IQVIA Holdings Inc. (IQV) Earnings Call Transcript & Summary

Hello, everyone. Welcome to the webinar for today, Master Data Management - From differentiator to mission-critical. My name is Brett Westen. I lead healthcare technology strategy, engineering and innovation within IQVIA. I'm pleased to be with you this morning. A couple of guests with us that we'll introduce in a moment. A few housekeeping items first. There is a Q&A that's available. You can ask questions at any time in the Q&A section. We will either answer those as we're going through the panel discussion or we will get to them at the end. If you want to enlarge the view, you can click the little gray frame that's above the slide window. If at any point your screen freezes, you can refresh. You'll stay within the meeting. And this session will be available on demand afterwards for anybody that wasn't able to join or if you care to view it again. In terms of the agenda for today, the discussion will be -- the format will be a panel discussion. We'll have some few lead insights around the need for MDM. And to set some context in our panel discussion, we'll be going through topics that we know and feel are going to be important to the audience here today. And like I said, we'll capture questions and either get to them as we go or answer them at the end. Excited to be joined by 2 IQVIA clients, fortunate enough -- that we've been fortunate enough to support on their data journeys. Ylan Kazi, Chief Data Officer of Blue Cross of North Dakota; and Jenny Hyun, Director of Enterprise Analytics at Vituity. I'm going to give each of our panelists a second to introduce themselves and their organizations. So Ylan?

Thank you, Brett. Hi, everyone. Ylan Kazi, Chief Data Officer of Blue Cross Blue Shield of North Dakota. And our organization is the #1 provider of health care coverage in the state of North Dakota. We have 860 employees that serve a variety of different constituents, and we have most of the doctors and hospitals within our network. In terms of what we're trying to do as an organization, so we've really kind of started our next journey in terms of data. I started at the organization back in September of 2022. And really, what we're looking to do is modernize the way that we use data, find new opportunities to provide value to the members and the providers that we serve and really to build a more robust data-driven culture. Prior to Blue Cross, I was Chief Data Officer at Children's Mercy, a larger provider organization. And then before that, I led Data Science and Machine Learning teams for UnitedHealth Group.

Great. Thanks. And Jenny?

Good morning. My name is Jenny Hyun. Thank you for the invitation to speak about MDM. Kudos to all of you who are on here because MDM is probably, I'd have to say, not the most exciting thing to think about on a Thursday morning, but so critical to business operations and reporting. A little bit about Vituity. I'm the Senior Director for Enterprise Data Analytics there. Vituity is a provider services organization. We provide clinicians, advanced providers and physicians that staff most -- many, many hospitals across the country. We are in about 21 states and represent about 4,000 physicians and advanced providers. We have multiple specialties. We're about 50 years old and started predominantly in emergency medicine, but with the change in the health care market, have diversified into different fields, different specialties. Now we have providers in our organization that range from anesthesiology, all the way to telehealth, in patient psychiatry, neurology, children -- sorry, critical care -- hospitalist care, critical care, skilled nursing facilities. So we run the gamut. And along with that, we've had a lot of different data challenges along the way. Much like Ylan, Vituity has a growth mindset and leadership that really support trying to move the organization to be more data-driven. And so we have also pursued a more modernized tech stack, including MDM. And we went into the cloud for our EDW in 2020 and have been building and refactoring and improving since that time.

