How can healthcare unlock the power of data connectivity?
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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?
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,
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