical industry is our understanding of the biology of disease. Big data comes into play around aggregating more and more information around multiple scales for what constitutes a disease—from the DNA, proteins, and metabolites to cells, tissues, organs, organisms, and ecosystems. Those are the scales of the biology that we need to be modeling by integrating big data. If we do that, the models will evolve, the models will build, and they will be more predictive for given individuals.

How wearables are poised to transform medicine

Wearable devices and engagement through mobile health apps represent the future—not just of the research of diseases, but of medicine. I can be confident in saying that, because today in medicine, a normal individual who is generally healthy spends maybe ten minutes in front of a physician every year. What that physician can possibly score you on to assess the state of your health is very minimal.

What the wearable-device revolution provides is a way to longitudinally monitor your state—with respect to many different dimensions of your health—to provide a much better, much more accurate profile of who you are, what your baseline is, and how deviations from that baseline may predict a disease state or sliding into a disease state. That means we’ll be able to intervene sooner to prevent you from that kind of slide.

What big data means for patients

What I see for the future for patients is engaging them as a partner in this new mode of understanding their health and wellness better and understanding how to make better decisions around those elements.

Most of their data collection will be passive, so individuals won’t have to be active every day—logging things, for example—but they’ll stay engaged because they’ll get a benefit from it. They’ll agree to have their data used in this way because they get some perceived benefit.

A better understanding of Alzheimer’s disease

For a long time, the plaque and tangles were the driving force for how people were seeking to understand Alzheimer’s and to come up with preventative or more effective treatments. What we were able to do was engage modern technology—the genomics technologies—and go to some of the established brain banks and carry out a much deeper profiling in a completely data-driven way.

We didn’t have to constrain ourselves by the plaques-and-tangles hypothesis. We could say, “We’re going to sequence all the DNA in different brain regions. We’re going to sequence the RNA,” which is a more active sort of sensor of what’s going on at the deep molecular level in different parts of the brain. And then, “We’re going to try to reconstruct predictive or network models to understand how the millions of variables we’re measuring are connected to one another in a cause–effect sort of way,” and, “We’re going to see how those models change between the disease state and the normal, nondemented state.”

Think of these networks as a graphical model where the nodes in the network are different genes and clinical features and DNA variance, and the edges indicate relationships between those variables that we observe over the population of brains we profiled. What we were very surprised to find is that the most important network for Alzheimer’s had nothing directly to do with tangles or plaques, but the immune system. We directly implicated microglial cells—which are sort of the macrophage-type cells of the brain that keep the brain healthy—as a key driver of Alzheimer’s disease.

The Future

One of the biggest problems around big data, and the predictive models that could build on that data, really centers on how you engage others to benefit from that information. Beyond the tools that we need to engage noncomputational individuals in this type of information and decision making, training is another element. They’ve grown up in a system that is very counter to this information revolution. So we’ve started placing much more emphasis on the generation of coming physicians and on how we can transform the curriculum of the medical schools.

Read the entire article here.

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