Great. And then for anybody that's not familiar with IQVIA, IQVIA's 2016 formed out of the merger of IMS Health and Quintiles, creating kind of one of the world's largest data analytics and technology companies. Data is core to what we do. And so mastered it. Something like Master Data Management actually is exciting to us on a Thursday morning in a group like this. We have to use it internally. We have to use it with our clients. Today isn't going to be focused on the work that IQVIA does in Master Data Management. You guys are here to hear from our panelists. At the end of it, we do have some contacts in case there are questions in terms of how IQVIA can help in this space. But without further ado, we'll get in. I mentioned, just wanted to give a little bit of context for today's conversation, and then we'll open it up to the panelists as well as the Q&A. And like I said, if you've got questions as you go through -- use that Q&A box, then we'll try and get to them as we can. The need for Master Data Management and conversations with our payer, provider, clients is still out there. The struggle in trying to stitch together provider data, patient data, member data is still out there. The systems are complex. There's, in most cases, not one single source of truth for member data or provider data. Each process has systems that are tailored around it. Most organizations, going back at least 10, 20 years, this idea of Master Data Management, let's get it all together in one place, have it clean, have it be trusted, has been out there. What we see is that when organizations are struggling with Master Data Management, it is felt across the organization. It's one of those pain points that unifies business, and IT perspective on the answer may be different in terms of what the right answer is for it. But the underlying pain points, whether it being longer to get processes done to things that actually directly impact the member satisfaction not being able to -- when they're in their app, they're missing claims or the provider is not sure where something is at in the process, like it is felt throughout the organization. What MDM did to attempt to solve some of these challenges was to create that single source of truth. There were some aspects to Master Data Management that are kind of table stakes, the ideas of, hey, this different -- this data is different in every system. Let's get it standardized. There's inconsistencies across the sources. So what I got during the credentialing process. And then when I got updates from the provider, it's not in sync. How do I validate the data? There's also gaps maybe in the data, so validating that against third parties. Being able to know that we're talking about the same patient or the same provider, right, having that match together. And then being able to have a consistent kind of view across sources. What is the best view of the member, the best view of the provider and making sure systems can consume it. When we look at the change in the health care business model, while those ideas were -- they help operationally. They help from a reporting perspective. There was value in doing it before when we look at whether it's value-based contracts, whether it's consumer. You pick any of the transformations that are happening in health care right now, this need for MDM has become more mission-critical, even if organizations don't necessarily recognize that we have an MDM problem or we need to make an investment in Master Data Management. Other initiatives that they are investing in, the success of those initiatives is dependent on having this master data under control. We'll talk in the panel discussion about what are some of the value in investing in MDM, how do you make the case for MDM. We'll hear that from our panelists. There is a lot of measurable qualitative and quantitative benefits to having a Master Data Management in place. Often, it's not just enough to have it. It's also operationalizing it. So we'll talk about what success looks like with Master Data Management. But in terms of getting there, once again, we'll hear from our panelists, but there is a lot of things outside of just picking an MDM tool before these challenges are addressed, things from data governance, change management, having processes to support it, I mean having the right architecture that fits. Jenny mentioned modernizing their data architecture. Knowing how MDM fits in is important, but that's just one piece of it. Knowing how does that get to other systems and know you've got the processes to support it are equally as important. So we'll go into the panel discussion. We'll start with Jenny.

And maybe from a provider perspective, what do you see as some of the mission-critical drivers for MDM? You mentioned kind of tackling data challenges with a growth mindset, but what do you think is really driving the need for MDM?

I think for us, at Vituity, as I mentioned before, we had so much growth within the last 2 decades in different areas of health care. And while most of our health care is still within the hospital space, we've also ventured out into our own independent clinics for urgent care. And so the diversity of how data was being sent to us, the diversity of how we were delivering care, really pushed us to some form of MDM. And I'll venture to guess actually at any of these organizations, and I was looking through all of the attendees, there's a variety of people here from different types of organizations. Insurers, delivery organizations, counties, states, all sorts of different kinds of health care organizations. But I'll venture to guess that at some point, there's some Master Data Management going on in your organizations. if it's just hard coding, honestly, in your SQL code or whatever code you're using, your analysts are doing some type of MDM because they have no choice but to do it in order to align your data sets. But that only goes so far. I think the push for us in going into MDM was this growth mindset that if we were not able to be agile or nimble enough to address the variety of needs, reporting needs, data needs, the way the data ingestion with -- how we were ingesting all of the different data from our various sources, we were not going to be able to survive and grow and diversify. And so for us, the mission-critical aspect of MDM was growth. We had different kind of payment structures. We were having different kinds of providers credentialed in many different ways, especially during the pandemic with telehealth and the growth of telehealth. So for us, as a provider organization, a midsized provider organization, it was really important for us to have a handle, as you said, right, on many of those critical data elements, like provider credentials, provider names even, provider NPIs, obviously, as well as the diversification for site location and how -- where and how we were delivering clinical services.

Great. And Ylan, from a payer perspective, you mentioned starting kind of the next phase in your data journey and continuing to modernize your data. How about from a payer perspective, what you're seeing as the drivers? Maybe similarities to what Jenny has and what might be unique to our peers in the audience?

