Rachel Woods: The three of us have all had the experience of getting an ad, whether it's on a webpage or what have you, that's ultra-personalized. I'm thinking about moments when I'm surfing the web, I'm on social media, and I get an ad to purchase an item — I'm always shocked that they seem to come up with the thing that I need in the moment, which speaks to the fact that a lot of these companies know a lot about us, more than I even can make sense of.

When we talk about consumer data, and particularly consumer health data, what actually are we talking about? What kinds of data are being collected? How is it actually being used?

Ty Aderhold: Think about anything that your smartphone or your computer is collecting. So your location data, the websites you visit, the apps that you use, when you use those apps and websites, where you click, or what you do in certain situations within those products or apps or websites. All of that is collected and is able to be used. Solomon, I'm sure there's others out there I'm missing out. Anything that jumps to mind for you in addition to what I listed?

Solomon Banjo: The other thing that jumps to mind to me is actually something you are all too familiar with, Rae, which is now with the spread of telehealth, we are seeing information that normally we would only say to a clinician in person, and therefore it would be covered by HIPAA, that is now in this gray area of, if I fill out a form for Amazon or you name it, giving them information that only I or my clinician would know, in order for them to triage and treat me appropriately, is that consumer data? And now, what is possible when you connect that data to everything Ty was just talking about?

Woods: And our listeners might not be aware of the fact that you're literally making fun of me right now, Solomon, because we were in a meeting at Advisory Board talking about, in this case, Amazon's initial acquisition of One Medical. We were talking about what it all meant, and in the middle of this meeting I was like, "I'm just going to get on and I'm going to try to make an appointment, and I'm just going to start using the app now." And willingly gave away a ton of information, and accidentally paid money and then realized, "Oh no, what have I done?"

Which speaks to the maybe slippery slope of all of this. And I think that our industry and the leaders that are listening to this episode are familiar with consumer data being used outside of healthcare. Targeted marketing for the goal of purchasing more goods. Can you give me an example of that slippery slope being used to get into the health side?

Banjo: So, you talked about targeted advertising, and I can't pass up the opportunity for a good pun. And probably the example that a lot of people think of is Target, and headlines they made a decade ago now about their ability to predict when someone was pregnant, even before they were aware, or in this case before their parent was aware, and wondering why they got all of this messaging about maternal care when they weren't pregnant. Surprise, the story goes, they were actually pregnant.

Now, a few things to unpack, because in the subsequent decade there's been a lot of journalism pushing back against this. Two things that strike me are, one — we don't talk about all the people who got that same mailer who were not pregnant, which tees up the second thing. No harm, no foul.

Target sends me, Solomon Banjo, a thing saying I'm pregnant, which is not something that I am not capable of being. I throw it in the trash. If using that same information, you're making decisions about resources or my clinical care and you get it wrong, different ballgame, different stakes there. And I think that is an important thing to keep in mind when we think about how and when this data will be used, because it probably will be used eventually around clinical decisions.

Woods: So how are healthcare organizations looking to use this data in order to support their business right now?

Aderhold: It goes back to this idea of targeted marketing that we're talking about, but also behavior modification. This is something we've heard used in the consumer data world as, you can take the information you capture, and very specifically market based on that information to influence choices people make.

And so in a healthcare setting, you can imagine organizations, particularly ones, say, they have a high value procedure, that they earn a fair amount of revenue if they get more patients into their business to do this. It's a huge opportunity for them to identify who those consumers of that service will be, and how to effectively market to those patients, to get them to choose their organization for that service.

Banjo: And I want to build on something Ty just said, because I think it is important to think about that behavioral modification piece, because we are so bad at that in healthcare. How different would our healthcare spend and outcomes look if we in healthcare could successfully get people to eat healthy, exercise, take care of their mental health?

All of these things that hinge on a type of behavior modification. And so I think there's also that piece of, if we had a better understanding holistically of the human, could we see some of that behavior change? Because I don't know about you all, but if I look across my desk, there is stuff that Instagram got me to buy that I wasn't planning to buy. And so, what's the healthcare equivalent of that?

