Chief Data Officer @ Surescripts | You Can Predict It. Should You? Data Ethics At Scale

Speaker A: That's part of being a data leader is understanding the right questions to ask. Yes, you are a data person. You maybe just don't think of yourself that way. You can't just set it and forget it. You can't just like let these things run.

Speaker B: Welcome to Tech Teams Today where we talk with the people building and leading the engineering orgs behind today's most innovative companies. Our guest today is Lynn Nowak, Chief data and analytics officer at surescripts. We explore her journey from practicing medicine all the way into becoming a technical leader. As you listen, it becomes apparent that Lyn is a fantastic leader that you may want to borrow a few things from on to the episode. Lynn, welcome to the podcast. Very excited to have you. The first thing I want to hit for our viewers, uh, is your path to being a tech leader. It's non traditional, but I think it's pretty darn cool. So I would love for everybody to understand how you got to where you are as a, uh, major role in an interesting company.

Speaker A: Yeah, well, first of all, thank you for having me, Josh. Excited to be here. And uh, yeah, I say non traditional is a good description for my path to this role. Uh, started out as a practicing physician all my life, wanted to be a doctor, uh, was able to accomplish that. Uh, finished undergrad, went straight to medical school, practiced medicine for just shy of 15 years. And um, while I was in practice, uh, there's a variety of reasons of why I left practice and I don't think we have a four hour podcast here so we won't get into all those. But um, there was a lot of frustration with just the administrative paperwork, non patient care aspects of practicing medicine that uh, as being a wife and a mom and having a young family just really unfortunately and really disappointingly was not what I envisioned my life as a practicing physician was going to be. And so really started to explore other options for how I could use that clinical background, um, and my training to stay in healthcare. But um, you know, not be on call every night and not be, you know, kind of bogged down by kind of the day to day, you know, internal medicine challenges that were really, you know, making it difficult. So I made a switch at that time, um, to work actually at a pbm. It was, it was a clinical role in a pharmacy benefit manager and it was really in that role. Um, I ended up really owning the physician experience for that um, industry company and getting more and more involved with. Well if you're going to work on the physician experience and try to make our engagement with the doctors better. You have to understand how the electronic medical records work and how interoperability works and what are their biggest challenges. So things like prior authorization and understanding, like what are the costs of medications that have been, you know, at that time were pretty, pretty blind for the doctors. So, um, those are the kinds of things I ended up working on in my role at the, at the PBM and just getting more and more involved with the technology side of health care, with the data and analytics side of health care, to really understand where the big opportunities were. And that sort of put me on a very unexpected path to, uh, doing a lot of. A lot of work with data and technology.

Speaker B: So was it something where you just kind of turned around and like, wait a minute, I'm a. I'm a data nerd now? Like, was there a moment where you realized this is unexpected?

Speaker A: Um, I. I'd say yes. I mean, it was sort of a series of moments. But I think probably the biggest one was, um, in my role as the chief physician Experience Officer. I worked very, very closely with our chief Data officer and, um, just loved the partnership, loved working with his team, the data scientists, the analysts, just kind of geeking out on the numbers and kind of realizing, okay, m. Math. I never really considered myself to be math, to be my thing, but data and math are not the same. I think people often mistake. Obviously, there's a lot of math and analytics, but, um, you can be really into data and still not consider yourself to be, like, in math. And so I just. I really like, appreciated and valued the insights that that data team provided to me so I could do my job better. I needed that information to succeed in my job. And there came a time when our chief data officer had decided to leave the company for personal reasons. And we both reported to the same guy. And our boss said, hey, um, he came to me and said, I'm not planning to replace Tom. I'm not going to backfill Tom. I'd actually like to move the data science team and the data product team under you. And my initial reaction was, why would you do that? Like, I love partnering with those folks. But, like, I'm not a data person. I'm a partner. I'm a business person. I'm a clinical person who loves working with data and working with the data people. But, like, how are these data scientists and PhD researchers and really, really wicked smart people going to look to me as a leader for all of their data work when I'm not a data guy? And that was when he really Kind of reminded me, first of all, yes, you are. Like, you're up in the weeds and doing all this work with them. Um, you're asking the right questions, they're bringing the answers. Like, that's part of being a data leader is understanding the right questions to ask, pointing them in the right strategic direction. So they're not just doing cool science experiments with the analytics, but doing things that matter to the company. And, uh, you know, as a physician, you are absolutely a data person. Like, you've been using data your whole career. You may not think of it that way, but, you know, in healthcare, in medicine, your job is to gather the data. Whether that's answers to the questions you ask a patient, lab results, X ray results, all of the diagnostic testing we do, that's all data. And you're pulling data together to make decisions and take action. Like, yes, you are a data person. You maybe just don't think of yourself that way, but you absolutely are. And so it kind of helped me reframe that in my mind that as much as I kind of self deprecate and say I'm not a data person, it's like, I guess I have to admit that I kind of am a data person. So that's, that was sort of the turning point for me to, uh, okay, I guess I am a data nerd.

