February 23: Today on TownHall Linda Yang, CIO, talks with Ylan Kazi, Chief Data Officer for Blue Cross Blue Shield of North Dakota about embracing a more data-driven healthcare industry and strategies for better hiring and employee outcomes. How can the healthcare industry embrace a truly data-driven culture? What is his perspective on the recent layoffs from several tech companies and how can healthcare organizations capitalize on an assumption that there are possibly more qualified data professionals in the job market? How can healthcare organizations better identify, hire, and retain this talent? As someone that has worked on both the provider and insurance side, what are some common challenges and how can each side learn from each other?
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Today on This Week Health.
I've seen data transformations kind of go off the rails where an organization gets really excited about data science, about machine learning, and they'll hire a data science leader.
They'll hire a few data scientists After about six to nine months, people start to leave because there wasn't necessarily a good strategy in place, or the data is just a mess. So you can't really do data science on poor data.
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Welcome to another Town Hall show on this week Health. I am Linda Yang, and today I am excited to have Elon Kazi as my guest. Elon is the Chief Data Officer at Blue Cross Blue Shield of North Dakota, where he leads the enterprise data and analytics solution team. His role here is really to help the organization find value in their data.
Welcome Elon. So Elon, how can the healthcare industry really embrace a truly data-driven culture? .
Yeah. Thanks for having me, Linda. you know, I, I think there's really three ways so I'll, I'll start with the first one. You know, Whenever you're going about creating , a more data-driven culture for an organization, there are certain elements that have to be in place , if you're really gonna be successful.
So one of them , is creating an element of psychological safety, not only for data teams, but even for the broader organization. , for many people in the organization. Starting to talk about more data specific terms. You know, Things like data literacy, governance, some of these other key terms in the industry.
A lot of people don't know what that means or what it is. And if you can create a good degree of that psychological safety where people can ask any question, no matter , how basic it may seem that can really go a long. So definitely psychological safety. I think the second one is the ability to either learn or to teach people how to use data that might not be perfect.
So, , when you start an organization, or even if an organization , is further along in , their data driven journey not all of the data is going to have , a high confidence level in terms , of data quality and trust. And one of the barriers that I've seen is people want perfect data.
They want it to be a hundred percent trusted. , but that's not the way that the world works. There are going to be data sets , or dashboards or analytics that are more directional in nature and, may not have that perfect data, but could still be very useful , in helping to make some high level decisions.
And so I think being able to utilize that imperfect data , is really key. And that also ties back into the psychological safety, the fact that people may make mistakes in the beginning, and , that's okay as long as they're being supported going forward. And then I would say the last one, and probably one of the toughest ones, is having a more aligned incentive model.
Whether we like it or not, all of us really respond to incentives. And sometimes they're aligned, sometimes , they're misaligned. From a, data team standpoint, many times there can be some level of productivity metrics where, you know, X amount of reports a month, X amount , of dashboards a month.
That's a pretty misaligned incentive because , it really incentivizes just production , and kind of doing more, it doesn't necessarily incentivize high quality. And so I think looking at the incentive model for the data team specifically and then looking at it. For the broader organization is , just as important.
So if you, want people in your organization to learn more about data, to, get to a point where there's that baseline level of data literacy, there has to be , some type of incentive that encourages them to get there. and sometimes the , incentive, you know, incentives don't necessarily always have to be monetary.
But even including data , as part of an overall organizational plan or as part of , a development goal , for members of the organization, I think is key or you just won't have , that staying power with it. .
That's great. Those three main points are certainly critical and I those resonate with me specifically the psychological safety.
Right. I think it's a great starting point because with creating and cultivating any, Positive culture. It's creating an environment that promotes psychological safety, and I really believe that if that's successful, then the other two points you've mentioned can really catapult those points , and
it makes the organization run more seamlessly. kinda staying on that same theme along the lines that people matter, right? Because at the end of the day, the insight drawn from data is really only as valuable as when people is acting on it, making some decision from that insight. So with that what is your perspective on the layoffs?
