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The Patient Equation with Glen De Vries

Biography: I had the opportunity to sit down with Glen de Vries, co-founder, and co-CEO of Medidata, a Dassault Systèmes company. Medidata is the most-used platform for clinical trials around the world, powering tens of thousands of clinical trials with millions of patients and billions of patient records. Glen is the author of The Patient […]

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Biography:

I had the opportunity to sit down with Glen de Vries, co-founder, and co-CEO of Medidata, a Dassault Systèmes company. Medidata is the most-used platform for clinical trials around the world, powering tens of thousands of clinical trials with millions of patients and billions of patient records. Glen is the author of The Patient Equation: The Precision Medicine Revolution in the Age of COVID-19 and Beyond (Wiley Publishing, 2020). You can follow him on Twitter at @CaptainClinical.

Q: Can you tell us a story about what brought you to this specific career path?

When I was doing cancer research 25 years ago, the data was literally all over the place. I roamed the hospital to find relevant patients and collect data or went to a physician’s office to access patient charts. I had a refrigerator full of samples and a lab bench full of experiments with the results written on paper. Data was just everywhere, and it was hard to manage. My goal was to improve the treatment of prostate cancer, but I was spending too much time with the “blocking and tackling” of research.  

I’ve always been a computer enthusiast, and so my natural reaction was to code my way out of the logistical mayhem that was clouding this research. That’s when I started thinking about what would eventually become Medidata. My bench mate in the lab was a physician who also thought about using systems to solve problems. When we started to compare notes, we saw that the amount of time spent on blocking and tackling, on finding data, was just slowing the process down. 

This was the mid-1990s and the internet was in its infancy. We had this crazy idea that maybe we could use the internet to avoid going to find data in other buildings or having to go look at charts. That idea led to Medidata. That was really the beginning of my career path changing from cancer research to uncovering how we can conduct research in a more streamlined, coherent way.

Q: Can you share a story about the funniest mistake you made when you were first starting?

When we started out, we were really naive about business. At one point, I was discussing Medidata’s revenue and someone asked if it was gross or net. I had no idea, so I said, “total.” I had never even thought about running a company. 

We were desperate to find somebody who knew something about business, so a friend introduced us to his college roommate – Tarek Sherif, who would become my business partner.  

Tarek and I made all kinds of crazy mistakes along the way. We started out with our servers in a closet. Then we moved them to a conference room with a 12,000 BTU air conditioner to keep them from overheating. During an office tour, someone put their hand on the air conditioner unit, and it fell through the window and shattered ten stories below. We immediately ran to the store and brought back a new air conditioner in a taxi before all our servers overheated.  

Q: Where do you see the future of healthcare?

I think the future is in assessing data on multiple levels to determine treatment. We can now go from analyzing a molecule of DNA to an organ, which then becomes a system in the body. The body interacts with other people, enabling us to measure behavior and cognition, or even contact trace, which is becoming a focus with COVID-19. It’s all relevant for patient care. 

We no longer have to run around to connect pieces of information. When we started out, we had these tiny threads of information we were trying to weave together. Now, we have this giant quilt of data at our disposal. The future is in determining how to use those data to greater benefit. 

For so long, mathematical simplicity has dominated the way we think about treating people. “Do you have high blood pressure?” If the answer is yes, we’re going to do something, but if it’s no, we won’t. But there are more factors to consider. Previously, we couldn’t even look at those factors, but now that we can look at data on different scales; we can get more sophisticated in determining how to treat people. That’s where this is all going – precision medicine. It’s ensuring the right treatment is given in the right amount at the right time.

Q: What is a patient equation?

The idea of the patient equation comes from this world where we’ve got so much information that you create a structured way to make sense of it. It’s the same way that you learn math – the more you learn, the more complex the equations get. As you progress through basic arithmetic and calculus, equations become more sophisticated to the kind of math that’s involved in machine learning and algorithms that people are applying to AI [artificial intelligence] today. 

So, the patient equation is literal, but it’s also a metaphor of how you practice medicine with so much information being used for decision making. It’s how basic arithmetic – a yes or no question – evolves into a sophisticated equation where we can take all of this data, determine what is important, and then solve that equation to do something beneficial for that patient.

Q: How might we detect disease earlier (COVID-19 included) by adding non-traditional information — e.g., cognitive, behavioral, or environmental data — to the traditional clinical measures?