Speaker B: And it's, it's a wonderful thing when somebody believes in you. I've had someone that did something similar with me where I was on the strictly tech side and they said, hey, we actually want you lead production. Like, I'm not a product guy. Like, well, similar, similar discussion. And it took me, I'd say, six months before I got over, um, realizing I actually am pretty good at this. What was your path to take you 6, 18 months, or you still figure that out, where it's like, oh, yeah, like, I am actually pretty darn good at this.

Speaker A: Yeah, I mean, I'll say as a, as a poster child for imposter syndrome, I certainly would not, um, acknowledge that I figured it all out. I'm learning every day, like I think we all should be. So I will certainly, uh, not claim to have it figured out. Um, I've definitely gotten more comfortable in this role over time. Um, you know, I get a lot of feedback from my team that they really appreciate that I'm curious. I ask them questions, I get into the details with them. You don't want me writing SQL code, you don't want me, you know, you know, running Python. But I will work with the folks who do. And I want to learn, I want to understand. This will tell me why this model works and this other one doesn't, or why would we use this methodology, not this other one? Um, I will often say the closest thing I am to a data scientist is I gave birth to one because I have a daughter who actually is a legitimate master's degree data scientist. Um, but even talking to her, you know, help me understand this. As a leader of the team, you know, you always are going to have to hire. I mean, I, I'm a big fan of hiring people way smarter than me who can do things I can't do. We complement each other. And so I feel like I bring. I bring the strategic direction. I bring, uh, again, like, making sure we're asking the right questions. Is, uh, the data science team focused on the right thing? Are my analysts using the right data source data for whatever questions they're trying to answer and then let them do their thing? So I don't try to be a SQL expert. I don't try to be an AI expert. I rely on my really smart people on my team that I hired to do that. And we work together to make sure we're solving business problems. So that's a role I'm very comfortable in, leading the business aspect of what this team needs to accomplish. And then I let the math guys do the math. So that's. That's, uh, you know, kind of been my take on it.

Speaker B: And there's something powerful about not having those skills and not even desiring to have those skills, because you inherently trust your team, and your team knows that you trust them. And that's a very empowering place to be as a member of a team, to know, like, hey, like, my boss, like, fully believes in me so much that, like, it's like, yeah, whatever you say. Like, that's a. That's a wonderful, unfortunately rare thing. So I think it's something that I, uh, would love. I would love to speak with more people that are operating like you with, uh, a, uh, willingness to be vulnerable and say, hey, that's not who I am. But I. But I trust you. So kudos to you and your team for operating like that.

Speaker A: Yeah, well, I think we've all reported to people who don't do what we do. Right? So, I mean, even when I was in a clinical role, you know, you've got a hospital administrator who's running a hospital. They can't do surgery, they can't run, you know, pick which antibiotics. So you always, when you're In a leadership position, you should always have, or, uh, often I should say will have people under you who have functions that you couldn't do their job even if you wanted to. So you really have to trust, you have to do your best to hire good people and make sure you're, you know, through that interviewing process, like through the interviewing process, I will often bring in technical partners to help me interview to do those technical pressure testing. Because I know like, I'm not going to be the best person to say, is this data engineer a, uh, high quality data engineer? And because I'm not a data engineer, so I will get other data engineers to interview, you know, a candidate to kind of pressure test on the, on the tech side. And I will pressure test on, you know, the. Is this person going to be a collaborative team member? Is this person going to be the kind of human I want on my team? Are they going to bring the right culture to, to our group and you know, making sure they have the technical skills is super important. I just know I'm not going to be the best judge of that. So I bring people in that I trust who will be a good judge of their technical capabilities and then I, you know, in the judge of other, you know, are they a good leader? If I'm looking for a manager person, uh, is it, are they just going to be a good teammate and a person that I want to like, go to dinner with and hang out with? Like that's the culture that we want on the team. And so again, it's a collaborative effort to make sure we get the right people.

Speaker B: Yeah, I think you're not, I think. But I know your medical background gives you a different lens because having been in the computer science side of space for a long time, normally the best developers get promoted, which creates problems because they kind of know everything and they aren't willing to operate like you or like, I don't know that. Because in the medical space there's so much to know. Right. That you can't know all of the things you have to have the partners. So I think that certainly set you up to succeed very well within this role, which is wonderful. So you mentioned your team a little bit. I want to understand the shape of your team and the type of roles you have. Um, and then we'll get into where they sit and all those things. But let's just start with like, what does your team look like? Number of people and types of roles.