Several tech companies. And how can healthcare organizations capitalize on an assumption that possibly more qualified data professionals are in the job market? Mm-hmm. .
Yeah, I, would say just to start, , and especially for people listening to this, if there are any introductions, connections , that I can make, , if an individual has been recently laid off, I'm definitely happy to do that.
Please reach out to me , on LinkedIn and , I'll do my best. I think from an overall standpoint, with Salesforce, Amazon, Microsoft, many of these other tech companies mm-hmm. . That does create more talent in the industry. So I think in, in some cases whether you're, you know, you can be a health insurance company a large health system, a provider system there will be more talent on the market.
I don't think it will be as easy to necessarily source that talent because in many cases many of these people are very highly compensated, especially if they're on the coasts. And I think that that can, just create challenges because especially hospitals, they have a lot of margin pressure.
So it it's difficult for them to, pay at the level , of a larger technology company. I think what they offer though, in , any healthcare organization is. , most of the work eventually impacts , the patient or the health insurance member. And I think that that's something where, technology, some of these other industries are very different.
But that's, really what gets me out of bed. That's why I'm very passionate about healthcare because I know the work that I'm doing that my team is doing eventually impacts the patient. So I think that is really important and , if that's something that you know, somebody's interested in they can definitely pursue that.
I think the other piece of it is many of these healthcare organizations, if you look at where they're at in terms of data, they're not , as far along , as a technology company. And so I. Having that type of technology company experience in terms of best practices, systems, processes, that knowledge can be very helpful to a healthcare organization.
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📍 Oh, certainly. , and so then how can healthcare organizations better identify and hire also retain talent?
Yeah. In, in terms of, hiring I think just having a strategy in place. I've seen data transformations kind of go off the rails where an organization gets really excited about data science, about machine learning, and they'll hire a data science leader.
They'll hire a few data scientists But then after about six to nine months, people start to leave because there wasn't necessarily a good strategy in place, or the data is just a mess. So you can't really do data science on poor data. . So definitely having that strategy, having the use cases where data talent can actually help the organization is definitely key.
And then I think having a good understanding of what are those specific roles and what will the responsibilities be? And I'll, reference data science again. Organizations many times think that they need data scientists and they'll bring them. in But then they want that data scientist to just create dashboards or to do reporting, and that's not very aligned with what a data scientist does.
So having a good understanding of what's needed today versus what may be needed in the future. From a retention standpoint, I'll just say it's challenging. in, general. And it doesn't really matter what company or what, industry. I mean, we have challenges in the healthcare industry, there's challenges in the technology industry, but I think that when it comes down to it, people want to do meaningful work and want to understand how does their work fit within the big picture.
and who are they actually helping , and creating value for? Whether it's, it could be the consumer, it could be the patient. It's kind of dependent on the industry. And I would also say, Focusing particularly on your high performers. your high performers are gonna be a lot more productive than anyone else.
They're gonna have just an amazing level of initiative. And what can happen, especially leading a team, is that you may just, let your high performers have a high degree of autonomy. and almost forget about them because you know that they can work on their own. versus having to spend time with some of , your lower performers, helping to coach them, helping to, help them with their skills.
That can be a mistake. I think you still have to have some elements of that, but just not losing sight of the fact that your high performers want to to learn. They're extremely valuable to the organization and it's really your job as a leader to help them in terms of develop.
I, I completely agree with that. , and I'm just curious, Elon, based on the flexibility of hybrid arrangements and , remote workforce, have you experienced a growing challenge in identifying high performers?
I think in some ways. Mm-hmm. . And part of that is just , a function of managing , a hybrid team.
I think in the past it was more around, like the old management styles were really based on manufacturing. And so in today's world it's very difficult to say that, well, this person. , created 10 dashboards this month, they're more productive than this other person who created five.
It's a lot more nuanced than that. And so I think from leading a data team, it's identifying what are the outcomes mm-hmm. that need to be achieved. And so, just from my perspective having outcomes tied to business priorities is key. Because if you have a person that is all the work that they're doing is tied to organizational business priorities.
