The Health Pulse

Alex Maiersperger: On today's episode, we talk to our guest about how the best technology leadership is people leadership. The importance of recognizing every data point is a person and leadership at home and at work-- setting vision, priorities, and boundaries in both settings.

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Dr. Onyi Daniel, board member for Sinai Health System in Chicago, previously vice president of Data and Analytics Strategy and Partnerships at Highmark Health. Onyi, thank you so much for being here.

Onyi Daniel: Thanks for having me. Glad to be here this morning.

Alex Maiersperger: You've worked across hospitals, health insurance, vendors. You've been hands-on keyboard and executive building and leading teams, and a board member. Are there common challenges each part faces? Or are they wildly different?

Onyi Daniel: Yeah, I would say there are some differences, but there are several common challenges. And I think regardless of sector, industry, or a specific sphere, there are challenges associated with people. There are challenges associated with rapid change.

And so one of the common themes that I kind of experienced, having crossed all of those components of the health care industry is, whenever there's a lot of change or significant change, there's a need for kind of strong, sound leadership to help guide that change, to kind of allay some of the fears associated with it, because ultimately, there's still a common goal and objective for that organization that we want to achieve, and we need people on board in order to make that happen. And so regardless of what the change is in the hospital system, it could be, we're switching to a new EMR, or we're actually getting on a common EMR or integrating EMRs.

For the payer, it could be, we're standing up a data and analytics organization. For the vendor, it could be we're developing this awesome new product that's really going to disrupt how we do business or how our clients do business. The common denominator there is, how do you make sure that you have really sound, clear leadership that can really articulate the vision for that change and get the teams on board because it's all going to be cultural at the end of the day?

Alex Maiersperger: I love that. So even in the most technology-heavy, even at a tech vendor, it's still all about the people. Yeah, there's certainly an AI arms race right now. It seems like we're in the age of AI. You talked about rapid change. That seems to be the place where most of that rapid change is happening. If engineers are expensive, if the chips, the data centers, the compute power, how does health care keep up? How do health systems compete?

Onyi Daniel: Yeah. Well, I mean, I think first it starts with just kind of being honest with ourselves in the health care space. You have to know who you are, right? If you're a hospital system, you're probably not going to have the scale and breadth and funding like Google or a Meta or a Microsoft or an OpenAI, right?

And so it's really like, let's-- I don't want to say stay in your lane because I think you can be absolutely innovative and cutting edge in a leading organization in the health care space despite that. But there's really going to need to be a focus on how do we leverage the expertise that we have within this space?

Some of that can come from external folks that you bring into your organization around data engineering, ML engineers, et cetera having those specific competencies. But it's really going to take intentional effort within the organization to say, how do I identify the core competencies and capabilities that the team-- the best team we could possibly build of the future that's really going to carry our organization and keep us competitive or a leading organization? How do we build that organically from within? And then maybe augment with external hires to kind of keep things fresh and leverage some of the broader industry or cross-industry experience.

And so organizations that are really serious about leading-- health care organizations that are serious about leading their organization into the future are really going to have to assess the competencies of your existing workforce, right? What are those core competencies, particularly for data and analytics organizations, but really all parts of the organization?

Because as we see the AI arms race, but that means that it's even more pervasive through the organization. So even your business partners are going to need to have some familiarity with AI; some competency as it relates to how to leverage AI, of course, some of the safety and risk components, but then how to think differently about the work that they do using AI. And that's going to require investment in competency, upskilling, whether it's professional development, training, additional context of that particular part of the business, and how it integrates with AI or how AI may potentially augment what that part of the business does.

That's where you're going to get the most bang for your buck because we just have to be realistic. A lot of the organizations, particularly in health care, don't have endless budgets to kind of in-source a lot of external folks or do-- so you have to work within the realities, but always having that visionary kind of, this is where we really see ourselves. We want to be the best health care organization, data and analytics organization within the health care space, et cetera. And here's how we're going to invest in our people and invest in those competencies needed for the future in order to achieve that.