Woods: And what you're getting at is the idea that, in healthcare, this is more than just targeted marketing. It can actually be used to inflect clinical outcomes.

Aderhold: The downside is that it lets existing incentives be taken to an extreme. So, something that you will often hear provider organizations talk about is their payer mix. They are always going to be worried about their payer mix.

Woods: And rightfully so, right?

Aderhold: Rightfully so.

Woods: The standpoint for providers, particularly health systems today, is — I keep using the word fragile, and somehow it gets more fragile as time goes on. That's why, from a business standpoint, health leaders have been focused on their payer mix.

Aderhold: Yes. But from a business standpoint, this ability to use consumer data and modify behavior can take that to an extreme. It can bring the ability to control your payer mix, to capture high value patients, I'm using air quotes here, "high value patients," maybe avoid having "low value patients" come in for your services.

It takes that ability to an entirely new level when you have so much more information on what makes those groups of patients act in certain ways, what makes them come to your facility or not, what makes them choose you or not, and the ability to potentially modify that behavior through digital channels.

Banjo: And let me tease out something Ty said as well, which is consumer data. We keep saying, the value of data is it helps us make better decisions, in theory. But in healthcare, we know that we do not have the same level of data on everyone, because of how they interact with the system, how often they interact with the systems.

And so when I think about, if we take the data that is being collected and generated and start to make decisions and iterating on those decisions, to use Ty's words, we could take that to an extreme where we're codifying and even potentially accelerating the bias already within the system. Because if we could almost have the data sources show, "Here are the people I represent," it's not going to be as diverse as what we would see in the census. We would not have the same clarity for all different patient groups. And so if we start using data to make resource decisions, who are we not giving those resources, or what assumptions are we making about them due to imperfect data that hampers their outcomes?

Woods: It sounds like even when these data are used for a clear business goal, like that targeted marketing example, if we stop there, it will result in the outcome that you, Solomon and Ty, are describing, where if we are focused on just getting those most profitable patients for our orthopedic procedure, standard thing that health systems do forever, that naturally is pushing certain populations to the bottom of the list. And that's before you even take things to the more extreme examples that you two are describing.

So, what do we do to make sure that health systems are making the kinds of moves that help them get out of this place of fragility, while also making sure that we're upholding positive outcomes for everyone? It actually feels like a very delicate balance. Is anyone even thinking about that yet?

Aderhold: I guarantee you that health systems are thinking about the orthopedics example you gave. I don't know if health systems have started to think about the long-term impacts, the ten years down the line, what does this look like for patients in our market impacts of those decisions.

The biggest push I would have is to also use this data for other benefits. In addition to those couple examples we are talking about, there is so much potential to take consumer data, outcomes data, and to better understand how to interact and treat a wider variety of populations. Z

And so if we only focus on how do I interact with and treat the high value populations, that affects your bottom line right away, and that is something that, Rae, you've already said, you have to focus on. But at the same time, you can also focus on uses of this data that are more around the outcome side of thing.

Banjo: Part of the challenge too is where we speak about providers of life sciences as a monolith, but if we dive deeper, I'm sure there are people who could talk circles around us in terms of the understanding of these challenges.

The question is, are their perspectives widely known through the organization, and are they in conversations where that perspective is helpful? And I say this because, Ty, I'm thinking about the example of the health systems who, through their use of cookies and other things, were sending very sensitive health information about patients to third parties, the Facebooks of this world.

I'm sure the people who made the decision that ultimately led to that data breach were not thinking about that. They were just like, "This is industry practice," they're doing their job. And so how do we think, whether you are a life science provider, data vendor, health system, you name it, health plan, how do we think cross-functionally about the data assets, so we understand them and their limitations?

And then also have the right structures in place to make sure that we are using them in the right ways as we're applying them to different problems, so we're not asking data to do something that quite frankly it's not up to, and/or we're thinking about the second or third order impact of how we might use this in this one domain. I think that is the challenge.

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