Within the various facets of medicine from pharmaceutical companies, to providers, to device makers, all are poised to embrace and capitalize from big data. The insurance sector is at the top of this list to significantly benefit from real-time data analytics.

In an industry that is burdened with one of the poorest customer satisfaction ratings, carriers need to hit the reset button on how they approach the shift towards “consumerism.” Specifically how real-time data can dramatically improve the overall customer experience and impact the bottom line of their business.

The ability to access this data will allow insurers to shift from a “batch and lag” system of processing fragmented information to one that provides a continuous flow of centralized data that never stops. Giving carriers the ability to “motivate both the patients and the medical systems that treat them to minimize the cost through better preventative measures.”

Most companies make a conscious and deliberate decision to embrace digitization and the information revolution. Yet the role of big data in medicine seems almost to compel organizations to become involved. In this interview, Dr. Eric Schadt, the founding director of the Icahn Institute for Genomics and Multiscale Biology at New York’s Mount Sinai Health System, tells McKinsey’s Sastry Chilukuri how data-driven approaches to research can help patients, in what ways technology has the potential to transform medicine and the healthcare system, and how the Icahn Institute is building its talent base. An edited transcript of Schadt’s remarks follows.

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.

It’s not going to be a discrete event—that all of a sudden we go from not using big data in medicine to using big data in medicine. I view it as more of a continuum, more of an evolution. As we begin building these models, aggregating big data, we’re going to be testing and applying the models on individuals, assessing the outcomes, refining the models, and so on. Questions will become easier to answer. The modeling becomes more informed as we start pulling in all of this information. We are at the very beginning stages of this revolution, but I think it’s going to go very fast, because there’s great maturity in the information sciences beyond medicine.

The life sciences are not the first to encounter big data. We have information-power companies like Google and Amazon and Facebook, and a lot of the algorithms that are applied there—to predict what kind of movie you like to watch or what kind of foods you like to buy—use the same machine-learning techniques. Those same types of methods, the infrastructure for managing the data, can all be applied in medicine.

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. Unless something catastrophic is going on within you—lipid levels that are way off the charts or glucose levels or something extreme—they’re not doing much to assess what your state of well-being is, and the information stored in medical records is not extensive enough.

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. That sort of modeling would be impossible unless you could phenotype individuals on a longitudinal and long-term basis.

And while the wearable devices today are in this more recreational-grade state, they’re changing incredibly rapidly into research grade and ultimately clinical grade. There are already glucose monitors that are FDA1 approved that individuals can wear and that interface with digital apps, which then connect directly with healthcare providers based on what they’re seeing with your glucose profiles. You’ll see that kind of sensoring get better and better, providing higher and higher grades and better and better profiles on individuals over time. I estimate that in five to ten years, accurate information about your health will exist more outside the health system than inside the health system. And that will force the engagement of that information by the medical community.

What big data means for patients, payers, and pharma

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. Ultimately, that’ll be the number of doctor visits you require, the number of times you were sick, the number of times you progressed into a given disease state. All should diminish. And there’s a benefit from being presented with the information, so they’re looking at dashboards about themselves—they’re not blind to the information or dependent on a physician to interpret it for them, they’re able to see it every day and understand what it means.

I believe payers are perhaps among the top of the chain as far as who can benefit from this. Because, ultimately, payers want to constrain the cost of each patient. They care about the health of the patient, but they want to do whatever they can to motivate both the patients and the medical systems that treat them to minimize the cost through better preventative measures, better targeted therapies, and increased compliance for medication usage. So now, payers are getting a better benefit from drugs being taken, because they’re able to see that the drug is being taken as prescribed or that it’s not having the effect on the patient so the patient can be switched earlier to a more effective treatment. If you’re able to intervene sooner in the course of a patient’s health, before they slide into a disease state, then you’re going to save money on those unexpected hospitalizations or emergency-room visits or even physician visits. Read the entire story here

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