Speaker A: Yeah. So we've. The team is evolving and growing, which has been really exciting and you know one of the things as I look for talent on the team, it's been really important to me to bring in people who do have a diverse background of working in different types of companies. It's always good to have that health care background because we are a health care technology company and there are just some um, um, unique aspects of health data. I'll just leave it at that. It brings its own unique um, challenges. But um, having a diversity of experience and background is really important as we've been growing this team. Um, but I'll kind of. The team is basically broken into three kind of major groups. Um, I have one that we call our enterprise platform team and that's really just the foundational, I'll call it data management. It's more than that but it's things like the data governance and quality and data, you know, just enablement, making sure we have good quality data that's available for the data consumers to use. So that's going to include my data engineering team. We have what uh, we call a data trust and enablement. Think of that as the data governance and quality team. Um, and then um, really the platform engineers that really we've done a massive migration of our data to the cloud over the last few years. We are now in the midst of ah, a re architecture to a more modern medallion architecture getting all the data into that new structure available for use, building out semantic layers, building out um, you know just again making that, that data clean, trustable, normalized, deduplicated and all that. So, and really organized in such a way that our analytics team and data science team and whoever else around the company needs to make use of that data can, can get to it and trust it. So that's, that's really what we call our enterprise data platform team who's sort of the core and the foundation for just making data available and ready for use. And then I'll say I kind of think of it as a cake where it's like sitting on top. The next layer of the cake is our enterprise analytics team which are really the consumers of the data to turn that data into an insight or turn analytics for product use. They're the data scientists, they're the data analysts. I've got a small research team of PhD researchers. So they are really the ones who are using that data to turn the data into insights and knowledge and something actionable that someone can make a decision with or take action on. And they're really organized also to support different aspects of both internal and external customers. So we've got partnership where we're working with finance to help improve our, you know, our financial forecasting models and working with sales for sales enablement and you know, and targeting and customer insights and customer reporting. And then we've got folks that work, you know, closer with our product teams to you know, we've got very technical data based products. So we've got some analysts that are really kind of helping be kind of the intel inside of many of our core network and data solutions products. So that's the enterprise analytics team and then the third arm is our data commercial, our data solutions team. So think of them as the ones that are actually taking a lot of these insights and creating external facing insight solutions for our customers. Whether it's for a life sciences company, uh, a pharmacy benefit manager, a provider, a pharmacy, a health plan. We have a lot of really interesting data that um, we can then make information available to help get patients on medications faster, to help clear prior authorizations faster. So that's not a hung. People aren't getting hung up waiting for their medicine. And also um, help life sciences companies really understand what's going on in the market with their drug. So you know, who's prescribing their drug is, are they gaining market share, losing market share, what happens when the competitor comes in to disrupt the market? That's just really valuable information for a manufacturer to know what's going on with their branded drug in real life, out in the market in pretty real time. So that's what our data solutions team is building those kinds of products to help our network partners just get insights about what's going on with their prescriptions.

Speaker B: Okay, cool. So let's talk physically. Do you have hubs of people or everybody's in one spot or you're spread across the country or the globe or what's the physical layout of you?

Speaker A: Yeah, we're pretty spread across uh, the country. So like many companies kind of post pandemic, we went to a virtual first format. We do have a handful of anchor offices, I'll call them. So we've got a large presence in um, in the D.C. area. Our main headquarters is in Arlington, Virginia. So we'll, we'll use that a lot to kind of gather people in in the Arlington office. Similarly we have a lot of folks in Minneapolis. We've got a, a big office in Minneapolis. Um, so there's you know, quite a few folks in that area. But you know, I've got people in. We just actually had a team meeting. I had my leadership team meeting at at my house here in St. Louis, which was great because I'm right in the middle of the country and, and I had folks coming in from Seattle, I had folks coming in from New Jersey, down from Minneapolis, up from a Georgia. So we really are spread pretty, pretty much across the country.

Speaker B: Nice. Nice. Yeah. It's a interesting evolution where there's some folks that are looking to go back to office, some folks that are just trying to. You know, it's always interesting to see how people are juggling that because it is, uh, interesting and it changes the way you hire and how you build your team and uh, how you just have a meeting. Right. Because you've got people on both, both sides of the country in the time zones and.

Speaker A: Exactly.

Speaker B: So exactly how do they, um, how do they partner with the business to understand the needs? Is that coming through you or do they have partners across the business or what does that look like?