Yes. I'd say similarities. So I think many of the constituents that Jenny had mentioned, we work with very similar ones in our space. And I think just the proliferation of the types of data that are needed, the different fees between provider organizations, health systems, payers, members, claims, et cetera. That's -- I think that's what -- both on the payer side and the provider side, they're seeing in different capacities. . I think one of the other business drivers that I've seen is data way back when 10, 15 years ago, there wasn't really the proliferation of it at that time. And so organizations could still get by just being scrappy, putting in place workarounds, and it worked good enough. It still was enabling business cases and new market opportunities. I think now with how -- with the volumes of data that are coming through, and even with data types, it used to just be more structured data. Now we have unstructured, semi-structured text imaging, et cetera. You can't get away with being scrappy anymore. You have to have more of those solid foundations for Master Data Management. And if you don't have them, it's going to stop your organization from being able to go to market faster, and it's going to constrain the types of opportunities that you can pursue as an organization. And so I think from a payer side, in terms of criticality, really, the 2 domains that we're focused on is, from a member standpoint, they're our most important constituent. And then I think more recently, a provider standpoint. And I think providers can create challenges because they may not all use the same systems. There are differences in sizing from different provider systems. And then I think even just a priority, if you're a smaller provider, MDM is not going to be your #1 priority. And so it's meeting them in the middle and showing how it can help provide better patient care and improve health outcomes.

Great. And then maybe going back to Jenny. So both of you mentioned some of the strategic priorities, but if you look at the next year -- year or 2, we've touched on themes of what's going on with the patients in the clinics, we talked about provider. What are some of the strategic priorities that, in your mind, you're thinking are opportunities from a Master Data Management? Whether that be -- might be impeded because they don't -- clients don't have it or it could be enabled because they do have it. But areas that are top of mind critical that could benefit from having Master Data Management.

I don't think there's an area that -- where we could go -- where you could go wrong with implementing some type of Master Data Management strategy or tools. The cleaner your data is, obviously, the better off, as Ylan mentioned, to be able to meet your providers and alleviate some of those data needs that they have to better deliver patient care, health outcomes and just the viability of your organization. For us, we've used MDM really to streamline a lot of the analyses that we've done. And so we don't have as much waste. I think we're all, within health care, facing an environment of more restricted reimbursements over the coming years. And that's going to put pressure on our organizations to be much more lean in terms of resourcing. And when your analysts are spending hours and hours just cleaning data, that's just not a very efficient use of their time. As Ylan mentioned, too, there is so much more data out there, some -- much more contextual data, much more need for predictive, machine learning, AI types of data. If you don't have MDM in place, you spend a lot more time either going around in circles because you don't have -- you're not reaching the conclusions that your data is -- actually, that's actually there, but has holes in the types of data that you're looking for. Or you're getting inconsistent results because your developers may be using different types of data sets. So as we move into more of this big data landscape, you will need MDM to make sure that your developers and analysts are using the full breadth of the data that you need to reach the conclusions on your business growth and your strategies as an organization. I think I might have diverged a little from your original question, Brett, but that's what I see in terms of kind of the future of data and how -- at least for Vituity, where we see our challenges.

Both of you mentioned like provider member. Ylan, I mean a lot to tackle there is from -- as attendees are thinking about parties that they might have in the year ahead, is there one of those areas that really is the most pressing from your perspective? Is there one area more than others that is potentially the most benefited or impacted from Master Data Management?

I think one of the ones that I've seen in the broader market is, I would say, in general, partnership opportunities between payer and provider organizations. I think there's a lot of room for improvement and how they work together. Because at the end of the day, from a payer side, we're focused on our member, and the provider is focused on their patient. And guess what, they're the same people. We're trying to do the same thing. I think where this comes about and where the priority can be is specifically with these value-based care arrangements. I think there's been just a proliferation of these. They started off very, very small in terms of their guidance and started up very broad, right? So focusing on just a huge population, trying to move more from fee-for-service to actually paying around quality. And in this arena, there -- it can be challenging because sometimes the payers can have much higher-quality data than the providers that they're partnering with. And so one of the questions is, how do you have the right level of parity? And how do you make sure everyone is reading from that same data script? You want to ensure that everybody that's part of the partnership is able to see everything consistently because providers want to know how they're performing. They want to know where the areas of opportunities are. And then the same from a payer side. They can see a population health level. Where are those opportunities to structure these programs and to provide better health outcomes? And I would expect to see that continuing into the future, just many more types of VBC programs and also getting a lot more granular into very specific either population types or even condition types.

No, that's great. I know both of you kind of focused heavily and passionate around analytics, and we've started to touch on it a little bit. Maybe a follow-up, Ylan. What role do you see MDM playing in the context of some of the AI? And maybe talk about improving outcomes, patient health, providers being able to collaborate with payers on that. What do you see as the opportunities for MDM there?

