Chapter 1: What is the main topic discussed in this episode?
Welcome to the McKinsey on Healthcare podcast. My name is Prashant Reddy. I'm a senior partner with McKinsey and Company based in our New Jersey office. In today's episode, I'll be talking with Pete McCabe, the CEO of DataVant, about data connectivity, data security, tokenization, and their potential to transform healthcare.
These topics are particularly exciting to discuss due to the recent developments within the healthcare IT and the information management space across technology, regulation, and frankly, the interest of various industry participants in really addressing the barriers to collaboration. Pete is a thought leader in this space, thanks to his leadership of DataVant.
After a merger this year with Siox Health, valued at approximately $7 billion, DataVant is said to be one of the largest healthcare data ecosystems in the world, enabling patients, providers, payers, healthcare data analytics companies, patient-facing applications, government agencies, and life science institutions to securely exchange their patient-level data.
Pete, thank you again for joining us today.
Yeah, thanks, Prashant. It's a privilege to be here.
So we have three or four broad themes we thought we'd just touch on today. The first we'll start with is data connectivity. So one of the motivations behind the recent merger between DataBand and Siox was to solve this challenge of healthcare data fragmentation. Could you talk us through why data connectivity is so important?
If you think about one of the biggest advancements in healthcare over the last hundred years, it's powering every health decision with data. And when you're able to do that, first and foremost, you put an informed patient at the center of his or her care. Secondly, you get to practice personalized health care.
If you just start with the basics that says, hey, when I walk into the hospital, does the doctor who's assessing me have my full data history? Wouldn't that be great?
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Chapter 2: What is the significance of data connectivity in healthcare?
Wouldn't that be personalized? So you really advance this idea of personalized health care. Thirdly, what happens is you can dramatically advance the speed and lower the cost that it takes life science companies and therapeutic companies to develop new drugs and treatments. You know, as a McKinsey study would say, 20%, 25% of U.S. healthcare spend is waste.
That's about a trillion dollars, 50% to 75% of that. waste can be eliminated with a better utilization of data. You're talking $500 to $750 billion of cost out of the U.S. healthcare system alone. So this idea of fragmentation or solving fragmentation is really about enabling every health decision to be powered with data.
That's great. I love the synthesis, especially around this personalization research and cost is kind of three different lenses. What are the leading causes of healthcare data fragmentation?
When you get into fragmentation, it's a daunting problem. If you take a look at the US healthcare system, you've got tens of thousands of different organizations sitting on thousands of different IT platforms with almost an infinite number of different standards and different privacy controls. First, I'd say as in the US, it's not a single payer system.
And so you've got the forces of competition and entrepreneurship at work. I look at myself, I'm relatively healthy, relatively young. I've probably got 20 different providers I visited over my life. My personal data sits in over 100 different systems. Nobody, starting with me, has a full picture of Pete. Different institutions, different technology solutions, highly regulated.
You end up with a complexity and fragmentation that you don't see anywhere else in the world.
Speaking of the rest of the world, how do you see this playing out in other parts of the world too?
Look, I think where you have a single payer system, they've got a massive advantage. They have more standards, whether they be data standards or privacy standards. In a lot of cases, they run on single IT platforms, but not in all cases. So you dramatically reduce the variables and you can go from, you know, China,
which ends up being pretty homogeneous to the EU, which is a little bit more homogeneous. And Canada, which is pretty tightly controlled to the US, which is pretty independent. Everybody faces the same challenge. Even in the single payer systems, they still have the similar type challenge. It's just degrees greater in the US.
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Chapter 3: How does data fragmentation impact healthcare decisions?
So think a health plan has TPO rights to the patient's data that they serve because that will help them drive value-based care. So in that case, you're able to get $10 million record requests at a time from a payer. Similarly, a life insurance company will get authorization through me when they're working on their policy. The government will have TPO.
We move a lot of data for the Social Security Administration. So there are mechanisms, TPO being the biggest one. that allow organizations in a very limited and very controlled and defined way to access or retrieve data for purposes of advancing care.
And does that extend into research as a topic, if you take life sciences or the intermediaries?
Yes, absolutely. And again, in two different ways. One of the things that we spend a lot of time on is the de-identified space where maybe COVID-19 is a great example. Early on in COVID-19, a bunch of different organizations, including the government said, hey, we need to get access to this data if we're gonna be able to put a dent in this pandemic. but it's all fragmented, right?
Can you help us? And so quickly, 20 odd different leading institutions said, hey, we'll contribute our data. If you guys can de-identify it, if you guys can tokenize it, we can make it available to researchers to go solve anything from the effectiveness of a new drug to correlations on demographics and pre-existing conditions with incubation.
The FDA is quite supportive of reducing the amount of time and cost to develop new drugs. We save millions lives and increase the livelihood of tens of millions in doing that. One of the things that they've introduced is this concept called real world evidence. real-world evidence sits on top of real-world data.
So think medical records that show that a drug might be efficacious for a disease it currently doesn't have an indication for. You can take that to the FDA, wrap it with your other research, and rapidly reduce the cycle times and cost to develop new drugs and get treatments in the right hand. And I would say We're in the first inning.
We're not even 10 pitches into the first inning on what's possible in life sciences with real-world evidence and real-world data. Anything from research to synthetic control arms to trial support, the list goes on as far as we can see.
This is a fascinating intersection, I think, across three or four different industries that haven't typically collaborated as much historically. One word you've said at least five times so far is tokenization. In the context of, I think, data security, could you describe tokenization in layman's terms and what it does and its impact?
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Chapter 4: What are the leading causes of healthcare data fragmentation?
But from a patient lens, how should patients think about this in the context of their consent and how they think through this?
Yeah, that's a great question, an important one. Whenever any of us enter into a healthcare institution, we engage in an active consent process. The color and the flavor might differ from institution to institution, but we always are engaged in that active consent process. What that allows me to do as a patient is control and influence how my data is used.
And it also allows the institution to de-identify the data. and utilize it under their governorship, typically an IRB or the like, to help utilize that data for the advancement of care?
I think this will be an ongoing topic of discussion given the sensitivity of privacy. Addressing it, I think, front on will likely be critical to long-term success. Maybe we move on to our third theme at this point, transforming healthcare, after we've discussed data security and data connectivity quite extensively.
As we think about how the healthcare system is being transformed for the future through data, one critical part of that transformation is enabling value-based care. What role do you see data connectivity playing in this transformation?
The foundation of value-based care, almost implicitly, is you have data, right? How do I know if the care I gave person X was efficacious? You can't really answer the question, A, if you don't know person X's condition before and after, right? Just start with that as the foundation.
But B, if you don't know the context of patient X's care, you know, I had diabetes, I had preexisting conditions, I had heart disease and allergic to these drugs. So if you think about value-based care, it can only be as good as the underlying data that supports the moves on value, the efficacy of the value. We are huge supporters of value-based care.
Today, there's good contracting, kind of commercial incentives to drive more and more value-based care. What we need to do is catch up the underlying data to support value-based care. And it's one of the things we see that the payers and providers and self-insurers driving quite hard and we're very anxious to support them in advancing their objectives. Amazon tells me what books I like.
And Netflix tells me what movies I want to watch. And I spent a lot of time in different industries. But the internet of things, we would take real-time feedback and reduce the fuel consumption of a locomotive by 8%. And it would take real-time feedback and prove the megawatt hours we generate from a wind turbine by 20%.
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