Speaker A: Yeah, great question. And we've, we've actually set up the team very deliberately to create that partnership model to be very business led. So um, you know, we want to make sure that everything my team is working on is delivering value to the business and value to our customers. So we created a position called an analytics business partner, the ABP team. So we have an ABP team, um, modeled much like after, like an HR business partner, which is a pretty common role where the analytics business partner is a part of the analytics team. So they're technically savvy. They, they probably have some, you know, they have some basic analytics experience and know how they're not probably as deep into the analytics as the actual analytics team, but they, they have that core technical and analytics knowledge, but they become embedded as part of their business partner team. So you know, we've got a person who's embedded in with the product team to really sit with them, sit in their meetings to really understand what are the, what are the product needs, what are the customer needs, what are the reporting needs for that particular product. So they can then almost serve a little bit as a translator. We don't want it to, uh. I said we don't want it to be a wall between the business and the analytics team. We want it to be a bridge so they can represent. Because a lot of times the business doesn't know what to ask for. They're often not particularly data literate. They may not even know what the data team can do. So they don't even know what to ask for. So that ABP role can kind of serve as the representative from the analytics team. In the product meetings to say, hey, well, would it be helpful to you guys if we would build a model that would predict which patients are going to do X versus Y? Like, oh, my gosh, that'd be amazing. You can do that. And that's kind of where I was 10 years ago as the business partner, learning what we could even do with the data. And it just felt like wizardry to me. I was like, wait, you guys can predict the future? You can tell me which patient's gonna pick up their medicine or not. You guys can tell me which patient's gonna stop taking their medicine or not with pretty good accuracy. And so just kind of using that role as that bridge to both inform the business partners what the data team is even capable of doing and what kind of insights we could bring to the table. And then also bringing those business questions back to the data team to say, hey, here's what they're really asking for. They want a report that can show this, that, or the other, or they know they need to show the value prop of this product. So this is how we'd like to kind of capture the data, analyze the data and turn it into a report for the client. And they kind of serve as that interpreter, if you will, between the business and the analytics team.

Speaker B: Yeah, I think that's a wonderful move. I've seen too many companies where, to your point, the data team is like magicians over there, and we don't even know what they do, and there's so much power, but they aren't talking. So the bridge is a beautiful vision of what you're trying to create. And get that data flowing back and forth in both directions is really key to make sure that your team knows what the team. What the business needs. But also the business, just to your point, even knows the capabilities of like, holy crap, we can do that. And then that gets the ball rolling. So that's a, uh, that's wonderful to hear.

Speaker A: Yeah. I like to refer to those folks as bilingual. They speak both tech and business. So you need that translator to kind of make that connection. Because too often, you know, people just end up talking past each other. And the data team thinks they understood what the business wanted, then they deliver. You know, they build out this whole thing, and then they go to deliver it. They're so proud of it. And the business business is like, that's not what I asked for. It's like they just. They don't always speak the same language. So having those critical people, it's a tough role to find. Like, Finding those bilingual people who are business savvy and also, you know, analytics savvy can be, can be tough, but it's a really, really valuable role when you can find them.

Speaker B: Agreed, agreed. Okay, so you mentioned healthcare data being interesting, challenging, a few different, uh, words. I've spent some time in the insurance space, so I know a little sliver of what you're talking about. I don't know what the way you do, um, just share with everybody else the challenges that health data brings to someone in your role that's responsible for that data. It sounds like you have all kinds of different health data.

Speaker A: Yeah, I'll say the complexities of health data, uh, you know, there's a lot of different directions we could go, you know, just starting on, on one side with just privacy issues. Right. I mean you are dealing with individual people and their very personal, very private health data. So, you know, we need to be very mindful of that. Security, privacy, all of those things. You know, we just need to be, you know, we always have been sour scripts has been around 25 years. The security and privacy of the data that our network, uh, transmits has always been very paramount. But then when you actually really get into the nuances of health data itself, just working with health data, I mentioned earlier about normalizing, deduplicating, uh, we're actually lucky in the pharmacy space that we have a pretty robust set of standards of how a prescription must be sent electronically. So that helps. But even within those standards, the date may be formatted in any one of multiple ways, states, names, um, even medications. There can be different formulas. It could be a pill, it could be a tablet, it could be multiple different strengths. And so all of those things can get really complicated. And you've got multiple different electronic medical records, multiple different ways of transmitting a prescription, multiple different ways that, you know, a claim can be adjudicated. So trying to put all of that together to get some sort of a standard. When you say, when uh, you say a new prescription, I'll give an example that seems pretty, pretty obvious. Well, it's not like when we're talking about a new prescription, is that a prescription that someone is getting for the first time? Is that a refill, is that someone has had a 12 month prescription and they've completed all their refills and now they need a new prescription for year two. So it's like a new prescription, but it's not necessarily new to that patient. So those data definitions and aligning on what do we mean when we ask for, tell me how many new prescriptions there were for this drug, you know, in the last month. You need to go deeper to define what you mean by a new prescription, because it's just, it's really not that simple or that clear. So that's just one, you know, easy example. And I could give you a million, but, um, it just gets really messy. Even things like, say, a blood pressure. A blood pressure has a systolic and a diastolic. There's two numbers that could come as one field, that could come as two separate fields. So even just trying to figure that out when you go to do the analytics can get really complex. Yeah.