Alex Maiersperger: I love how you took the tech-heavy question and tech-heavy side of the organization and brought in the business world immediately. And so it's not like a technology issue. It's an everyone issue. That's a really great insight.

Alex Maiersperger: I think AI feels like it's both everywhere and nowhere within health care at the moment. Especially kind of mainstage speakers at conferences, when you go, you feel like their organization's far ahead of us in AI, or their organization's using it everywhere.

So we sort of hear about it all the time, but we also hear it hasn't changed my job day-to-day yet, or I haven't really felt it. So there continues to be a lot of talk, but maybe less specifics. Are there places where AI is making a difference right now in health care? And what are the biggest use cases or opportunities that you see?

Onyi Daniel: Yeah, I mean, I think there are a few places where AI really is making a difference. I think there's still some work to do, which really is more around time and maturity around kind of measuring the measurement because I don't think there's a consistent standard across industry for measuring.

But a few use cases-- I mean, there are a few that really stand out in health care. One is ambient listening. Ambient listening is going to be critical. And for those that are-- I'm sure many health care organizations, hospital systems are either exploring it in the POC stage or at least talking about the potential not only for improving the clinician-physician-patient interaction, but also reducing burnout for the clinician-- the provider having to document SOAP notes and all the documentation required as part of that interaction.

And so that's a huge opportunity to-- on both sides, right? The physician or clinician experience, the patient experience, and then just timing and productivity-- not to fill the physicians roster with 30 more patients. But to say now, we're not crunched and to kind of reduce burnout. That's a huge one. That's a huge opportunity area. And I feel like it's gained a lot of traction in the last year.

Another opportunity area, which isn't necessarily limited to health care, but is really gaining traction and is really for customer service calls, making that documentation available for customer service representatives so they don't have to point, click, search, et cetera on kind of optimizing their processes and optimizing their documentation which could improve call times. That is a huge opportunity area. And if organizations aren't exploring that yet, then they probably should.

And then there's also code generation and kind of code optimization use cases more for our developers. I mean, that's absolutely huge. And I think most organizations should be exploring that area as a potential use case if they're not already doing so.

I think one of the big issues, though, is making sure that organizations have actually defined what the priority is in terms of how you want to gain value out of AI, and then have organizations define value for that organization. And that's where I think there's a lot of discussion and lack of specifics because I don't think it's been very well-defined for organizations yet, and you're still in that process.

So, for example, if you're able to optimize your developer team's productivity by 30% they've got 30% more productivity because of code optimization capabilities using large language model, for example. How are they redirecting that newfound productivity into some other project? Are they redirecting it into another project? Are they now able to focus on other priorities?

Are they able to take the afternoons off? How is that being-- how are we actually getting the value out of that increased productivity? I think once organizations have solidified the definition of value-- and it might vary from organization to organization for now.

But once you have that definition, you can actually start to baseline, OK, where are we? And then you can even assess maturity over time. Eventually, the broader industry will kind of converge and kind of get a little bit more consistency and uniformity around value measurement. But for now, organizations are going to have to do that due diligence kind of as an organization and then kind of see how it pans out over time.

Alex Maiersperger: You said so many great things there. But all I'm taking mental notes on is I'm defining value in AI as "afternoons off." That's going to be-- that's my baseline.

Onyi Daniel: Oh, yeah, I like it. [CHUCKLES]

Alex Maiersperger: I love that you defined it or talked about the need for an organization to define what does value look like? And I think the example of ambient listening and what you do with physician time that they get back is really important. I love how you brought it back to just measuring value and thinking about it. Because if you just take that time that they've saved in note taking, and you cram more patients in, is that helping them in the way that you want it to? And so that's a great insight as well.

All right, you've made it to the speed round. There's some yes's and no's on this or that. And so we go quickly through these questions. A few are, yes or no. And a few are, choose between the two. So in totality, will AI make health care more affordable?

Onyi Daniel: Damn, the speed round. And this one is the hard one. [LAUGHS]

I will have to say, I don't know. The verdict is out. I can't say yes, but I also can't say no.