Yes. I think Jenny alluded to you have to have MDM in place if you're really going to be successful with machine learning and AI, and I would echo that 110%. I think where organizations can sometimes go wrong is everyone gets excited about AI and machine learning, wants to go wholehearted into it, make a ton of investments. And then a year or 2 down the line, they start realizing that, oh, our data quality is not where it should be or we don't have the right governance structures in place to actually use algorithms off of it and trust the predictions. And so I -- that's where I've seen MDM play just a crucial component in it and I think start to be a little bit more well known within the industry. I think it used to be a very small data specialty that really nobody outside data teams either knew about or cared about, and now that is becoming more important because it can be a key barrier to actually utilizing AI and machine learning.

Yes. I think outside the data teams, you say -- even MDM, I've had people say, mobile device management, right? Yes, it's seen as critical to the teams. I mean -- Jenny, I mean it sounds like a no-brainer, right? The way that we're talking about MDM, it's foundational. The costs are going to be high without it. These -- the initiatives aren't going to be successful with it. What do you see as some of the big challenges in getting MDM and investments in MDM prioritized within the organization?

Yes. I think I alluded to it in my intro. It's hard to get people -- other than us, it's hard to get people very excited about Master Data Management. It is talking to them through the intricacies of Master Data Management. Just the sale of it to our leadership is often very difficult. I think, generally speaking, anyone in IT or data and in leadership understand that we need it. Pushing them to actually invest in it as part of a larger data strategy or -- is difficult. And I think what has worked for Vituity is not couching it as a data initiative and not couching it as a machine learning initiative or a reporting initiative. Really, MDM is a necessary business initiative for growth. And as Ylan alluded to before, there are all of these other opportunities for population health, value-based purchasing, all of these other things that are actually going to require your organization to know everything that you can about your patients in the most -- with the most data clarity, so that you're using your resources efficiently. And without MDM, that growth is really hampered. You spend a lot more time -- your analysts spend a lot more time cleaning data just to merge them together, just to make them kind of walk the same path in terms of your data story. And you don't want to do that. I think you really -- I think selling MDM to your leadership really has to start with how the health care market is changing, and you as a data leader in your organization want to be able to meet those challenges with the most sophisticated tools at your disposal, which includes a lot of different kinds of MDM tools. As I mentioned before, I think most organizations are doing some variety of MDM. And you might be just taking the baby steps towards MDM or it may be that one of your analysts actually is incorporating this into your code already. So you're probably doing some form of it technically, but until it's actually embraced at the leadership level, you have a culture that embraces MDM that relies on it, I think that it's difficult to get all of the value out of it. One of the questions in the chat box in the Q&A talked about how data governance and MDM work together. Absolutely. You cannot just have -- you cannot just sell MDM, as I mentioned, as a data solution. It is a culture of data at your organization. And it is supported by that growth mindset of wanting to be efficient stewards of your organizational resources and meeting the needs of your patients where they are and the providers who actually provide that care. So I think data governance and the culture of growth, focus on data, that all has to be kind of a web across your organization in order for MDM to be successful.

That's great. I mean, Ylan, in particular, you mentioned kind of getting ready for that next step in the data journey. Are you seeing any challenges positioning MDM, to Jenny's point, where organizations might think they're doing it, but they're not doing it well and explaining how is that impeding them on that data journey? Why does this -- it's easy to focus on we want to be doing NLP, and we want to be doing AML, and to say, okay, this very nonsexy idea of having MDM in place needs to be there first.

Yes. That, I would say, can be very organization specific. So I think there's an element of the current environment that an organization is in. So for example, if an organization is early in their data maturity journey, leading with MDM, you're just going to be encountering a lot of people that don't know either anything about it or it's very tough to show them the relevance of it when they might be more focused on, how do I get my dashboards faster? How do I actually get my analytics? So I think there's an ecosystem component of making sure you're in the right environment and you have the right level of data maturity. Let's say that you're in an organization that's maybe further along in the maturity curve, but then it becomes either an extension or a part of broader data governance efforts. And I think at that level, then you can really start to show, if you were to make a certain investment in MDM tools or even certain methodologies, what is the actual value that it would provide. And I think that, that value story is key to any type of MDM efforts or initiatives that are within an organization. If you're not showing how MDM is either going to improve revenue, reduce cost or reduce risk, it's going to be very difficult to convince teams and even at an executive leadership level, convince your top executives because you're not really showing that connection back to the business. And I really liked how Jenny talked about, this isn't really a data or an IT initiative. This is a business initiative. This is how we'll actually do business better, create better products and services, shorten the time it takes to go from idea to implementation. And so it really is very business-focused, and you have to speak that language of business in order to be successful.