Speaker B: So that's why the base layer of your cake is that team that do the data cleansing. And that sounds. Oof. Yeah, that sounds like a lot of work to do. Okay, so you have all this data and, uh, you want to innovate, but you have some responsibility to make sure you're safe with the data. How do you measure or how do you manage that line of. We want to come up with new things, but we also need to be responsible and safe with the data. Has that ever come up?

Speaker A: Absolutely, it absolutely comes up. We, um, have a very robust data governance process, both technically and policy. And process wise. Um, a big part of what we manage also are the data rights we have from our partners. Surescripts exists as a network to move information between partners on the network. The data access we have is really predicated on the relationships we have with our network partners. So we take that very seriously as well. Um, and even once you have access to the data, there's a lot of things we can do that we have that second question of should we do? And, um, I'll give an example, not from my current role, but, um, from where I was previously. We had a data science team looking at opioids. And what should we know about opioids? What kind of models can we build about opioids? We were able to build a very robust model to predict a patient's likelihood to become addicted to opioids before they had even gotten one prescription. That's a very powerful piece of information as a physician. You know, it's like, okay, on the one hand I would. That's really valuable information to have. On the other hand, how do you actually put that into practice? Like when you're going, you've got somebody in there with a broken leg, you're going to give them pain medication. Like, how do you deploy a risk score, if you will, for this patient is highly likely to be addicted versus not like how are people going to receive that? How does a patient. What are you going to say? Well I'm not going to give you the stronger pain medication because I've got a data number here that tells me you're going to become an addict. You have to think about how these results are going to be used before you even start asking the questions. So there's a whole level of not just permission to use data and contractual and legal rights to use data, but also the ethical and just responsible level of, you know, if we're asking this question and getting this answer, what are we going to do with the answer and you know, what might the, the implications or unintended consequences of that information be? So, so we have a pretty robust process, um, internally certainly around governance and contracts and legality and compliance with regulations. But then we also have what uh, we call our Responsible Use of Data committee that um, when we talk about building new products, when we talk about bring. And we don't do this for every single analytics we do, but if we're talking about bringing a product to market, we will go through a process where we review what is this information, how is this information going to be used? Are there potential unintended consequences to patients? Are there potential unintended consequences to some of our network partners and customers that we need to think through? Um, and then we'll bring those things to our Responsible Use committee.

Speaker B: Well that's like minority report type stuff of uh, we should not give this person this message. That's, that's, that's crazy. So something that in our prep for this recording session, you, you talked about your medical oath of doing no harm and you bringing that to your role now and the responsibility you and your team have with that data. How do you, how do you ensure. And you talked about some of the steps that you and the company have. But engineers, analytics people get excited because they find a solution, they find a thing and they're all excited. How do you manage that? Like, well that's, that's great. But it could be used like this. What kind of discussions have you had with people where they get so excited about a thing, but it's like we actually shouldn't do that and helping them recognize that.

Speaker A: Yeah, you know, I think one of, one of the things that I think is really special about our company is just the mission at the core, which is, you know, it's a very technical company. We're moving a lot of data, we're moving a lot of technical information around. But at, ah, our Core, we exist to help people get on their medications and help, you know, doctors have more efficient practices as they're trying to treat patients and help pharmacies dispense the medications effectively and safely. Like, that's why surescripts exist. So even in the hiring process, we want to make sure that, you know, we will ask questions when we're. Even if it's an engineer, you know, it doesn't matter what the role is. Like, having an appreciation for that mission and having a real, um, ideally a passion for that mission, but, you know, at a minimum, an appreciation for that mission and recognizing that, you know, for every. Every transaction exists because there's a patient trying to get care somewhere. And so if we screw that up or if we, you know, if we're not responsible about, you know, how we. How we take that role seriously, there could be. There could be a real person who could get hurt by a mistake here, so. Or by being cavalier about a rule or being cavalier about, um, you know, kind of the guardrails that we put in place around safety, privacy, security, all those things I mentioned earlier. Like, there are real people in this data that could be impacted by what we're doing. And we, you know, we really keep that at our core all the time. Just as an example, in my team meetings, every, uh, every town hall, we have a monthly town hall. Every town hall, we start with our mission and our vision and is really why we exist and just remind people, yeah, you may be, you know, playing with numbers on a keyboard, but those numbers represent a prescription, and that prescription represents a human person, that it's our responsibility to make sure we're honoring that. And. And just keeping that part of the culture, I think, is really just as important as any sort of oversight and sort of like policing, although we do those things as well, um, just keeping it a core part of our culture that there are patience behind all these numbers we're messing with here.