Alex Maiersperger: All right. That's the first maybe in the yes/no speed round.

Onyi Daniel: Yeah, sorry. [LAUGHS]

Alex Maiersperger: We'll get away from the yes/no's. What's one thing, culturally or from their health care system, you'd steal from another country to make us healthier in the US?

Onyi Daniel: Oh, I would absolutely steal the kind of mandate around health care as a basic kind of-- as a right. Everyone has access regardless of your ability to pay. Now, does it mean that the systems are perfect. There are obviously gaps, long wait times, et cetera. But just the ethos, the principle behind it, that access to health care is fundamental for everyone. I would absolutely steal that and apply it somehow here.

Alex Maiersperger: We went from real heartfelt to what's your favorite dessert.

Onyi Daniel: Oh, my favorite dessert, so many. Anyone who knows me-- and I have colleagues that-- I'm teased to this day-- knows that I had the biggest sweet tooth. I was called the cookie monster. They would take the slice of cake and joke that I should get the whole cake. And everybody will share the slice.

My favorite dessert-- honestly, I'd have to say it's between cakes, primarily, vanillas, pound cakes. I like some frosting, but I love cookies. I will eat cookies-- macadamia white chocolate, oatmeal. Any type of cookie, I'm a sucker.

Alex Maiersperger: I knew we were friends. My co-workers, every time we go out to a lunch or to celebrate an occasion or things, they say that my dietary habits are very Buddy The Elf-like. My food groups are all the sugars and syrups.

Onyi Daniel: Yep, that's me too.

Alex Maiersperger: I've got that gene as well.

Onyi Daniel: [CHUCKLES]

Alex Maiersperger: All right, on the opposite end of the spectrum, what's your go-to workout or exercise?

Onyi Daniel: Oh, I run every single day. Doesn't matter if it's 90 degrees or negative five degrees, which is in Chicago, it's a tough thing. But I run every single day. And I've done it for almost 30 years or so.

It calms me down in the morning. No matter what country I go to, I still run. I figure it out. Sometimes I get lost. But yeah, it's my go-to exercise running.

Alex Maiersperger: I don't share that, gene. I wish I did.

Onyi Daniel: [LAUGHS] I got to run off the cookies.

Alex Maiersperger: Yeah. All right, I'm going to get on that train. What's your favorite app on your phone?

Onyi Daniel: Oh, that was a visceral reaction. [LAUGHS]

Alex Maiersperger: I've thrown out the phone.

Onyi Daniel: I know. I know. I would say the app-- I don't know if it's my favorite as in I love it. But the app that I use the most is LAME. It's my email, my Gmail app, so I can check my emails.

I have a phone that I know is like fantastic. Everybody says so, but I'm not leveraging any of those cool features. I probably should. So maybe that'll be my next goal is to actually optimize the phone that I have, which is interesting coming from someone steeped in technology and all that, but I'll put that on my list of assignments.

Alex Maiersperger: All right, sorry to give you homework in this. This was supposed to be a speed round, not a to-do list. Books or movies?

Onyi Daniel: Books.

Alex Maiersperger: And morning or night?

Onyi Daniel: Morning.

Alex Maiersperger: All right. Awesome. We made it through the speed round. And I think some of the important questions there on the-- I think all are important. We love learning about you. The AI question of will it really make health care more affordable and sort of totality in the future? I love the confidence to say I don't know. And the jury's still out, and we're kind of seeing some examples now what's it going to look like in the future.

And then I love the ethos of how do we make health care feel or be a human right? And that's something you've seen in your travels and life. And so you've been leading in health equity for a while now. And are these new technologies and the reliance on them going to make things better or worse? Are we not paying attention to something that the way that we should? And could we be causing more harm than good?

Onyi Daniel: Yeah, absolutely. These new technologies and capabilities could actually make things better, or they could make things worse. And here's why I say that kind of being in this space is we have a lot of data. Most organizations or are a lot of organizations have been using some form of AI for maybe 10 years. Maybe not as sophisticated, obviously not generative AI, but have been using some sort of AI.