They're just doing, frankly, better work , than maybe another individual who's not aligned to it. Mm-hmm. . And then the other piece is a focus on value. And this part is challenging because, all of the work we do isn't necessarily tied to dollar amounts, but in many cases it can be tied to things like cost savings efficiency savings.
So I think having at least some back of the napkin measurement on what is the value that's being. Is also key, but it is, it is challenging and I think for, especially for leaders who have never managed a hybrid team where you do have remote employees, it requires kind of upskilling your own individual leadership capabilities because you're not necessarily in the office with them and you can't walk by their desk and just have a conversation with them.
So it, it definitely requires an evolution and just management style. ,
certainly. I certainly agree with that. It, in my experience, Elon, it requires just more reaching out to individuals on the team and engaging in collaboration or conversations remotely right. Instead of that face-to-face interaction.
So tha yeah. Thank you for that insight. , and one last question, Elon. You've worked on the insurance Side of healthcare as well as the provider side of healthcare and healthcare systems. What are some challenges that each area face and what can one area learn from the other?
Yeah, that's a, that's another great question.
From a provider side I would say the. culture I think that many provider systems are,, are still working very similarly to, 10 years ago, 20 years ago. They may have different tools, different technology, but the mindset and the culture is still kind of lagging where society is going.
I think the other piece is, when you think about a provider system and just innovation in general. . There are always a lot of challenges with innovation because I think from a patient care standpoint, innovation can mean putting a patient's health at risk. And many times that's what doctors, nurses, other clinicians are thinking about.
But I think , using technology, using things like data science and machine learning to enable better patient care is a great connection point And so I think just helping to, educate clinical folks on how this can be used. And then also showing that it's not, gonna replace their jobs, it's going to help make them even more effective.
I think that's one of the biggest challenges that I've seen from , a provider side, from a payer side payers generally just have more robust and complete data. and where
challenge is in all the different data sources that they need to integrate. Whether it's from an electronic medical record, it could be things like social determinants of health interoperability data.
The list kind of goes on and on. So it's more of, you have all this data, how do you combine all of it to just make and improve better insights? I think that's , a key challenge. And then I think sometimes perception too, from a health insurance standpoint. we're all about helping , our members and that involves partnering very closely with providers.
I think that there can be times where providers just may be questioning, okay. Are we on the same side? and we are, we just look at things a little bit differently. The providers very focused on the individual patient, whereas the health insurance companies focused on their overall member population.
Right. And, hearing you talk about some of the challenges in the different areas, I mean, the ultimate goal is really to enhance better outcomes for patients. , right? Mm-hmm. , using data, , it's a great avenue to implement the insight drawn from that data to improve outcomes.
So , I love where the industry , is heading really , to really utilize the data because to your point, we're not lacking data. It's integrating that data. It's pulling insight from the data and involving the people. Collaborating with the people such as the doctors, the nurses, to really use that data to enhance and outcomes.
Mm-hmm. . Well, Elon, it's been a great conversation. Is there anything you'd like to add before we end our show?
Yeah. , the only other thing that comes to mind , for people listening is these types of conversations that we're having, just with practitioners in the. I think , are essential.
I find that, when I have these types of conversations, they're more valuable than reading , a thick book on whether it's, data leadership, data science, healthcare. . But being able to, speak with peers, speak with others in the industry is just key to development. And then also just being able to share best practices.
You know, I've,, made many mistakes in my career. I've also had the benefit of having some amazing coaches and mentors where they've shared the mistakes they've made so that I can avoid them. And I think that that's really important.
Right. Oh, certainly. , and that's what this Week Health podcast , is trying to do, right?
To engage new emerging leaders with those established leaders to start collaboration and sharing knowledge, because , that's where it begins, is the knowledge sharing in conversations. Mm-hmm. . All right, Elon. Well, it's great to have you on the show. , I thoroughly enjoyed our conversation. I'll end it here and I hope you have a great weekend.
Well, thank you, Linda. It was great to be on. Thanks for.
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