Speaker B: Yeah. My favorite companies, the ones I respect the most, operate the way you do, where every group or team meeting starts with, as a reminder, this is who we are. This is what we do. This is how we do it. And those are things you can never, never say, say enough. So again, applause for you and your team for doing that, because I found that the teams that do that really do understand the mission and do deliver a better product for their. Their customers. And to your point, your. Your product helps people get healthy. There's, you know, it's hard to be more important than that. Um, okay, so I want to pivot to some of the challenges that a leader, specifically in data, specifically in health and specifically in the age of AI, has your job just got easier and harder at the same time. And I think we can all connect the dots on how it got easier with the models helping you figure out a few things, but also the access to that model and the data that you and your company are dealing with, how has that changed the way you govern data or the way you talk about data across the organization?

Speaker A: Yeah, so I, I, I'd say a few different, a, uh, few different things come to mind. I, I mentioned we have, you know, a data governance. We also have AI governance at, at the company. So that, and we don't consider them to be the same. So the AI governance has its own pathway and channels for sort of oversight and enablement. So it's not just a police body, but it's also an enablement body. Like how do we get people access to these tools when they need them, um, for the right reasons and make it easier and really encouraging the adoption of AI, but again, in a responsible way. And then I think also a core part of my team's m job when we talk about that foundational layer of just as data readiness and data quality, a big part of that is do we have data that we can trust? So all these AI models, like you can start to deploy all these models, but if they're sitting on top of data that's not trustworthy or data that's not as complete as it can be or isn't being managed properly, the AI models are only going to be as good as the underlying foundational data that they're using. So we take that very seriously as well to make sure, um, you know, the AI is using good data. And then, you know, from an enablement perspective, I mean we absolutely are embracing, you know, I'll call it agent assisted engineering, agent assisted coding and analytics. Um, I would say we're not, nor do I really want us to be at this point, at the point of, oh, just like Claude will build that, like, we don't need the humans. Like we are very much a human in the loop culture right now. That may change over time for some things and I think in baby steps or maybe some bigger steps. But, um, you know, we are not at the point, as amazing as these are. I mean, first of all, they're changing every six weeks something new comes out. And so you're constantly having to evolve what is the best thing to use, what is the best model, what is the best platform to use for a thing, but also just not letting this turn into a runaway train either and encouraging our engineers and analysts to use the AI tools that are available. It can absolutely accelerate work. The power of the analytics we can do is not something we didn't even dream of two years ago. But you can't just set it and forget it. You can't just let these things run loose. And you also have to over, I think of it like a really good Internet. Like you still need to oversee what they're doing and they're going to screw up and they're going to make mistakes, they're going to make assumptions that they shouldn't make because they don't know otherwise. And that's where the humans come in to really just like you would a good intern or a uh, junior employee, like make sure you're training and overseeing the AI. So it's not just a runaway train that starts doing things you don't want it to do. Again, keeping in mind we have patience at the end of the work we do, so we have to take that very seriously and keep it safe.

Speaker B: Yeah. The pace of change to your point is head, um, spinning and it's almost a full time job to keep up with it. Uh, so I certainly understand and appreciate the situation that you and your team are in trying to again deliver safe solutions across, across the country while also staying at the, for, at the forefront. So that's a, that's a challenge. So um, let's talk about the final bit. Is your, your leadership approach, your leadership style and how you, how you shape that and how you're shaping the future leaders of your teams and what are the things that you try and model for your teams and trying to make sure they understand that to be a great leader, these are the things that you should be doing.