But a lot of organizations have not had the investment in their upstream data governance, upstream data quality, and upstream data collection. And so here's where I think I really lean in heavily on kind of AI governance and responsible AI is that if we don't put that due diligence, which has been historically the case on the upstream data collection, we miss out on data inclusion.

So if we're building kind of health care capabilities, or we're building models or predictive models that are specific to certain groups, but we haven't collected a representative set of data upstream, or we're not governing the data appropriately, and the quality is, subpar, what we're doing is essentially going to reinforce-- not only are we not going to have the intended outcomes that we're looking for, but we could be potentially reinforcing the same health care disparities, biases, et cetera that are perpetuated today; that we're trying to address today.

So it really starts not at the downstream predictive models or AI machine learning models, et cetera. It really starts upstream at the data collection, the data quality, and the data governance. And so where I'm kind of excited is I think the generative AI kind of coming onto the scene has sparked a little bit more of investment and attention to that upstream data quality, data collection, and data governance so that we can have data inclusion so that we are including all the populations, specifically the target populations which cross all demographics, groups, et cetera, in the model building.

The other key piece here that I think a lot of organizations-- hopefully they'll turn their attention to is the workforce. So when you're building models, you really need that workforce that's actually building them; that's actually engaged in the end-to-end AI development life cycle to be representative of those populations.

And so for me, what that looks like is an investment in some of the fields around STEM, data science, making sure that the folks that are coming into those fields are representative of the broader population because context is king when it comes to analytics. It's also king when it comes to AI. And it's king when it comes to addressing health inequities and health disparities.

And so there's a workforce component that we need to focus on. There's a data quality governance and collection data inclusion component. And then you have your how do we make sure that the outcomes are representative or not introducing unintended adverse bias, but making sure that we are having the intended positive outcomes for the target population? And so all those things need to be taken together. It needs to be comprehensively and systemically developed in order to make sure that we're not reinforcing adverse outcomes.

Alex Maiersperger: You've inserted it into quite a few of the questions or quite a few of your answers of sort of what seems like a leadership philosophy or a life philosophy. I would love to learn more about sort of where does that come from? And how do you go about leading teams to connect their work to that end result, truly making a difference in people's lives?

Onyi Daniel: Yeah, I'm a very passionate-- anyone who knows me knows I'm passionate about several things. I'm very principles-driven as well. Very much anchored in principles of truth, integrity. Accountability is huge.

And I just think that people are really the focus. One of the things when you're in technology is a lot of times, a lot of the focus is on the technology or the platform or the application or the specific capability. But at the end of the day, it's really about people.

And I think that focus and kind of having that kind of anchor or cornerstone in everything you do allows the leader to capture and communicate the vision in such a way that the team can kind of take hold and be inspired by it and really be bought in. And so that ownership of the vision can't just be the leader's responsibility. I think the leader's responsibility is to be able to kind of communicate that vision and facilitate individual ownership across the team of that vision.

Once people are bought into a vision, they're going to do their absolute best to achieve or make sure that it's-- make sure that we achieve the vision together. They're going to be bought in. It's an ownership thing. It becomes a part of someone's kind of passion and drive.

And so that is kind of the leadership philosophy that I take with me in everything that I do. It's not about implementing something from the top down. It's really organic. If I believe it in my heart, and it's driven by principles, I'm going to communicate that and hopefully inspire the teams because also it's the right thing to do.

So we want to do the right thing. We're going to do the right thing, and so let's do it together. And I think that's so important for organizations to realize. Even if you're a technology organization, or if you're in a department of your organization-- data and analytics, IT, technology, information security, et cetera that tends to be more tech-heavy-- at the end of the day, there's a person at the end of every data point. And so we have to treat each data point with the integrity and the kind of respect that it deserves. And that's kind of my anchor.

Alex Maiersperger: I love that. The person on the end of every data point is such a meaningful way to express the work and connecting to that vision and effort. And so really appreciate that-- life philosophy, leadership, philosophy.