One of the things that I talk about in the The Patient Equation is determining what really matters to the patient. What outcomes enhance, not just their longevity, but their quality of life? People are going to have different views about that depending on where they are in their life and what their goals are. If you think about what matters to the patient, then you can start to connect to the kinds of information, traditional and non-traditional, that are inputs to the equation. 

Solving for something that doesn’t make somebody happier, live longer, or be a more productive member of society doesn’t do any good. The idea that we can solve for something that is impactful is a really important piece. We can think in terms of, “to get this quality of life, are there diseases that I need to detect or treat earlier?” I think the answer is inherently yes, but sometimes it might be no. There are things we are overtreating or spending time and money on, and I don’t just mean from a healthcare cost perspective. Typically, when we treat a patient, it causes some level of pain. Even if you are just taking a pill, you likely had to have your blood drawn several times to prescribe that pill. The more sophisticated we can be in terms of figuring out what really matters, what information we really need in order to figure out all the aspects of what we are going to do from a therapeutic perspective, the better.

In traditional clinical measures, we are probably doing some things we don’t need to, and it’s because of that single-threaded, one variable at a time view of medicine. We’re looking at a lot of trees, but I think the patient equation is about seeing the forest. The forest is about seeing this person who is living a long, enjoyable, productive life. Let’s figure out what we should and shouldn’t do to the individual trees that make up that forest to make it as beautiful a forest as possible.  

Q: Can you explain the significance of the unexpected potential of smartphones, smartwatches and other wearables, and how they’re changing the future of clinical trials and health care?

I think there are several things that smartphones, smartwatches and other wearables are going to do. One of those things is that it makes the transfer of data easier. We won’t have to go looking through different doctors, paper charts, or electronic medical record systems to bring it all together if every patient is walking around with their medical records. Whether those records are in the cloud or on their phone, I do think we’re marching towards that. In the grand scheme of things, it’s great. It means that we are going to have all that information more accessible to make better decisions.

The other aspect is that these devices make it easier to measure things that matter to patients. If we’re talking about quality of life, I can tell my doctor how I was feeling on certain days or how productive I was or how much exercise I got – if I remember. But my phone or smartwatch can tell me how many steps I climbed before I was out of breath. A smart mattress can tell me how well I slept. Then, we can start to look at why I didn’t sleep well on certain days or if there is a trend in my cardiovascular system that could interfere with my work or my enjoyment of my kids. 

These devices will help us collect data that is going to allow us to have this view of objective, quantitative endpoints that are directly related to what patients care about – making people’s lives longer and better in ways they appreciate. We couldn’t achieve a lot of those things 20 years ago with a paper medical chart. With these devices, we are going to be able to associate important, emotional outcomes with something that we can put into a mathematical equation. 

Q: What advice do you have for working smarter, finding purpose and beating burnout? 

At Medidata, we have an advantage in that we have an important mission. I think if you are doing anything in healthcare, you’re probably already motivated to improve people’s lives, and that should be hugely motivating. 

I always tell people I work with that if you think about all the people in your life, at least one person is going to be taking a prescription medicine that we’re working on today, and that’s motivating. So, I think if you can think about purpose or communicate purpose in a visceral way, if you can make people excited about the work that you are doing, that makes it easier. 

That doesn’t mean people won’t burn out, though. There’s a graph that shows that the harder people work, the more productive they are, until they reach a certain point. Then the productivity drops sharply. It’s not much different from the patient equation. For example, you can track your sleep quality on your smart phone and watch your progression from unproductive to productive. You can see the warning signs or realize you’ve gone over the peak. Maybe there’s stuff you’re doing that you shouldn’t be. We saw that with our employees because it was so easy to wake up and get on the phone or a Zoom call. What started as a productive activity soon consumed the entire day. So, we gave extra holidays and instructed our team not to meet on Fridays. It’s entirely possible that you might be overtreating the problem.

Q: What key learning would you like to add from the book and what we need to consider next in healthcare?

I hope that as people start to think about The Patient Equation, they understand that this is not a situation where people understand the concept, implement it, and everything is great. This is a really big problem to solve. We need to think differently about how individuals (patients and their caregivers) and the healthcare system (hospitals, insurance companies, government) have a set of incentives and behaviors that affect our view of disease and therapy. I hope that for all those constituents, this helps begin to shift their way of thinking. 

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