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Like it or not, we live our lifestyles now through our phones - ML allows us to learn from it.Īnd last, technology allows us to scale existing therapies in new orders of magnitude. Identifying the clues in our behavior that eventually add up to significant lifestyle risk is the first step towards changing and improving that behavior. For example, correlating patterns with data like where you shop, when you eat lunch, what activities do you do, what shows you watch, what your exercise routine has been, how much you sleep, even perhaps whether you remember to charge your phone. Second, applying Machine Learning to vast amounts of new data is identifying all kinds of nuances of human behavior that we aren’t nearly as good, as humans, at noticing. Comparing the immediate efficacy of two algorithms for lifestyle behavior modification on two different populations can happen not just over years or months - as a RCT would have to be - but over weeks and even days, improving our responses and lifestyles that much faster. The same kind of A/B testing that Amazon does, for example, to optimize ecommerce - everything from the look of the website to the flow of the experience to the nature of the shipping that you get - can be now applied to behavior modification for health.
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Rolling out new versions of products has been difficult and expensive. Most medical products are released and then rarely get updated (think about how old the stethoscope is!). These RCTs are invaluable laboratories for learning about what is effective therapeutic behavior modification, or improving efficacy - and it’s not toxic. Second, technology’s ability to do constant A/B testing essentially enables RCTs, or Randomized Clinical Trials, every moment that technology is present and being used. For one, it’s always there, thanks to the leap in mobile tech, whether that be phones or fitness trackers. Let’s think about why technology actually does work for modifying behavior. So the question is, how can we target this superpower ability of tech to have 4x the ability to impact that the $3 trillion healthcare budget does? We know technology modifies behavior we live this every day. Just look at the phone you’re probably reading this on, which has foundationally changed the way we communicate - along with huge other swaths of human behavior, in both positive and negative ways - from the ability to call a ride service in practically any city at any time to tracking your health to screen addiction. Tech, on the other hand, modifies behavior very well. We like to eat what we want, to exercise or not exercise if we choose. We know this, because the platitude doesn’t work. The true problem is the difficulty of modifying behavior. As we know very well, that platitude will not solve our healthcare problem. Too often we think of this as the “just eat right and exercise” problem. Because behavioral change is the best and most powerful way to impact that whopping 40% slice. Doctors, entrepreneurs and founders need to be thinking (and treating with) lifestyle as medicine.
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We can do better than simply focusing on that small 10% slice of the pie in fact, we’re looking in the wrong place. But that 40% of behavioral patterns - why can’t we tackle that? This is what real prevention would look like: nothing comes even close to mattering as much towards whether you will die prematurely as your behavior does.
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So, unfortunately, are social circumstances and environmental behavior. Genetic predisposition is hard to change. Vijay Pande, PhD, is a general partner at Andreessen Horowitz, as well as an Adjunct Professor of Bioengineering at Stanford University, where he continues to advise the Pande Lab - focused on tackling challenging problems in chemical biology, biophysics, and biomedicine.Įverything we do in the $3 trillion healthcare market today only affects 10% of outcomes to premature death.