On the life side, I do think there's quite a few leaders and quite a bit of discussion on the internet forums and in conferences and in the people you meet with in networking, especially for a female leader to balance family life and professional life and board membership and all of the things. Is there any way the leadership philosophy-- falling into that life philosophy, how do you define having it all?

Onyi Daniel: [CHUCKLES] Yeah, it's interesting. I think my leadership philosophy is kind of my life philosophy. Putting 100% into my sphere of influence, which happens to be data and analytics, health, equity, technology, health care, et cetera, but also putting 100% at home.

And I think it comes down to principles, right? So I'm very family-oriented. My husband and I, we have three kids, almost four. We'll have four in September. And I am going to give 100% to my family the same way I'm going to give 100% to work. But I'm also very intentional about the boundaries that I set.

I'm intentional about prioritization and intentional about kind of looking strategically, even with family, what am I going to regret if I don't do X, Y, or Z thing in five years or 10 years as it relates to my family? And I always take that approach with every decision because that's how I create balance.

And I do the same with work. In 10 years, if I say we want to be the best data and analytics organization, what decisions do I need to make right now in this moment in order to achieve that? Am I going to wake up 10 years from now and be like, oh, my gosh, our organization is horrible? What did we do? Or am I going to wake up and say, yes, we were kind of tied to that vision and that strategy?

So that's my approach. I think having it all is relative. It depends on what your passions are. But especially as female leaders, I think there's a tendency for us to have a larger share of the responsibilities, the mental load of school scheduling, sports, and all of that stuff. But if you are passionate about it, you will figure out how to create the boundaries necessary in order to achieve your desired outcome within your family, as well as with your employer.

And if you're at an employer that doesn't share that value, then sometimes hard decisions need to be made because you may not necessarily be able to change your employer. Or you can't change your current employer, but you can change to another one. And so I'm very open and enjoy the freedom of being able to set those boundaries, make those decisions to optimize both professional and family life.

Alex Maiersperger: Incredible advice. And sounds like clearly the organizations and your family organizations are so fortunate to have your vision and leadership and principles behind them. And we're certainly rooting for the growing Daniel family. How exciting.

Onyi Daniel: Thank you. I appreciate that. Thank you.

Alex Maiersperger: There are so many challenges in the health care system, whether it's the personal balancing of all of the things, whether it's reimbursement rates and whatever policy is coming. So it's very easy to spout off kind of here are the biggest things and issues that we see preventing us from having a healthier future. We try to end on a very positive note of what's one thing you're very optimistic about that's going to help us create a healthier future?

Onyi Daniel: Yeah. I love the fact that people are more aware and kind of looking to be more empowered when it comes to their health care. And I think some of that is driven by just more of an awareness and a need for more control over our health outcomes, driven by social determinants of health and kind of more awareness there, which I've loved to see that area kind of expanding and growing over the past five to eight years.

Some of it is also kind of data-directed, consumer-directed data. Well, what does this mean for health care? How do I get more access to my data to help manage my health care? Some of it is app-driven, right? Now I've got my Apple Watch. I've got my Samsung watch. I'm able to kind of monitor more components of my health that I probably didn't have access to 15 years ago.

And so I'm excited about the ability for us to really empower individuals to be more proactive in their own health care, and kind of more preventative in terms of like chronic diseases, et cetera. And so I think that's one of the most exciting things for me. I think we'll start to see that pan out in health care costs. Ultimately, I still don't know, though, if AI is going to help with that or not. Verdict is still out, but I think that's what excites me the most.

Alex Maiersperger: Incredible. Dr. Onyi, we have the cookies and extra frosting with a side of vanilla cake and extra frosting in the mail. Thank you so much for joining us on The Health Pulse Podcast.

Onyi Daniel: Thank you so much. This was a lot of fun. I really appreciate it.

Alex Maiersperger: And to our listeners and viewers, we know you have infinite demands on your time. Thank you for spending a little bit of it with us. If you'd like to join as a guest, leave a comment or send your favorite cookie recipe. Please reach out thehealthpulsepodcast@sas.com We're rooting for you, always

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