Speaker A: Yeah, I'll say I kind of referenced this earlier, but I ask a lot of questions and I by nature am very collaborative. I probably to a fault want to kind of be in there in some of the details and understand what's going on. I certainly don't think, uh, at least I try not to be at the point of micromanaging. I mean I like to give my team a lot of autonomy and as I mentioned earlier, I like to make sure they know they're trusted and they're empowered to run the team to do the work that they've been hired to do. But I'm also going to want to know what they're working on and I also want to understand what they're working On I ask a lot of questions. So I try to be a very engaged leader with my team, but at the same time, you know, know that they're trusted, know that they're empowered. But I also want to know what you're working on, and I also want to get enough validation that kind of like I do with my kids, like, I trust you, but I'm also going to want to know what you're doing. Um, and I'm blessed. It's worked very well with my kids, and it seems to work well with my team that they know they can come to me. They know they can. You know, I've got their back. If they need an advocate, if they need funding, if they need resources, like I'm going to be an advocate for that team, they got to justify it to me, they got to give me a good business case. But, you know, I'm gonna go to. I'm gonna go to bat for the, you know, the group, any chance I have to. And. And I think being the other thing is, you know, when we talk about the status of work or the like, red, yellow, green, and we're getting kind of status updates on things, I want problems brought to me sooner rather than later. I want a culture of people questioning and challenging and saying, hey, that doesn't look right, or this thing's off track. Like, we gotta course correct here. You know, I want there to be that open culture of being able to say, hey, I need help, or hey, this thing isn't working, rather than finding out so far down the line that something's off track, and then we've got a much bigger problem. So, you know, creating that, I'll call it a safe space or a safe culture to raise questions, raise alarms, bring the yellow and red things to my attention or to your, you know, to their manager's attention so we can address them sooner rather than later. And having that not be a sign of failure or somebody screwed up or somebody's going to be in trouble, it's an opportunity for us to get it back on track or, you know, get it, get a better result. And just making sure people know that that's not only okay, but that's expected. Like, I want, and I expect you to raise those alarms and, and send up a flag if something's not going well.

Speaker B: So that's the kind of leader I aspire to be, the kind of leader that I like to work with and for, I think it's unfortunately rare. How, uh, did you get like that? What are the things that shaped you to be that kind of a leader,

Speaker A: you know, I think, I mean, you, you always take a little bit from, ah, any mentors, leaders, whether it's a coach, a teacher, previous bosses, parents. And you know, maybe it goes back to, you know, my own parents. It was like, look, just if you screwed up, tell us like, you're not. And that's kind of how I parented my kids. You know, it, you know, they come in late once, it's like, all right, you're not grounded for the rest of your life. Like, you're gonna, like, this isn't okay, I'm gonna give you a mulligan. But you do it again, and we're gonna tighten up the rules. So I think just modeling that behavior of everything is not a crisis. Everything, Every, you know, everything that goes wrong doesn't mean you're grounded for life. Um, you know, you're not gonna get fired because something didn't go right. Like, let's, let's, like, let's talk about it. Let's course correct it. It's a learning opportunity. It's a way to make something better. And I guess that's maybe that's kind of how I was parented and that's how I, you know, have parented my kids. But it works really well with team leadership as well. When people aren't always afraid they're gonna get fired. Every time someone. Something isn't going well and they see that other people have brought forward questions, and we, um, we acknowledge that and we almost celebrate the transparency and don't, you know, come down on people. And it's not that people never get in trouble if somebody really screwed up. Like, yeah, somebody really screwed up. But, um, you know, for the day to day, I mean, in this innovative space, stuff's gonna not work and stuff's gonna go wrong. So we just need to acknowledge that and almost encourage that, you know, that kind of transparency. Uh, yeah, yeah.

Speaker B: The culture of fear never pans out well.

Speaker A: Right.

Speaker B: So. Yes, agree. Okay, so we've, uh, made it through. We've got a handful of questions here at the end that everybody that's on the podcast that I ask, um, I told you before, there are no wrong answers. Um, I might judge you on the first one, but okay, you know, we'll figure that. Uh, so the first one is there's a image format that is used wildly on the web and on phones where people share animated things. And it's spelled gif, but different people pronounce it different ways. How do you pronounce it?

Speaker A: I'm going to go with gif.

Speaker B: Oh, I am going to judge you. I'm a soft g. I'm a jif guy.

Speaker A: Okay.

Speaker B: I'm in the minority. I'd say about 75% peanut butter.

Speaker A: Jif is peanut butter.

Speaker B: I know, I know. I. I've had lots of arguments about this. Yeah, I've heard them all, and they're all like, that's a good argument. But I'm still weird and say jif. So, yeah, I am in the minority on that one. So I. Yeah. Oh, well, that's okay. All right, so we talked a little bit about this, like, seconds ago. But, um, I'd like to know the most impactful piece of leadership content that sticks in your head, Coach. Have been from a book or a quote or a podcast or something. Do you have anything like that?

Speaker A: Yeah, I'll say. The one. The one that always comes to mind when I get a question like this is because it was very impactful for me was the book Lean in by Sheryl Sandberg. Um, I read that right as I was making my transition from clinical medicine into the healthcare industry side, and just really, um, a few things about that book stood out to me. One is, I think she did a beautiful job with the image of, you know, your career is not likely to be a ladder. People talk about climbing the corporate ladder, which. Which feels very linear. Um, you know, she said most. Most careers actually are more like a jungle gym where you may go up and then over. You may go down to go up. And. And I. That really stuck with me. Ah. And that certainly has been the case with my. With my career has been anything but linear and certainly not predictable. Um, and then just also, you know, just her encouragement, you know, not. Not just to women, but to all people, especially kind of junior leaders, to lean in, like, be at the table, ask the questions, have an opinion, be heard. And, um, that's hard for some of us. That's, you know, some of us who aren't, you know, naturally that, um, you know, that, uh, just energetic or extroverted. I happen to be lucky. I'm, you know, I. I kind of by nature pretty assertive, but not always. And so you just kind of have to sometimes have those reminders to. To get in there and. And be willing to take a chance, willing to speak up, willing to take an opportunity that maybe you don't feel totally comfortable doing, but you got to trust yourself that you're pretty smart and you can figure it out. And. And I've given that encouragement to a Lot of. A lot of, you know, you know, younger leaders and aspiring, um, you know, kind of up and coming employees that like. Yeah, just. I trust you. Trust yourself. Like, get in there.

Speaker B: Yeah. Well, and your career speaks to that. And you had that moment where you had to lean in, into this new space that you were questioning yourself and everything. So that this. That is wonderful. Um, okay, so, um, this would be an interesting question for you given your, ah, your path to, to here. So the question is, the first product that you ever were a part of shipping, um, either as a team or on your own, what technology was it?

Speaker A: Oh, the first, like way back when. Oh my gosh. So the first product I was a part of was, uh, back, um, when I was at the PBM and it was built on, I want to say the platform was called Foundation14. I don't even know if that's a brand. I just know that's what we call internally. But, um, yeah, it was a patient adherence. A, uh, medication adherence product that helped patients, ah, you know, that we knew were at risk of not taking their medications, helping. Helping get them back on their meds.

Speaker B: Gotcha. Okay. Foundation 14. That is not one I've heard before. So we'll add it to the list. Okay. Um. All right, Data entry. Everybody does things differently. Um, I work on just my laptop and that's like the, Like, I don't use a separate mouse and keyboard. I just use that. I don't. I'm not proud of it, but that's what I use. So what's your optimal way to work?

Speaker A: Well, I've got my laptop prepped up. I've got a keyboard. I've got my mouse. I've got one big monitor here. I've got my. A little like, travel monitor that I take with me that I've actually turned into sort of my third monitor. So I've got my little kind of control center here, which is. Actually feels pretty mild. My husband is a, uh, he's a options trader. He's a market crater. So he's got like, it looks like he could fly to the moon with. He's got five or six different screens. So mine's. Mine's pretty, uh, paltry compared to his desktop. But, um, yeah, I've got a pretty good. Pretty good setup here.

Speaker B: Do you travel with your keyboard and mouse or like, if you're on the road, you just work on the laptop?

Speaker A: Um, I usually bring my mouse. I don't bring the keyboard. I did at one time. I still have it. I have a fold, like A travel keyboard that folds in half with was pretty slick, but I kind of ended up not using it and I ended up just using the laptop.

Speaker B: Okay. Okay. All right.

Speaker A: And I'm really good at bringing my portable monitor and not using it, so my. He makes fun of me that I always drag a bunch of stuff around that I never use.

Speaker B: But yeah, yeah, I do things like that too. I. I sympathize. Okay. Um, so we've made it the end for folks that have stuck around and heard us all the way here that would like to hear more from you. Lynn, what's the best way for them to get in contact with you? Is it LinkedIn or something else?

Speaker A: Yeah, I'm, I'm on LinkedIn. Um, you can also. We. We have the. The surescripts.com website. So if there's more questions about my, my company or what we do or anything like that, um, you know, you can certainly get to us through the. The surescripts.com website or. I'm pretty easy to find on LinkedIn.

Speaker B: All right, perfect. Well, Lyn, thank you so much for the time. Sharing your journey, sharing your knowledge, sharing everything you have with our listeners and viewers. Appreciate it greatly.

Speaker A: Thank you for the opportunity, Josh. Been great.

Speaker B: That was Lynn Nowak, Chief Data and Analytics Officer at surescripts. I know there's at least one person in your network that can learn from what she shared. Go ahead, send it to them. They'll appreciate it. I know I will. Techteams today is brought to you by Revelo. If you're looking to build out your engineering team with world class vetted talent from Latin America, that's times on the line and ready to ship. Check us out@revelo.com. i'm Josh Anderson. Thanks for being here and I'll see you in the next episode.